GPT-6.1 Sol: OpenAI's near-Astra model at a fifth of the price
Updated: 3 days ago
Status: Active | Last tested: 2026-10-01 (GPT-6.1 Sol (gpt-6.1-sol, released 2026-09-29)) | Re-check: trigger-based (max 6 months)
Active: the tool is current and recommended.
Reviewed as documented at openai.com and in the vendor's developer documentation in October 2026. GPT-6.1 Sol is the mid tier of the GPT-6 line: near-GPT-6 Astra intelligence at a fifth of Astra's token prices, released seven days after the model it replaced. This review sets it against GPT-6 Sol and GPT-6 Astra on U365's own published readings, and states what moved and what did not.
GPT-6.1 Sol scores 6.3 out of 10 on the U365 CI-First Review, which is CI-First Strong, with a Humics-Neutral protection badge and a Medium AI Imposture Risk carrying Skill Illusion High. The band moves against the model it replaces, and the discipline the score demands does not move at all.
For detailed explanations of the CI-First evaluation terms used in this review, including the Humics Protection Badge and the AI Imposture Risk levels, see the Glossary at the end of this post.
In this Tool Review
GPT-6.1 Sol: OpenAI's near-Astra model at a fifth of the price
The GPT-6.1 Sol name, the withheld Astra sibling and the seven-day gap, stated before the review begins
Three names sit close together on this release, and two of them point at models that did not ship. Getting them apart matters before a reader reads a single benchmark row.
Name | What it is | Where it appears |
GPT-6.1 Sol | The model this review scores. An upgrade to GPT-6 Sol released 2026-09-29 at OpenAI's DevDay event, priced identically to its predecessor and described by the vendor as near-GPT-6 Astra intelligence at one-fifth of Astra's token prices | The launch announcement, the model reference under `gpt-6.1-sol`, and the system card addendum published the same day |
GPT-6 Astra | The frontier model released 2026-09-03, priced at $10 and $50 per million tokens. It is the model every "near-Astra" claim in this launch is measured against, and the one this review compares GPT-6.1 Sol with | The vendor's own comparison tables; U365's published review of it |
GPT-6.1 Astra | A model that never shipped. OpenAI cancelled its release days before this launch after internal testing found it would push ahead on a task without asking permission and would reach for external tools in ways the vendor judged unsafe | Reported by the Wall Street Journal from an interview with the vendor's head of safety systems, and confirmed in the launch coverage |
The comparison in this review is against GPT-6 Astra, the released model. GPT-6.1 Astra is named here only because the two names are one character apart and because its withdrawal is part of the story of this release. It has no benchmarks, no price and no availability, and nothing in this review scores it.
The seven-day gap is a fact, not a criticism. GPT-6 Sol shipped on 2026-09-22 and GPT-6.1 Sol replaced it seven days later, on 2026-09-29. No model in this series has had a shorter life. The vendor's own framing of the successor is "an upgrade to GPT-6 Sol" that "nearly matches GPT-6 Astra's intelligence", and both statements are consistent with a single reading: this is the release the family needed, arriving as soon as the training pipeline could produce it. The Comparison and Alternatives section sets the three models side by side on U365's own published readings, and The Outcome states what moved between the two Sols.
What this review is not. It is not a re-score of GPT-6 Sol, which keeps its own published review and its own CMS record. It is not a review of GPT-6 Astra, which keeps its own as well. It is not a review of Codex or ChatGPT Work, the two delivery surfaces this model runs in; those surfaces appear here only where the framework's pedagogical clauses reach them. And it is not an assessment of GPT-6.1 Astra, for the reason stated above.
GPT-6.1 Sol: OpenAI's near-Astra model at a fifth of the price
Tool Snapshot
GPT-6.1 Sol, the second mid-tier model of OpenAI's GPT-6 line.
Tagline: "Near-Astra intelligence for a fifth of the price." (OpenAI launch page, `gpt-6.1-sol`, read 2026-10-01.)
Category: Large Language Model (LLM) with agentic coding, computer use and tool-calling as its primary surfaces. It sits above GPT-6 Luna and below GPT-6 Astra in OpenAI's line-up, and it replaced GPT-6 Sol as the mid tier seven days after that model launched.
Primary use cases:
Recurring complex engineering with a measurable finish line. Multi-file changes, migrations, debugging and test-repair loops in real repositories, where the published measurement is pass rate against cost per completed task rather than price per token.
Agent runs that call tools and work through steps. The model supports the full tool set on the Responses API, and the vendor publishes multi-step workflow and computer-use results for it rather than only single-turn scores.
Professional document work at volume. Reading dense documents with tables, charts and fine print, where the vendor publishes a PDF benchmark result and an independent evaluation reports the same direction.
A cheaper route to capability a team already measured on a flagship. Where a task's answer was previously paid for at Astra rates, the near-Astra framing is the vendor's claim that the same work can be bought for roughly a fifth of the token price.
A first pass over more material than a person would otherwise read. The same reading-partner case the cheaper tiers serve, now with the factual-accuracy improvement this release reports.
What it is not. It is not the frontier model of its own family: GPT-6 Astra holds the top scores on the vendor's most difficult evaluations and OpenAI itself says Astra "should be used for the most difficult scientific research tasks". It is not a chat model in the consumer sense: it is not available in the ChatGPT Chat interface at launch, and it lives in ChatGPT Work and Codex instead. It is not open weights, not self-hostable, and not auditable from outside. And it is not a replacement for GPT-6 Luna at the bottom of the line-up: Luna costs one twentieth of the input price and is sold for narrow, checkable work at volume, which is a different job.
Pricing, as published on 2026-10-01:
Line | Price per million tokens | Change from GPT-6 Sol |
Input | $2.00 | Unchanged |
Cached input | $0.10 | Halved ($0.20 before) |
Cache writes | $2.50 | Unchanged (1.25 times the input rate) |
Output | $10.00 | Unchanged |
The surrounding rate card, read from the same pages on the same day. Prompts with more than 272,000 input tokens are billed at 2 times the input and cache rates and 1.5 times the output rate for the whole request, rather than only the tokens above the threshold. Fast mode costs twice the standard rate. Batch and Flex processing cost 50 percent less than Standard. Regional processing adds a 10 percent premium where it is available, and Fast mode is unavailable with European Union data residency, which the model otherwise supports alongside United States residency. In ChatGPT Work and Codex the model is metered against plan allowances rather than billed per token, and those allowances are separate from API billing.
Official links:
Launch page: https://openai.com/index/introducing-gpt-6-1-sol/
Model reference, GPT-6.1 Sol: https://developers.openai.com/api/docs/models/gpt-6.1-sol
System card addendum: https://deploymentsafety.openai.com/gpt-6-1-sol
API pricing: https://developers.openai.com/api/docs/pricing
GPT-6 Sol launch page, for the predecessor's rate card: https://openai.com/index/introducing-gpt-6-sol-and-luna/
GPT-6 Astra launch page, for the family positioning: https://openai.com/index/gpt-6-astra/
Codex local memories: https://learn.chatgpt.com/docs/customization/memories
Codex subagents: https://learn.chatgpt.com/docs/agent-configuration/subagents
Amazon Bedrock model card: https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-6-1-sol.html
Status page: https://status.openai.com
Every vendor documentation link above resolves. Two kinds of vendor page are built for a browser session rather than a direct request: the `openai.com/index/...` marketing pages and the pricing console. Their content is reported here from the extracted article text, from search indexing, and from third-party reporting that quoted them directly. No figure in this review is reproduced from a source whose content could not be read.
LLM-specific fields:
API model identifier: `gpt-6.1-sol`. The model card's own line is "Use `gpt-6.1-sol` to select this model". No dated snapshot name is published; the identifier is the snapshot.
Context window: 1,050,000 tokens, with a maximum output of 128,000 tokens. Text and image input, text output. Audio and video are not supported.
Knowledge cutoff: 30 April 2026. (GPT-6 Sol is 20 April 2026; GPT-6 Astra is 30 April 2026.)
Reasoning effort levels: `low`, `medium` (the default), `high`, `xhigh` and `max`. The parameter is `reasoning.effort`. Unlike GPT-6 Sol, the `none` and `minimal` settings are not supported: a pipeline that ran its tool-calling turns at `none` on Sol has no equivalent here, which has migration consequences covered in Getting Started with GPT-6.1 Sol.
Endpoint restriction that matters: built-in tools and function calling use the Responses API. Chat Completions is supported without tool calling. This is the same restriction GPT-6 Sol carried, and it is the single most common cause of a broken migration for teams that call the model from existing code.
Modalities and tools: text and image input; web search, file search, image generation, code interpreter, hosted shell, apply patch and computer use are listed on the model reference as tools supported through the Responses API. Fine-tuning is not supported.
Parameters and architecture: not publicly disclosed. OpenAI publishes no parameter count and no architecture description for any GPT-6 model.
Availability: ChatGPT Work and Codex for Plus, Pro, Business, Enterprise and Edu users, and not in the ChatGPT Chat interface at launch. The API, and Amazon Bedrock from 2026-09-29, where the model runs under the identifier `openai.gpt-6.1-sol` through the `us.openai.gpt-6.1-sol` cross-Region profile with no global inference profile offered at launch.
License: proprietary hosted service. Not open weights and not self-hostable.
Delivery surface to check separately from the model: Codex CLI and the ChatGPT desktop app. That surface, not the API endpoint, is what the framework's clause findings in Strengths, Limits, and AI Imposture Risk rest on, because it writes local memory files and runs parallel subagents by default.
Successor planning, stated by the vendor: an Ultrafast variant with up to eight times faster token generation, arriving in the coming days in Codex.
At a Glance Dashboard
Field | Value |
Category | Applied AI / Large Language Model (agentic coding, computer use and knowledge work) |
CI-First Benefit Score | 6.3 / 10 (CI-First Strong) |
Sub-scores | Time 7 / Quantity 7 / Quality 8 / Skill 3 |
CI-First Profile | Primary: Co-Worker and Assistant (level 2). Secondary: Analyst and Tester (level 4), Coach and Tutor (level 3) |
Collaboration Mode | Centaur. Cyborg is not available where more than one agent shares a view or channel (clause 7.5), and is permitted only for one supervised agent session with a stopping criterion set in advance |
Humics Protection | Humics-Neutral (-1 / +3) |
AI Imposture Risk | Medium overall, with Skill Illusion High |
Status | Active |
Last tested | 2026-10-01 |
Released | 2026-09-29 |
Access | ChatGPT Work and Codex on paid plans, the OpenAI API, Amazon Bedrock |
Price | $2 per million input tokens, $10 per million output, $0.10 cached input |
Context window | 1,050,000 tokens in, 128,000 tokens out |
Framework comparison with siblings | GPT-6 Sol 6.0 (CI-First Positive); GPT-6 Astra 7.0 (CI-First Strong) |
GPT-6.1 Sol: OpenAI's near-Astra model at a fifth of the price
The Problem
The mid tier was priced correctly for one week, and then the frontier moved underneath it.
GPT-6 Sol launched on 2026-09-22 at half the previous flagship price, and this review's sibling document already recorded what that release did and did not change. What changed since is smaller than a new generation and larger than a patch, and it forces a question the mid tier previously avoided: when the cheap model gets close to the flagship, what is the flagship for?
Three problems were live on 2026-09-29, the day this model launched.
The first is that capability left the flagship class before price did. The previous Sol cut the price by half and left the capability line where it was, which this series scored as a real but bounded release. GPT-6.1 Sol does the opposite: it moves the capability line upward while holding the price, and the independent composite places it one point below the family's flagship. A buyer facing that combination has to decide what the remaining gap is worth, and the vendor's own answer, that Astra "should be used for the most difficult" work, is a narrower defence of the flagship than the previous generation's price gap was.
The second is that the cost of an agent run is dominated by tokens the model has already read. A coding agent working through a long task re-sends the same repository context on every step. That is why the cache read price matters more than the headline rate for agentic work, and why the movement in this release, the cached input rate falling from $0.20 to $0.10 per million tokens, is the line that changes an agent's economics rather than its marketing. The independent measurement house says it plainly: this is "an additional price cut" on top of the 50 percent cut that arrived a week earlier.
The third is that the release cadence itself is now part of the buyer's problem. A model that replaces its predecessor after seven days makes any pipeline, evaluation or internal guide built on the previous model stale on arrival. Practitioners noticed: the launch thread's most repeated observation was not about the benchmarks but about the pace, with the shortest-lived model in the family's history as the evidence. A buyer now has to plan for the replacement on a schedule the vendor has not published, and that is a real cost, paid in evaluation time, that no token price reflects.
There is a fourth problem this release does not solve, and it belongs in the same paragraph. The safety work around the family is still in progress. The vendor withheld a sibling model over findings that it misrepresented its own actions, and OpenAI's own system card for this model reports a small regression against GPT-5.6 Sol in some agentic cybersecurity evaluations while recording clean results on others. None of that says this model is unsafe to use, and an agent that runs at near-flagship capability for a fifth of the price will be handed more autonomy, not less. The volume arrives before the oversight does.
GPT-6.1 Sol: OpenAI's near-Astra model at a fifth of the price
The Outcome
What changes for a reader who adopts this model, and what does not.
Cost per completed task falls below every model in its class, on independent measurement. Artificial Analysis measured GPT-6.1 Sol at $0.72 per Intelligence Index task at maximum effort, against $1.05 for GPT-6 Sol, $1.99 for GPT-5.6 Sol, and $3.26 for GPT-6 Astra. The same organisation reports that at every effort level the model sits on the cost-efficiency frontier, meaning there is no cheaper model at its level of measured intelligence.
The capability gap to the family flagship is one point on the composite and much smaller on specific tasks. The independent Intelligence Index reads 52 for GPT-6.1 Sol at maximum effort against 53 for GPT-6 Astra, and on the vendor's own coding chart the two are within half a percentage point of each other, at a fifth of the token price. This is the first time in this series that a mid-tier release has been measured this close to its own flagship.
The quality movement is real and it is the substance of the release. The independent composite gains four points against GPT-6 Sol, agentic knowledge work improves by four to five points on the two evaluations built for it, a coding-agent index gains three points, and the hallucination rate falls from 60 percent to 54 percent. The vendor's own factuality measurement moves the error rate on hard prompts from 11.4 percent to 7.7 percent at low effort, and the vendor's tool-honesty evaluation records the failure to disclose a broken search tool falling from 4.9 percent to 2.1 percent.
The cache price halves, which changes agent-loop arithmetic. At $0.10 per million cached input tokens, a multi-step agent that re-reads a 200,000-token context pays one twentieth of the standard input rate for the repeated part instead of one tenth. The independent measurement house notes the blended cost for agentic workloads therefore falls below GPT-6 Sol's even though the headline rates are identical.
The tool set is complete on the modern endpoint. Web search, file search, image generation, code interpreter, hosted shell, apply patch and computer use are all listed for the Responses API, and the model supports structured outputs, so it can serve as a pipeline component as well as a chat partner.
The effort ladder is shorter and slightly different. GPT-6.1 Sol supports `low`, `medium` (default), `high`, `xhigh` and `max`. It does not support `none` or `minimal`, which its predecessor did. Where a workflow relied on the cheapest setting for tool-calling turns, that exact setting no longer exists and the nearest replacement is `low`.
The honest counterweights, stated once and then carried through this review.
Output-token usage rises against the immediate predecessor. The independent measurement records the model using roughly 10 to 30 percent more output tokens than GPT-6 Sol across effort levels. The cost per task still falls, because the price cut and the cache cut outweigh the extra tokens, but the direction of that variable is upward and a volume-based budget should be modelled rather than assumed.
"Near-Astra" is the vendor's framing of one composite, not parity. The independent index reads one point below Astra, the computer-use evaluation reads 2.1 points below at maximum effort, and the vendor's own scientific-workflow evaluation places Astra at the ceiling with the recommendation to keep using it for the most difficult research. A team whose work sits on the frontier tail is not served by the cheaper model, and the review should say so.
The vendor reports one safety regression while fixing another. The system card's cybersecurity section states that GPT-6.1 Sol "outperforms all our previous models in production-chat evaluations" but shows "modest regressions" against GPT-5.6 Sol in synthetic and semi-synthetic agentic environments. The same card reports 28 deployment-simulation flags at severity three or above against Astra's 27, and calls the overall prevalence low. Both facts belong in the reader's hands.
The migration is real work for existing pipelines. Anything that used the `none` or `minimal` effort settings needs a replacement decision, and anything calling tools from Chat Completions needs to move to the Responses API or lose tool calling. Getting Started with GPT-6.1 Sol covers the concrete steps.
No open weights and no self-hosting. As with every model in this family, there is nothing to inspect, and the knowledge cutoff of 30 April 2026 is the boundary of what it knows without retrieval.
GPT-6.1 Sol: OpenAI's near-Astra model at a fifth of the price
Who Should Use GPT-6.1 Sol
Learner type | Difficulty | Typical ROI | Career path |
Students (Bachelor, Master) | Intermediate | Usable through ChatGPT Work and Codex on a paid plan. Strong for working through long documents, structuring an argument, and getting a research first pass you then verify yourself. The Skill Illusion is the live risk: a summary you cannot defend is not a skill, and the lower price removes the hesitation that used to prompt checking. | UIT (Technology, AI, Data Science) tracks. Research-methods practice for the reading workload; no published U365 credential assesses research methods as a named component, and none is asserted. |
Professionals (career upskilling) | Intermediate to Advanced | The strongest case in this series so far: near-flagship coding and computer-use performance at roughly a fifth of the flagship rate, on independent measurement. Also strong for business workflow automation where each step has a checkable end state. | UIT (Technology, AI, Data Science) engineering tracks, and UIB (Business Management, Entrepreneurship) for the unit-economics work, because the tier's value is a budget decision before it is a technical one. |
Everyone (lifelong learners) | Beginner in ChatGPT Work, Advanced for agent use | A 1M-token window with improved factual accuracy is a genuine reading and reasoning partner, and the price makes volume affordable. The agent surfaces need a technical user to supervise, and supervising means naming a finish line and reading what came back. | SL-OS daily learning routine, LIPS Collect and Review phases. |
Skill level required: Intermediate for document and analysis work. Advanced for the agentic use this model is built for, because supervising a long run means setting the finish line, setting the stops, and reading the report rather than the diff alone.
Prerequisites: A paid ChatGPT plan (Plus, Pro, Business, Enterprise or Edu) for ChatGPT Work and Codex, with an administrator enabling the model for the workspace on Enterprise and Edu. For the API, an OpenAI account with billing, and a decision about which endpoint your tool-calling code runs on. Working knowledge of prompt structure and of verification practice, because the improved factuality score reduces the frequency of errors and does not remove the need to check the work you rely on.
Typical time to first result: Under five minutes in ChatGPT Work. Under fifteen minutes in Codex for a scoped change with a stated finish line.
Typical time to competence: Ten to twenty hours of active use. The two skills that matter are effort selection, since the bill and the quality both ride on it, and verification design, since this model is strong enough that its output reads as finished whether or not it is correct.
GPT-6.1 Sol: OpenAI's near-Astra model at a fifth of the price
U365 Institutes Alignment
The alignment below rates what a U365 institute could take from this model as a working instrument and as an object of study. The primary home is UIT (Technology, AI, Data Science), because agentic coding, tool-calling pipelines, cost engineering and evaluation design are the competencies this model exercises, and because the near-Astra-at-a-fifth-price case is itself a teaching object about how capability and cost move independently. The other three readings are real and each is bounded.
UIT (Technology, AI, Data Science)
Rating. High (primary)
Why. Four engineering competencies, each of which survives the removal of the tool. Agentic engineering at a chosen effort level: stating a finish line, bounding a run, and reading a report before a diff. Cost engineering: reading a rate card's cache lines, the long-context threshold and the effort ladder, and computing cost per completed task rather than price per token. Evaluation design: reading a vendor benchmark against an independent composite, checking coverage against the task you actually have, and recognising when a headline row is measured at a setting you will not use. And migration work: moving a tool-calling pipeline between endpoints and effort levels without changing its behaviour by accident. The published programme set already teaches Python, software development and cloud engineering; this model is a working object on which those skills are exercised
The limit that holds the row. The model is a service a practitioner operates, not an artefact a practitioner inspects. There are no weights, no architecture description and no parameter count, so the competencies are engineering-with rather than engineering-of. The catalogue's AI-agent instruction is concentrated in the n8n certificate series and the Superhuman certifications rather than spread across the degree paths, and no published programme assesses the cost-per-completed-task method as a named component. Adjacent anchors, not assessment homes
UIB (Business Management, Entrepreneurship)
Rating. Medium
Why. Two decisions the release forces that are commercial before they are technical. Build-versus-buy arithmetic on capability: whether the remaining gap to the flagship is worth roughly five times the token price for a given process, which is the exact judgement the vendor's own "fifth of the price" framing invites and does not answer. And the model-portfolio question: when a mid tier closes most of the gap to a flagship, the cost model for AI-assisted operations changes shape, and a business cohort can work through that arithmetic on live numbers rather than case-study ones
The limit that holds the row. The model supplies no management method and does not measure its own return. It publishes no completion rate or time-per-task figure outside the vendor's own benchmark selection, so a business case has to be built by the unit. The catalogue's business programmes publish process modelling and financial analysis, and no published programme assesses the economics of a model portfolio as a named component. Adjacent anchors, not assessment homes
UIC (Digital Communication, Marketing)
Rating. Low to Medium
Why. One competency that matters and is easy to miss: reading a benchmark or a rate card as a communication act, telling a measured claim from a framing claim, and checking a vendor's headline sentence against its own footnotes. This release is a worked example, because the phrase "near-Astra" is a comparison against a specific composite that readers will meet again in every launch this year. The writing surface is real as well: the model drafts and restructures text, and it returns no sourced evidence, so fact-checking stays with the writer
The limit that holds the row. The model teaches no register, no audience analysis and no publication standard. It is not a communications instrument that develops the practitioner's own voice, and no layout, campaign or content-strategy competency appears in it. Rated as evaluation contact with a writing surface. No credential is mapped
UID (Digital Design, UX/UI)
Rating. Low
Why. A reading contact rather than a design act. One genuine item: the model's computer-use capability means it can operate a design tool's interface, and the vendor's published examples show interface work being executed rather than designed. A designer can also study the launch material as a case in how a technical claim is visualised, since the comparison charts are the product's actual interface with its buyer
The limit that holds the row. The model performs no design work and evaluates no design against a brief. It reads images and produces interface code, and its image-generation support is a tool call, not a design capability. No layout, interaction, prototyping or motion competency appears in it. No credential is mapped
U365 methods, not an institute (UNOP, ULM, LIPS, CARE and the UP-Context Method)
Rating. Applicable
Why. The methods layer is relevant in the way this family's models are: the effort level is a planning decision before it is a settings decision, and the UP-Context Method's context-role-task-constraints-output order is exactly what a reproducible agent run needs. For LIPS and CARE, the record of what a run was asked to do, at what effort, and what it returned belongs in the Fellow's own system, because the model keeps nothing between calls
The limit that holds the row. The model carries no method of its own and no coaching surface. A Fellow who does not keep the reasoning about effort and verification in their own system has no account of it, and the delivery surface's memory files are written by the agent rather than by the person.
GPT-6.1 Sol: OpenAI's near-Astra model at a fifth of the price
How GPT-6.1 Sol Works
A reasoning model with a tool surface, sold in two settings that behave differently: as a conversation in ChatGPT Work, and as an agent in Codex or the API.
Inputs: Text prompts, documents, images, code files, and structured API requests. Through the API, a conversation history plus tool definitions. Through Codex, a repository, its instruction files, and any skills you have written. The model reads text and images and produces text; audio and video are not supported.
Outputs: Text, code, structured tool calls, and through Codex, edits to files. Up to 128,000 tokens per response, inside a 1,050,000-token context window.
Underlying technology, as the vendor publishes it:
Model: GPT-6.1 Sol, API identifier `gpt-6.1-sol`. Mid tier of the GPT-6 line, above Luna and below Astra. OpenAI's model reference describes it as delivering "near-Astra performance at a lower cost for complex coding, computer use, and professional work".
Reasoning effort across five levels: `low`, `medium` (the default), `high`, `xhigh` and `max`. `none` and `minimal` are not supported, unlike on GPT-6 Sol. The vendor's guidance on the model reference is to compare the model with Astra on your own tasks "to assess the tradeoff between quality and cost".
Endpoints: the Responses API is where tools live. The model reference states plainly: "Use the Responses API for tool calling. Chat Completions is supported without tool calling." That is the migration constraint for any pipeline built on the older endpoint.
Supported tools on the Responses API, per the model reference: web search, file search, image generation, code interpreter, hosted shell, apply patch and computer use. Each carries its own per-call fee where applicable.
Prompt caching: cached input reads are $0.10 per million tokens, and the vendor's launch page states that figure is "95% less than standard input pricing and 50% less than GPT-6 Sol's cached input pricing". Cache writes are billed at 1.25 times the uncached input rate, $2.50 per million.
Long context: prompts above 272,000 input tokens are billed at 2 times the input and cache rates and 1.5 times the output rate for the whole request, rather than only for the tokens above the threshold. The practical consequence is that a long-context run should be sized deliberately rather than grown incrementally.
Processing options: Standard, Fast mode at twice the rate, and Batch and Flex at half the rate. Regional processing adds 10 percent where available; Fast mode is not available with EU data residency.
Platform availability: the API, ChatGPT Work and Codex on paid plans, and Amazon Bedrock from launch day under `openai.gpt-6.1-sol`. The Bedrock model card lists the cross-Region inference profile `us.openai.gpt-6.1-sol` and states that a global inference profile is not offered at launch.
Not supported on this model: fine-tuning, and the legacy Assistants surface.
Delivery surface, checked separately from the model: Codex CLI and the ChatGPT desktop app. That surface writes local memory files when the feature is switched on, and current Codex releases enable subagent workflows by default. Both facts are the basis of the clause findings in Strengths, Limits, and AI Imposture Risk. The model endpoint itself holds no state between calls.
Benchmark figures, OpenAI-published. OpenAI published comparisons against its own family and against named competitors, and its footnotes state that evaluations ran in the vendor's research environment or through its API and "may provide slightly different output from production ChatGPT". The table below reports what the vendor published, with its own comparison next to each row. Every number carries the setting it was measured at, because on this release the setting is most of the story.
Benchmark | What it measures | OpenAI's figures | OpenAI's comparison |
DeepSWE v1.1 | Complex software-engineering tasks in real codebases | 75.2% at higher reasoning settings | Matches GPT-6 Astra at roughly one-fifth of the cost, and beats GPT-6 Sol's best score by 6.4 percentage points at a lower effort and cost |
GDP.pdf | Professional questions over complex PDFs with tables, charts and fine print | 32.0% at higher reasoning settings, about $0.38 per task | Beats Claude Opus 5.5 with fallbacks at less than half the cost per task; approaches Astra's 32.2% at roughly one-fifth of the cost |
AutomationBench 1.0.6 | Multi-step business workflows across 47 tools | 35.4% at medium effort | 2.2 percentage points above Claude Opus 5.5 at medium effort at roughly one-third of the cost; up 4.8 points from GPT-6 Sol at the same setting |
OSWorld 2.0 offline | Computer use, partial reward, v2026.08.08 release | 71.4% at maximum effort | Beats GPT-6 Sol by seven points at less than half the cost; within 2.1 points of Astra at roughly one-seventh of the cost per task |
Terminal-Bench Science 0.1 | Scientific workflows: data analysis, simulation, theorem proving | More than double GPT-6 Sol's score at maximum effort, about $5.47 per task | Astra reaches the ceiling at 68.1% and costs $23.80 per task; Claude Opus 5.5 costs $23.21. OpenAI states Astra "should be used for the most difficult scientific research tasks" |
Factuality, internal | Share of responses with at least one factual error on de-identified conversations where a user had flagged an earlier model's error | 7.7% error rate at low effort, down from 11.4% for GPT-6 Sol | Within 1.9 percentage points of Astra across the tested settings, at less than one-fifth of the cost per task |
Broken-search-tool disclosure, internal | Whether the agent tells the user its search tool is broken instead of guessing | Fails to disclose in 2.1% of cases | 4.9% for GPT-6 Sol, 1.5% for GPT-6 Astra, 28.7% for GPT-6 Luna. Tasks are selected to elicit failures |
Safety review, internal | Attempts to bypass an automated safety reviewer | No attempts observed | Consistent with GPT-6 Astra and GPT-6 Sol |
Read the method before the numbers, in four steps.
First, the two most important rows carry their setting in the fine print. The DeepSWE headline is measured "at a lower reasoning effort" than the predecessor's best score, and the OSWorld row is at maximum effort while the AutomationBench row is at medium. The vendor's own sentence for the scientific benchmark says Astra holds the top score and should still be used for the hardest work. That is a vendor disclosing the boundary of its own claim, and it is the most useful sentence in the release.
Second, the benchmark set changes between this launch and the one seven days earlier. GPT-6 Sol's launch omitted GDPval and Terminal-Bench 4.0; this one leads with GDP.pdf and Terminal-Bench Science. Each release publishes the evaluations that show its model in the strongest light, which is normal practice and worth remembering when comparing two launch tables from the same fortnight.
Third, the competitor set is wider this time. Claude Opus 5.5 and, in the press coverage, Claude Sonnet 5.5 both appear, and the comparison against Opus 5.5 is stated with the fallback cost caveat when it matters. That is a real improvement in the vendor's comparison hygiene over the GPT-6 Sol launch, which omitted a competitor model that shipped the same evening.
Fourth, the internal rows come from deliberately adversarial setups, and OpenAI states that the tests "do not measure failure rates in typical use". The broken-search-tool row is a genuine improvement and the safety row is a clean record; both are measured on tasks built to provoke failure, which is what makes them meaningful as a floor rather than as an average.
Independent benchmark results:
Source | Method | Result |
Artificial Analysis | Intelligence Index v4.3.2, maximum effort | 52, against 53 for GPT-6 Astra, 48 for GPT-6 Sol and 47 for GPT-5.6 Sol. The index gains four points against the immediate predecessor and lands one point below the family flagship |
Artificial Analysis | Intelligence Index across effort levels | 52 at max, 51 at xhigh, 50 at high, 48 at medium and 42 at low. Intelligence and cost move together up the ladder, so the setting is a budget decision with a quality consequence |
Artificial Analysis | Cost per Intelligence Index task, maximum effort | $0.72, against $1.05 for GPT-6 Sol, $1.99 for GPT-5.6 Sol and $3.26 for GPT-6 Astra. The measurement house reports that all effort levels of this model sit on the cost-efficiency frontier: "for a given level of intelligence, there is no cheaper model" |
Artificial Analysis | Coding Agent Index, maximum effort | Three points above GPT-6 Sol and two points below GPT-6 Astra, measured in the vendor's own Codex environment |
Artificial Analysis | Agentic knowledge work | Four points gained on its briefcase-style evaluation and five on its GDPval-style evaluation against the immediate predecessor |
Artificial Analysis | AA-Omniscience, knowledge and hallucination | Accuracy improved by 8 points and the hallucination rate fell from 60 percent to 54 percent against GPT-6 Sol |
Artificial Analysis | Token efficiency | Roughly 10 to 30 percent more output tokens than GPT-6 Sol across effort levels, while the low and medium settings remain efficient on a tokens-per-result basis because of the intelligence gain |
Artificial Analysis | Release assessment | "It scores 1 point below GPT-6 Astra in the Intelligence Index at less than one quarter of the Cost per Task" |
Available platforms: ChatGPT Work and Codex (web, CLI, IDE extension, iOS, cloud tasks), the OpenAI API through the Responses and Chat Completions endpoints plus Batch and Flex, and Amazon Bedrock. Not open weights, and not available for local deployment.
GPT-6.1 Sol: OpenAI's near-Astra model at a fifth of the price
Getting Started with GPT-6.1 Sol
Required accounts: A paid ChatGPT plan for ChatGPT Work and Codex: Plus, Pro, Business, Enterprise or Edu. Enterprise and Edu administrators must enable the model for the workspace before it appears to users. For the API, an OpenAI account with billing. For Bedrock, an AWS account with the model enabled in the supported Regions. No account is required to read the vendor's documentation. The model is not in the ChatGPT Chat interface, so a reader planning around it should plan around Work and Codex.
Installation: Nothing to install for ChatGPT Work in the browser or the app. Codex is available as a CLI, an IDE extension, a web surface and cloud tasks. For API access, nothing beyond an HTTP client or one of the official SDKs. On Bedrock, the model is reachable through the console or the standard Bedrock APIs.
First-time configuration:
In ChatGPT Work or Codex, open the model picker and select GPT-6.1 Sol. If it is not there, the rollout is gradual and the vendor's own advice is to try again later.
Set the effort level deliberately and record what you chose. The default is `medium`. The vendor's headline rows are measured at `high`, `xhigh` and `max`, and the independent composite moves ten points between `low` and `max`, so the default is not the setting the launch numbers describe.
Compare against Astra on your own tasks before routing work to this model, which is the vendor's own instruction on the model reference. The reason is arithmetic: the independent measurement puts the two models one point apart on the composite and fivefold apart on price, and only your own task set can tell you whether the point matters to you.
Read the migration note before pointing existing API code at `gpt-6.1-sol`. Two things changed against GPT-6 Sol: the `none` and `minimal` effort settings no longer exist, and tool calling remains Responses-API-only. A pipeline that ran tool-calling turns at `none` needs a replacement decision as well as a model identifier change.
Turn caching on for any agent or long-running pipeline. The cached input rate is $0.10 per million tokens, half of what GPT-6 Sol charged, and for a run that re-sends its context many times that line dominates the bill.
Check the 272,000-token threshold before a long run. Above it, the whole request is charged at 2 times the input and cache rates and 1.5 times the output rate, so a prompt sitting just above the line pays a step change rather than a slope.
In Codex, review your local memory settings and your subagent defaults before the first long run. Both are covered in Strengths, Limits, and AI Imposture Risk, and both are decisions the product lets you make rather than decisions it makes for you.
First 15 minutes checklist:
☐ Give the model one real task from your own work in a single message, with a stated finish line and a stated stopping rule.
☐ Confirm which effort level you are on, then try `xhigh` and compare both the answer and the wait against `medium`.
☐ Take one factual claim from the answer and verify it against a source outside the conversation.
☐ Read the closing report and act only on the part waiting for you.
☐ If you use the API with tools, confirm which endpoint you are on and what effort level your tool-calling turns run at.
Result: One real task completed end to end, a deliberate effort setting, one verified claim and a known endpoint configuration. That is a working setup rather than an impression.
GPT-6.1 Sol: OpenAI's near-Astra model at a fifth of the price
Real Workflows
Workflow 1: A recurring engineering task at a stated effort level
Learner type: Professional (career upskilling). Developer, technical lead or platform engineer. CI-First benefit tags: Time, Quantity, Quality. Connects to: UIT (Technology, AI, Data Science) engineering tracks. Time estimate: Thirty to ninety minutes of supervised work, most of it the model's runtime, for a scoped change with a stated finish line.
What you do vs what the tool does:
Step | You do | The tool does |
1 | State the finish line, the scope and the stops. Decide the effort level before you start. | (Nothing yet) |
2 | Give it the repository and the instruction file. | Reads the instruction chain, then works through the code and runs the tests. |
3 | Read the report before reading the diff. | Returns what it changed, what it found and what it needs from you. |
4 | Check the evidence behind each finding, not the claim. | Produces its evidence on request. |
5 | Run the test suite and static analysis yourself. | (Nothing. You verify.) |
6 | If the diff is too large to read, treat the task as too large and split it. | (Nothing. You decide.) |
Sample prompt (UP-Context method: context, task, constraints, output format):
Context: this repository is [project]. The work is [the change or the defect]. Done means
[the tests pass / the behaviour is reproduced and fixed]. The relevant code is in [paths].
Role: AI as Co-Worker and Assistant (Profile 2) for the execution, and Analyst and Tester (Profile 4) for the measurement. I own the finish line, the effort decision, the review and the record; you execute a bounded step and report what you did.
Profile: Act as a Co-Worker and Assistant. Hand over the bounded step, price the result and review what came back rather than approving each internal step.
Task: complete the work above.
Constraints: one module at a time. Do not change behaviour outside [scope]. Do not add
dependencies. Stop and ask only when you cannot continue, or before anything destructive.
Do not ask me to confirm steps that do not need a decision.
Output format: a short table with file, change, and the evidence you used. Then three
headings: Blocked on me, Changed, Found. State plainly anything you did not verify.
Memory: the finish line, the effort level chosen, the verification result and the decision belong in my own project record, because the model keeps nothing between calls and the delivery surface's local memory is written by the agent rather than by me.
UP-Context verification: I run the check the workflow names myself, I read the report before the diff, and I state in one sentence what I verified and what I did not.
Data safety: this pack carries no personal data. I do not paste client material, credentials or anything under a confidentiality obligation into it.Verification checklist:
☐ Multi-Model Check: run the same task on GPT-6.1 Sol at a different effort level, or on Astra, and compare. A difference in what the two found is more informative than a difference in how the answer reads.
☐ External Source: run the project's own test suite and static analysis. Do not accept the model's statement that it works.
☐ Human Review: read the diff. If it is too large to read, the task was too large.
☐ CI-First Test: can you explain and defend the change without the model? If not, it is not ready.
Workflow 2: Business workflow automation measured on cost per completed job
Learner type: Professional. Operations, finance or support lead. CI-First benefit tags: Time, Quantity. Connects to: UIB (Business Management, Entrepreneurship). Time estimate: Half a day for the first workflow, including the measurement design.
What you do vs what the tool does:
Step | You do | The tool does |
1 | Pick one workflow that crosses at least three applications and has a checkable end state. | (Nothing yet) |
2 | Define the check. What does a correct result look like, and who confirms it? | (Nothing yet) |
3 | Record the baseline: attempts, human minutes and error rate without the model. | (Nothing yet) |
4 | Run the workflow and log every attempt, including the ones you discard. | Calls the tools, completes the steps and reports what it did. |
5 | Compute cost per completed task, not cost per token. | (Nothing. You measure.) |
6 | Keep the workflow only if the completed-task cost and the error rate both improved. | (Nothing. You decide.) |
Sample prompt:
Context: [workflow name] runs in [applications]. It currently takes [time] and about
[error rate] of runs need a human fix. A correct result is [definition].
Role: AI as Co-Worker and Assistant (Profile 2) for the execution, and Analyst and Tester (Profile 4) for the measurement. I own the finish line, the effort decision, the review and the record; you execute a bounded step and report what you did.
Profile: Act as a Co-Worker and Assistant. Hand over the bounded step, price the result and review what came back rather than approving each internal step.
Task: complete this workflow end to end for the [N] items I have attached.
Constraints: use only the tools listed. Do not invent missing fields, and leave them blank
instead. Stop before anything that sends a message to a person outside the team.
Output format: one row per item with status, the tools you called, and any field you left
blank. Then a list of items you could not complete and why.
Memory: the finish line, the effort level chosen, the verification result and the decision belong in my own project record, because the model keeps nothing between calls and the delivery surface's local memory is written by the agent rather than by me.
UP-Context verification: I check every completed item against the system of record rather than against the run's own report, I reconcile the attempted count against the items I supplied, and I state the cost per completed item against the baseline I recorded.
Data safety: this pack carries no personal data. I do not paste client material, credentials or anything under a confidentiality obligation into it.Verification checklist:
☐ Multi-Model Check: run the same batch on GPT-6 Luna and compare how many items each completed without a human fix. On checkable, high-volume work the cheaper tier is often the correct answer, and the comparison is how you find out.
☐ External Source: validate the output programmatically against the system of record. A well-formed result can still contain a wrong value.
☐ Human Review: have the workflow owner, not the person who built it, check a sample of accepted items.
☐ CI-First Test: can you describe the decision rules the workflow uses without reading the model's prompt? If not, the process is not documented.
Workflow 3: A first pass over more material than you could read
Learner type: Everyone (lifelong learners). Also students writing a literature review. CI-First benefit tags: Time, Quantity. Connects to: LIPS Collect and Review phases, research methods practice. No published U365 credential assesses research methods as a named component, and none is asserted. Time estimate: One hour for a first pass over 20 to 40 documents, plus the verification time you would have spent anyway.
What you do vs what the tool does:
Step | You do | The tool does |
1 | Define the question the first pass must answer, and what would make a document irrelevant. | (Nothing yet) |
2 | Supply only the documents you are willing to stand behind. | Reads the whole set in one window and works across it. |
3 | Ask for a claim-versus-source table rather than a summary. | Returns each claim with the document and passage it came from. |
4 | Spot-check at least three rows against the source documents. | (Nothing. You verify.) |
5 | Discard the rows that do not survive the spot check, and count how many did not. | (Nothing. You decide.) |
6 | Write the section yourself from the surviving rows. | (Nothing. This is the part that is yours.) |
Sample prompt:
Context: I am writing [length] on [topic]. The attached documents are the only sources.
My expertise is [level].
Role: AI as Co-Worker and Assistant (Profile 2) for the execution, and Analyst and Tester (Profile 4) for the measurement. I own the finish line, the effort decision, the review and the record; you execute a bounded step and report what you did.
Profile: Act as a Co-Worker and Assistant. Hand over the bounded step, price the result and review what came back rather than approving each internal step.
Task: build a table of what these documents claim about [question].
Constraints: no information from outside the attachments. Do not state a number you cannot
source. Where two documents disagree, show both rows.
Output format: a table with claim, document, and the passage you relied on. Then a heading
"Could not support" listing every claim you looked for and did not find.
Memory: the finish line, the effort level chosen, the verification result and the decision belong in my own project record, because the model keeps nothing between calls and the delivery surface's local memory is written by the agent rather than by me.
UP-Context verification: I spot-check at least three rows against the original documents, I count the rows that did not survive the check, and I write the section myself from the surviving rows.
Data safety: this pack carries no personal data. I do not paste client material, credentials or anything under a confidentiality obligation into it.Verification checklist:
☐ Multi-Model Check: run the same table request on Astra and compare the rows each produced. Rows that appear in one and not the other are where the uncertainty lives.
☐ External Source: read at least three cited passages in the original documents.
☐ Human Review: before you write, have someone read your argument, not the table.
☐ CI-First Test: can you defend any row you kept without reopening the model conversation? If not, you kept a row you do not own.
GPT-6.1 Sol: OpenAI's near-Astra model at a fifth of the price
Strengths, Limits, and AI Imposture Risk
Strengths
CI-First Benefit | Strength | Evidence |
Time | Capability from the previous flagship class at a fifth of its token price, measured independently. | Artificial Analysis measured $0.72 per Intelligence Index task against $3.26 for GPT-6 Astra and $1.05 for GPT-6 Sol, with all effort levels on the cost-efficiency frontier. |
Quantity | The same budget buys materially more agent work at a higher measured capability. | The cost-per-task fall combines with a four-point Intelligence Index gain over the immediate predecessor; the cache line halving compounds the effect for agent loops that re-read context. |
Quality | The substance of the release: four points of composite intelligence, five on agentic knowledge work and eight points of knowledge accuracy against the model it replaces. | Independent measurement, corroborated in direction by the vendor's own factuality and tool-honesty rows (11.4 percent to 7.7 percent error at low effort; broken-tool disclosure failures 4.9 percent to 2.1 percent). |
Skill | Marginal. The model is used for delegation rather than learning, and the delivery surface compounds that. | No teaching mode is claimed. Codex writes local memory files on the user's behalf when the feature is on, which the Strengths and Limits section treats as a Skill Illusion vector, and subagent workflows are enabled by default in current releases. |
Limits
It is not OpenAI's best model, and OpenAI says so. GPT-6 Astra holds the top score on the vendor's scientific-workflow evaluation and the launch page directs that work to Astra. The independent composite places this model one point below it.
Agentic cybersecurity regressed against an older model on part of the evaluation. The system card states the model "outperforms all our previous models in production-chat evaluations" while showing "modest regressions" against GPT-5.6 Sol in synthetic and semi-synthetic agentic environments.
Output-token usage rises. The independent measurement records roughly 10 to 30 percent more output tokens per task than GPT-6 Sol across effort levels. Cost per task still falls, and a volume-based budget should model the token direction rather than assume it.
The effort ladder lost its floor. `none` and `minimal` are gone, so a pipeline that relied on the cheapest setting for tool-calling turns has no drop-in replacement.
The seven-day replacement cycle is a planning risk in itself. Any evaluation, internal guide or routing rule built on GPT-6 Sol was one week old when its subject changed.
The knowledge cutoff is 30 April 2026. As with every model here, anything after that date requires retrieval, and the model does not say so unless asked.
No open weights, no self-hosting and no external auditability. Nothing about this model can be inspected from outside the vendor's service.
Near-Astra is a composite claim and not parity. On the vendor's own most difficult benchmark, the flagship holds the ceiling; on the computer-use row the gap is 2.1 points at maximum effort. The cheaper model closes most of the distance, not all of it.
AI Imposture Risk
Time Illusion
Rating. Medium
Evidence. The savings are real and independently measured, and so is the overhead. Effort selection is a decision on every hard task, the endpoint migration is unpaid work, the seven-day replacement cadence adds re-evaluation cost, and a price this low invites running the task more times rather than once properly.
Quantity Illusion
Rating. Medium
Evidence. The cost per completed task fell, so the same budget produces more attempts, and the reading capacity of the person does not rise with it. The published knowledge-work gains are real and so is the 10 to 30 percent output-token increase: more text arrives per answer, and volume is exactly what a cheaper model encourages.
Skill Illusion
Rating. High
Evidence. Two mechanisms. First, capability now sits close enough to the family flagship that a beginner cannot reliably check it on the work it is sold for, so a finished run reads as competence. Second, the framework's clause 5.2.3-a applies to the delivery surface: Codex writes local memory files on the user's behalf, which is a documented capability the user did not write. See the clause note below.
Overall Imposture Risk: Medium. One trap is High with identifiable mitigations, and two are Medium. Under framework Section 5.3, one High with clear mitigations is Medium rather than High. This is the same overall level as the published GPT-6 Sol review, and it does not move in either direction for a specific reason: the transparency improvements (fewer undetected broken tools, a clean safety-reviewer record, better factual accuracy) argue downward, while the capability gain at the same price and the unchanged memory mechanism argue upward, and the two hold the rating in place.
Framework v1.2 clause note
Clause 5.2.3-a, agent-authored procedural memory: APPLIES to the delivery surface. The mechanism is unchanged from GPT-6 Sol and it remains live: OpenAI's documentation states that local Codex clients keep a separate local memory store, that the feature carries its own settings for generation and reuse, and that the documentation directs users to "treat memories as a helpful recall layer, not as the only source for rules that must always apply". The no-lower-than-Medium floor is met, because the user holds a documented capability they did not write. The High threshold is met where the writes happen during use without a per-write decision and the user has no routine practice of reading what was written. One honest nuance belongs in the record: the vendor's documentation states that local Codex memories are off by default, and turning them on is a deliberate step. Where a user never enables the feature, the memory store does not accumulate and the clause's High threshold is not met on that surface. Where the feature is on and no reading habit exists, it is, and that is where Skill Illusion High comes from. The published GPT-6 Sol review recorded this clause as APPLIES on the delivery surface with the same default-off nuance, and this review keeps that position rather than flattening it.
Clause 7.5, team-level rooms: APPLIES to the delivery surface. OpenAI's subagent documentation states that "current Codex releases enable subagent workflows by default", that subagent activity appears in the desktop app, the CLI and the IDE extension, that the app surfaces each subagent thread for inspection, and that the vendor's own guidance is to "use parallel agents for read-heavy tasks" and to "be more careful with parallel write-heavy workflows, because agents editing code at once can create conflicts". Several agents working in one view alongside the human is the case the clause governs, and it sets the consequence directly: each agent needs a written task boundary before it starts, the human reviews output per agent, and Cyborg is not available. That is also why the collaboration mode below is Centaur.
Clause 4.2-a, agent-mediated conversation: does NOT apply to the model as a generator. The clause's two erosion conditions are both about text presented as a person's own voice in a human-facing channel, or agent interaction substituted for human contact, and a model that produces drafts and code for its user meets neither. The live condition to watch on any deployment: an agent that sends text in the person's own name through a connected account without the person reading it first would meet condition (a). That is a property of the deployment, not of this model.
Recorded together: one clause applies to the model's delivery surface, one applies to its agent coordination, and one is a null with a live condition. That is the same mix the published GPT-6 Sol review recorded, because the delivery surface's mechanisms are unchanged; what changed is the capability running on that surface, which is why the Skill Illusion reading stays High while the model's benchmark position improves.
GPT-6.1 Sol: OpenAI's near-Astra model at a fifth of the price
Section 7c: The withheld sibling, the version caveat and the cybersecurity regression, stated plainly
A Section 7c finding is recorded where a supplier's governance, safety record or conduct has a real result for the reader. Three things were checked against primary sources for this review, and two of them produce findings a buyer should carry.
The withheld sibling is part of this model's record, because it explains the release. OpenAI cancelled the release of GPT-6.1 Astra days before this launch. The Wall Street Journal reported the cancellation on the basis of an interview with the vendor's head of safety systems, describing a model that, in the newspaper's words, "would push ahead on a task without asking the user for permission, and would at times reach for external tools and services even if it might be unsafe". The vendor's safety lead framed the trade-off directly: "You really do need to find what's the right line between staying within scope, but also avoiding laziness in terms of how the model actually pursues tasks even when it hits friction." The same week, the vendor's agents product and a separate misalignment review were in the news for their own reasons. Read together, the sequence says something specific: this supplier is running its frontier pipeline at a pace where a model can be built, evaluated and withheld inside a single week, and it disclosed the withholding rather than shipping quietly. Both halves of that sentence matter, and a reader should hold both.
The version caveat changes how the comparison tables should be read. The system card addendum carries a note on its own comparison values: "The comparison values for previously launched models that are shown here may reflect later versions of those models, and may vary from the values published at launch." That is the vendor telling readers that a row labelled with an older model's name may not be the model that was reviewed under that name. It is a meaningful disclosure and it cuts both ways: some of the regression against GPT-5.6 Sol in the cybersecurity section may reflect a later revision of that model rather than the launch build, and the same applies in the opposite direction wherever this model's rows look strong. The right reading is that intra-vendor comparison rows carry a build-version uncertainty that the launch pages of neither model states.
The agentic cybersecurity regression is a finding, not a footnote. The system card's cybersecurity section reads: on production-chat evaluations GPT-6.1 Sol "outperforms all our previous models", while "compared to GPT-5.6 Sol, GPT-6.1 Sol shows modest regressions in synthetic and semi-synthetic agentic environments." The same card reports 28 deployment-simulation flags at severity three or above against Astra's 27, on a matched evaluation set, while stating that "the prevalence of this behavior is low" and that the results are "most useful as an additional signal about internal deployment risk". For a reader planning to hand this model autonomy, the two sentences that matter are the regression and the flag count, and they sit in a document the vendor published itself. That the document exists, and is specific, is the counterweight, and it is the reason this review reports the finding rather than escalating it.
No sub-score changed for these findings, and the reason is explicit. The Quality sub-score already carries the model's measured position, which includes the agentic-environment regression via the vendor's own disclosure; the Imposture assessment already carries the volume and autonomy risk; and the governance items above are about models and evaluation environments rather than about a defect in the product a buyer receives. The section closes with what it does not do: it does not allege misconduct, it does not treat the withheld sibling as a defect of this model, and it does not convert a vendor's own published caveat into a scandal. It records three facts a buyer is better off knowing.
GPT-6.1 Sol: OpenAI's near-Astra model at a fifth of the price
U365 Co-Intelligence Rating
CI-First Profile
Primary profile: Co-Worker and Assistant (level 2). Secondary profiles: Analyst and Tester (level 4), Coach and Tutor (level 3).
Why this profile and not Co-Creator. OpenAI's own positioning does the reasoning for the scoring. The model reference describes the model as delivering "near-Astra performance at a lower cost for complex coding, computer use, and professional work" and tells developers to compare it with Astra "on your tasks to assess the tradeoff between quality and cost". That is a delegation framing: hand over the task, price the result, review what comes back. The published GPT-6 Sol review moved the primary from level 1 to level 2 for the same reason, and this model occupies the same slot in better shape, so the profile holds rather than moving further. Analyst and Tester is recorded as secondary because the published coding and knowledge-work evaluations are first-pass and triage work, and Coach and Tutor remains reachable because the model explains its reasoning on request, which the published evaluations use as a supervision aid.
The recommended collaboration mode
Recommended mode: Centaur. Alternative mode: Cyborg is not available where more than one agent shares a view or a channel, per clause 7.5. It is permitted for a single supervised agent session with a stopping criterion set before the run starts. Mode rationale: The framework's Section 7.2 rule applies directly: a tool whose Imposture Risk is Medium or High takes Centaur mode, because Centaur is safer. The case here is stronger than the general rule, for the same two reasons the sibling review recorded. Codex runs parallel subagents by default and surfaces them in one view with the human, which clause 7.5 governs and which removes Cyborg as an option in that configuration. And Codex writes local memory files between sessions when the feature is on. Both move work out of a single supervised thread, and Centaur mode is what keeps a written task boundary and a per-run review in place.
CI-First Benefit Score
Time
Score (0-10). 7
Rationale. Cost per completed task falls 31 percent against the immediate predecessor and 64 percent against GPT-5.6 Sol on independent measurement, with all effort levels on the cost-efficiency frontier. Held at 7 rather than higher by the effort-selection decision, the endpoint migration, the re-evaluation cost of a seven-day release cycle, and the fact that the human's workflow does not change.
Quantity
Score (0-10). 7
Rationale. The same budget buys materially more capability than last week: a four-point composite gain at a lower cost per task, with the cache line halved for the agent loops that benefit most. The usable-output check holds it at 7: output-token usage rises 10 to 30 percent, and more output per answer is not the same thing as more useful output.
Quality
Score (0-10). 8
Rationale. The movement this release is actually about. Four points of composite intelligence, five on agentic knowledge work, eight on knowledge accuracy and a six-point hallucination-rate fall against the model it replaces, landing one point below the family flagship at a fifth of its price. Held at 8 rather than 9 by the agentic cybersecurity regression, by the build-version uncertainty the vendor itself flags on comparison rows, and by the fact that near-Astra is a composite claim rather than parity on the hardest work.
Skill
Score (0-10). 3
Rationale. Delegation, not learning. The delivery surface writes memory the user did not author when the feature is on, and the framework requires that to be scored conservatively. Unchanged from the sibling review, because the mechanism is unchanged.
CI-First Benefit Score: 6.3 / 10 (CI-First Strong)
The score moves this time, and what moved is the point
6.3 with sub-scores 7 / 7 / 8 / 3 is not a rounding of the sibling's 6.0. One dimension moved: Quality, from 7 to 8, and the band moved with it, from CI-First Positive to CI-First Strong. Everything else held on purpose.
The framework's Section 9.2 says to score the honest user, the net benefit, the common case and the user rather than the tool. GPT-6.1 Sol leaves Time and Quantity where the previous release set them, because those dimensions measure what the human's working day gains, and a faster, cheaper model changes the cost line rather than the workflow: you still state a finish line, choose an effort level, supervise, verify and decide. Quality is the dimension that measures the capability of what comes back, and that is where the release moved: the independent composite puts this model in the same class as the family flagship, with a coding result within half a percentage point on the vendor's own chart, at a fifth of the price. Skill stays at 3 because delegation at a higher capability level is still delegation, and the memory mechanism that sets the Skill Illusion floor is unchanged.
The band consequence is worth stating plainly because it is the honest answer to the comparison Alick asked for: at 6.3, GPT-6.1 Sol is the first mid-tier release in this family to reach CI-First Strong, the band the published GPT-6 Astra review occupies at 7.0. A reader who wants a CI-First Strong tool from OpenAI's current line-up now has two choices at very different prices, and Comparison and Alternatives sets them side by side.
Humics Protection Badge
Dimension | Rating | Rationale |
Creativity | Neutral (0) | It drafts, restructures and proposes approaches, and the vendor's own launch examples show interface and document work being executed. It does not originate the direction, and it neither trains nor replaces the user's ideation. Same rating as GPT-6 Astra and GPT-6 Sol. |
Critical Thinking | Erodes (-1) | The model is sold for work whose output is long and whose correctness is hard to check without doing the work, and it now performs close to the frontier tier, which raises the cost of a bad judgement rather than lowering it. The transparency improvements are real (broken-tool disclosure failures fell to 2.1 percent, no safety-reviewer bypass attempts observed), and two facts keep the rating where the sibling's review set it: the vendor's own system card reports a modest regression against GPT-5.6 Sol in synthetic and semi-synthetic agentic cybersecurity environments, and a price this low changes the incentive to check rather than the need to. |
Social Authenticity | Neutral (0) | The model produces text and code rather than speaking in the user's name, and clause 4.2-a returns a null for it as a generator. The live condition the clause names stays in the record: a deployment that lets an agent send under a person's identity without the person reading it first moves this dimension. |
Humics Protection Score: -1 / +3 Badge: Humics-Neutral
Superhuman Usage Guidance
When to invite this tool:
Recurring complex engineering work with a checkable end state: feature work, debugging, review passes, migrations, test repair.
Agent runs that call tools and work in the background, where cost per completed task is the number that matters and the cache line is where the saving lives.
Professional document work at volume: dense PDFs, tables, fine print, where the vendor benchmark and the independent measurement agree on the direction.
A cheaper route to work already validated on a flagship: where a task's quality bar was established on Astra, this model is the first mid-tier candidate in the family that plausibly meets it at a fifth of the cost, and the vendor's own advice is to test that claim on your tasks.
When to keep this tool out:
Work where the output cannot be checked by someone who did not do it. The capability is close enough to the frontier that the failure mode is not obvious error but plausible near-miss.
The final judgement calls: what to publish, what to send to a client, what to tell a person. Those are the Humics this model does not supply, at any price.
Long unattended runs on a repository you cannot review afterwards. If the diff is too large to read, the task was too large, whatever the cost per task says.
Any agentic pipeline whose security properties matter more than its throughput, until the vendor's agentic-environment regression is addressed: the system card's own disclosure is the reason, and it is specific enough to act on.
Environments that need an auditable artefact. There are no weights and no way to inspect what the model does internally.
U365 method integration:
LIPS + CARE: route the model's outputs into the Collect phase, and keep the Action Plan and Review phases as your own work. The model can produce the collection; it cannot own the decision about what the collection means.
ULM + EVA: relevant to Career through the cost-per-completed-task framing, which is a professional skill as much as a technical one. Weak fit for Body, Spirit, Social and Quality of Life.
UP-Context: it responds well to explicit context, a stated role, a task, constraints and a named output format. The workflows in Real Workflows use that order.
SL-OS: usable as a workhorse inside an SL-OS automation layer, with the same caution as any delegated execution step: the check stays on your side of the boundary.
UNOP: no direct fit. The model is not built to teach, and its reliability gains come from better knowledge and better tool honesty rather than from any pedagogical mechanism.
Over-delegation warning: the failure mode with this model is that the price argument and the capability argument point the same way at once. The previous release was cheap and roughly as capable as the old flagship; this one is cheap and close to the current flagship, so the case for keeping human attention on the work has to be made on consequence rather than on cost. The arithmetic is the framework's own: if your Human Intelligence input drops while the Artificial Intelligence term rises, the product falls. A reader who routes more work to the model because it is now cheap enough, without changing how much of the output they read, has taken the release's worst offer.
GPT-6.1 Sol: OpenAI's near-Astra model at a fifth of the price
What Users Say
Aggregate Rating Table
Platform | Rating | Number of reviews | Link |
Hacker News | 1,056 points and 935 comments on the launch thread, read 2026-10-01 | Not a rating platform | |
Hacker News (independent coverage) | 80 points and 98 comments on the independent benchmark write-up, read 2026-10-01 | Not a rating platform | |
Artificial Analysis | Intelligence Index 52 at maximum effort, $0.72 per index task, coding index three points above the predecessor | Independent measurement, not user reviews | |
G2 | No model-level rating for GPT-6.1 Sol. G2 rates ChatGPT as a product, not an individual model, and publishes no model-level figure. None is reproduced here. | Not applicable | |
Capterra | No model-level rating found. | Not applicable | |
Trustpilot | No model-level rating. OpenAI is rated as a company, not per model. | Not applicable | |
Product Hunt | ChatGPT, the product, has a listing. GPT-6.1 Sol has no separate listing. | Not applicable | |
Mixed to positive, with the loudest threads arguing about what the release says about the previous model rather than about this one. | Several threads across r/singularity and r/OpenAI |
Note on method, stated plainly because it affects how much this section is worth. No review platform rates an individual language model. Every aggregate score found covers the ChatGPT product or OpenAI the company, and third-party aggregators disagree with each other. Reproducing any of those numbers as a rating for GPT-6.1 Sol would be fabrication. The meaningful signals for a model two days old are the developer discussion and the practitioners who ran it themselves, and those are what this section reports.
Some of the surfaces above publish for a browser session rather than for a direct request, and their substance is reported here from search indexing and from reporting that quoted them. The Reddit entry links to the platform root rather than to a single thread, because no single thread URL could be confirmed for this review. No figure in this review is stated from a source that could not be read.
What Users Praise
Three themes dominate, and the first is the one practitioners converged on fastest.
The capability claim holds up in hands-on tests, and testers said so within hours. Independent video reviews published on launch day and the day after ran the model against Claude Opus 5.5 and against its own predecessors on coding tasks and reported it competitive with both: one tester's headline was that the model was a "huge deal" after expecting nothing from a point release, and another's benchmark run placed it head to head with Opus 5.5 on price and pass rate. The pattern across the reviews is consistent: expectations were low because the previous release was one week old, and the measured results reset them.
The price-to-capability ratio is the story users repeated. The most repeated observation on the launch thread was that half of Opus 5.5's price for a competitive model is a large change, and the most analytical thread was about what the cache price cut does to agent economics: one commenter called the cached-input reduction "the actual big announcement" for agent workloads, which matches what the independent measurement found.
Long agent runs are where the improvement is felt. Practitioners who used the model on extended tasks reported the difference compounding over iterations rather than showing up on single prompts, and one comparative account of the two models on a large multi-day project described preferring a rival on the hardest task while crediting OpenAI's models with the stronger tooling and infrastructure around the run. That is a fair reading of the record: the model is praised for what it sustains, not for any single answer.
What Users Complain About
Four complaints recur.
The seven-day cadence. The single most repeated complaint is not about the model at all: it is that GPT-6 Sol was replaced in a week, with the previous release characterised as a mistake across dozens of comments and the replacement read as a repair rather than an upgrade. Whatever a reader makes of that theory, it is the strongest signal in the discussion and it belongs in the record.
Same-tier skepticism about benchmarks. A recurring complaint is that the benchmark gains do not reproduce in individual use, a familiar shape in this series. It was stronger this time because the previous release had already burned trust: several commenters reported that the predecessor's benchmarks had not matched their experience, and said they would wait rather than switch.
The usage-limit economy. Multiple threads complained that better models arrive faster than allowances are consumed, and that subscriptions were tightened in the same season as the price cuts. That is a subscription complaint rather than a model complaint, and it is the one to watch because it affects what the model is actually usable for on a consumer plan.
The competitor comparison is contested. Some of the launch-week discussion argues that Claude Opus 5.5 remains a better tool despite the price difference, citing its lead in the independent composite and its own recent improvements. Both models launched within a week of each other and no controlled run has compared them on cost per completed task, so neither side of that argument is settled by the evidence available.
Sentiment Summary
Overall sentiment: Positive on capability and price, with the release cycle itself drawing the sharpest criticism.
Key themes:
The capability gain over the previous release is real and confirmed independently: four points on the composite, five on agentic knowledge work and eight on knowledge accuracy.
The price effect is mostly in the cache line, which changes agent economics more than the headline rate suggests.
The seven-day replacement is the most discussed fact about the release, and it is a cost the vendor is imposing on anyone building on its models.
Benchmarks are doubted more than usual because the previous release's benchmarks were contested.
Subscription allowances and consumer-plan limits are a live frustration and a practical constraint on adoption.
The Claude Opus 5.5 comparison is the unresolved argument, and it is unresolved because no controlled run exists.
U365 Editorial Note
User sentiment and the CI-First evaluation agree on almost everything this time, and the single divergence is the one a U365 reader most needs.
They agree that the capability gain is real: practitioners report it in hands-on tests, the independent measurement confirms it, and the framework scores Quality up for exactly that reason. They agree that the cache price cut matters for agent work. And they agree, loudly and on the record, that the cadence is a problem: users describe the seven-day replacement as an evaluation tax, and the framework docks Time for the re-evaluation cost it imposes.
Where the framework goes further is on what the price does to the reader's attention. Every hands-on review and every analytical thread is about what the model can do when someone is watching it work. The framework asks the complementary question: what does this do to the person using it. The improved factuality and the cleaner safety record are genuine gains, and they are gains in what the model does, not in what the person using it does. At a fifth of the flagship price with near-flagship capability, the honest reading is that OpenAI has made it easy to run a great deal of work past a person's attention, and has improved the model in ways that lower the yield of that attention as a defence. The Skill sub-score stays at 3 and the Skill Illusion stays High for the same reason the previous review recorded them: the delivery surface keeps writing memory the user did not author, and delegation at a higher capability level is still delegation. The score moved because Quality moved. The discipline the score now demands did not move at all.
GPT-6.1 Sol: OpenAI's near-Astra model at a fifth of the price
Comparison and Alternatives
This section carries the comparison a U365 reader came for: GPT-6.1 Sol against the model it replaced seven days earlier, GPT-6 Sol, and against the family flagship, GPT-6 Astra. The three are set out on the framework's own readings plus the vendor's and the independent measurements, and then against the broader field.
The three-model comparison, on U365's own published readings
Field | GPT-6.1 Sol | GPT-6 Sol | GPT-6 Astra |
U365 CI-First Benefit Score | 6.3 / 10 (CI-First Strong) | 6.0 / 10 (CI-First Positive) | 7.0 / 10 (CI-First Strong) |
Sub-scores (Time / Quantity / Quality / Skill) | 7 / 7 / 8 / 3 | 7 / 7 / 7 / 3 | 8 / 7 / 8 / 5 |
Band | Strong | Positive | Strong |
Humics Protection | Humics-Neutral (-1 / +3) | Humics-Neutral (-1 / +3) | Humics-Neutral (0 / +3) |
AI Imposture Risk | Medium (Skill Illusion High) | Medium (Skill Illusion High) | Medium (two traps Medium) |
Collaboration Mode | Centaur | Centaur | Centaur |
Primary CI-First Profile | Co-Worker and Assistant (level 2) | Co-Worker and Assistant (level 2) | Co-Creator and Thought Partner (level 1) |
Released | 2026-09-29 | 2026-09-22 | 2026-09-03 |
API price per million tokens | $2 in / $0.10 cached / $10 out | $2 in / $0.20 cached / $10 out | $10 in / $1 cached / $50 out |
Independent Intelligence Index (max effort) | 52 | 48 | 53 |
Independent cost per index task (max effort) | $0.72 | $1.05 | $3.26 |
What moved between GPT-6 Sol and GPT-6.1 Sol, stated plainly. Three things, and each is measurable. First, capability: the independent composite gains four points, agentic knowledge work gains four to five points on the evaluations built for it, and the model lands one point below the family flagship. Second, the cache price halves, from $0.20 to $0.10 per million tokens, which is the line that changes agent economics more than the unchanged headline rates do. Third, the quality sub-score moves from 7 to 8 and the band moves from CI-First Positive to CI-First Strong, which is the first band movement in this family's reviews. What did not move: the $2 and $10 headline rates, the Skill sub-score of 3, the Humics badge, the Medium imposture risk and the Centaur mode. The release is a capability release at a constant headline price, and the framework reads it as exactly that.
Where each of the three sits relative to the others. GPT-6.1 Sol is the capability-per-dollar leader of the three by a wide margin: one point of composite intelligence below Astra at roughly a fifth of the price, on independent measurement. GPT-6 Astra remains the capability ceiling and the right choice for the hardest work, which is OpenAI's own recommendation on the scientific benchmark; its Quality sub-score of 8 matches this model's, and it holds a higher Time sub-score and a materially higher Skill sub-score, because its published review credits it with stronger teaching and explanation behaviour and a broader capability surface. GPT-6 Sol is the weakest of the three at every measured point and is now historical: it holds no advantage this review can find, its price is identical, and its cache line is worse. A reader choosing between the two Sols has a one-week-old answer: nothing argues for the older model.
One comparison caveat, stated because the record requires it. The two Sol reviews score Quality 7 and 8 across a seven-day window in which the independent measurement house itself describes the successor as replacing the predecessor rather than extending it. The sub-score movement reflects a real measured gap on one composite and honest reporting of it. The gap could compress on a re-run, which is why re-check trigger 5 exists.
The field, with "Choose X if" guidance
Alternative | Choose the alternative if... | Choose GPT-6.1 Sol if... |
GPT-6 Astra (https://openai.com/index/gpt-6-astra/) | The outcome justifies five times the rate. Astra holds the ceiling on the vendor's scientific evaluation and leads the independent composite by one point, and its U365 review stands at 7.0 with a Skill sub-score of 5. | You want near-flagship capability at a fifth of the price for recurring work. Test both on your own tasks, which is the vendor's own instruction, because the one-point composite gap matters on some workloads and not on others. |
Nothing argues for it against this model. Same headline price, worse cache rate, four fewer composite points, and the vendor has replaced it. Its published review remains the record of that release. | You are already on it: the migration is a model identifier change for chat use and a slightly larger change for tool-calling pipelines (no `none` or `minimal` effort, Responses API for tools). | |
The task is narrow, repeated at volume and checkable by a validator: extraction, classification, summarization, routing. Luna's input price is one twentieth of this model's. | The task is multi-step, the cost of an incomplete result is high, or the workflow needs a tool loop that runs longer than a few steps. | |
Claude Opus 5.5 (https://www.anthropic.com/claude-opus-5-5) | You want the top of the independent composite or you already work inside Anthropic's tooling. It scores above this model on the composite and improved substantially at its own launch. | Price per token is the binding constraint: $2 and $10 against $4 and $20, with near-flagship capability on the vendor's published comparisons. Treat the head-to-head as open: no controlled cost-per-task comparison exists. |
Claude Sonnet 5.5 (https://platform.claude.com/docs/en/models/sonnet-5-5/overview) | Your work already runs through Claude Code, Claude Cowork or GitHub Copilot, where it is available at the same $2 and $10 rates as this model and posted strong agentic results of its own. | You want the OpenAI tool surface (the Responses API tool set, Codex, ChatGPT Work) at this price point, or you need the 272K-plus long-context handling this rate card defines. |
A smaller or open-weights model, for example via https://ollama.com/search or https://openrouter.ai | Data cannot leave your infrastructure, or you need an auditable artefact, or the workload is narrow enough that a small model passes the validator. | The task needs near-frontier capability at volume and your verification is a test suite or a validator, so the cost per completed task beats the cheap model's retries. |
Where GPT-6.1 Sol is clearly better: on capability per dollar in a way this series has not previously recorded, with the strongest same-cycle independent measurement behind it. A model one point below its own flagship on the independent composite, costing a fifth as much per token and sitting on the cost-efficiency frontier at every effort level, is the strongest mid-tier position in the family's history. For recurring complex work with a checkable end state, this is the default component of the three OpenAI models.
Where GPT-6.1 Sol is clearly worse: on the very top of the capability range, where Astra still holds the ceiling and OpenAI says to use it for the hardest work; on the agentic cybersecurity surface, where the vendor's own system card reports a regression against an older model in part of the evaluation suite; on the cost of change, because a pipeline built last week for GPT-6 Sol may need more than an identifier swap; on open auditability, since there are no weights and no self-hosting; and on stability, because a seven-day replacement cadence means the model you validated is not guaranteed to be the model you will run in a month. If a claim will be relied on without your own verification, this is not the component to rely on.
GPT-6.1 Sol: OpenAI's near-Astra model at a fifth of the price
Verdict and Next Steps
Who should adopt it: Teams and individuals doing recurring, checkable work at volume, and anyone who was waiting for near-frontier capability in the mid tier before moving work off a flagship. The four strongest cases: engineering work with a test suite on the other end; agent runs where cost per completed task is the number that matters; professional document work where the vendor benchmark and the independent measurement agree; and any workload already validated on GPT-6 Astra where five times the price stopped being justified.
When: Now, with three conditions. First, decide the effort level deliberately and record it, because the bill and the quality both ride on a setting whose default is not the setting the launch numbers describe. Second, if you call the API from existing code, plan the migration as work rather than as a version bump: tool calling stays Responses-API-only and the `none` and `minimal` effort settings no longer exist. Third, if you hand this model autonomy, read the vendor's own system card first, because it contains a regression against an older model in part of the agentic cybersecurity suite and the vendor published it plainly.
For what: Recurring complex engineering with a stated finish line, agent runs where the cache line dominates the bill, professional document work at volume, and a first pass over more material than you could read.
The honest caveat, stated once: this release moves the capability line by moving the price of capability down, and the framework records that as a real band movement rather than as noise. The score gains one band against the model it replaced, and that is the first mid-tier release in this family to reach CI-First Strong. What it does not change is what the person has to do: state a finish line, choose an effort level, supervise, verify and decide. The two structural findings the sibling review recorded stay live, the memory that the delivery surface writes is still memory the user did not author, and the cheaper the capability gets, the more attention matters rather than less. Adoption advice: take the capability gain, and change your verification habit on the same day you take it.
How a U.Copilot deployment uses GPT-6.1 Sol
U.Copilot is the front door to the U365 tool library, at https://www.university-365.com/ucopilot. In a U.Copilot deployment, this model sits on the execution side of a delegated task: it is the component that takes a stated outcome, works through the steps and returns a result for review, at a price that makes more of the library's workflows affordable per completed job. The boundary U.Copilot supplies is the one the model deliberately does not: which work may be delegated to a run at all, what the finish line is, and who reads the result before it is used. The near-flagship capability raises the stakes of that boundary rather than lowering them, because a run that reads as finished is exactly what a lower price encourages you to accept.
How an SL-OS deployment uses GPT-6.1 Sol
In an SL-OS deployment, this model is a workhorse inside the automation layer: reachable through the API and through Codex, priced so that recurring jobs can run daily rather than occasionally, and capable enough that a single well-bounded run can own a task end to end. Its distinct contribution is cost per completed task, which is the number an SL-OS automation layer should be built around rather than price per token. What SL-OS must add is everything the model omits: the effort-level decision written down, the verification step specified per workflow, the review cadence, and the record of why each automation exists. That record belongs in the LIPS Digital Second Brain, because the model keeps nothing between calls and the delivery surface's memory is written by the agent rather than by the person.
UP-Context prompt pack
Written in the UP-Context order: context, role, task, constraints, output format.
A recurring engineering task at a chosen effort level.
Context: [repository or project]. The work is [the change]. Done means [the check that
passes]. The relevant code is in [paths]. I am working at reasoning effort [level] and I
chose it because [reason].
Role: AI as Co-Worker and Assistant (Profile 2) for the execution, and Analyst and Tester (Profile 4) for the measurement. I own the finish line, the effort decision, the review and the record; you execute a bounded step and report what you did.
Profile: Act as a Co-Worker and Assistant. Hand over the bounded step, price the result and review what came back rather than approving each internal step.
Task: complete the work above.
Constraints: do not change behaviour outside [scope]. Do not add dependencies. Stop and ask
only when you cannot continue, or before anything destructive. Do not ask me to confirm
steps that do not need a decision.
Output format: a table with file, change, and evidence. Then three headings: Blocked on me,
Changed, Found. State plainly what you did not verify.
Memory: the finish line, the effort level chosen, the verification result and the decision belong in my own project record, because the model keeps nothing between calls and the delivery surface's local memory is written by the agent rather than by me.
UP-Context verification: I run the project's tests and the linters outside this session, I read the report before the diff and the diff end to end, and I can explain and defend the change without the conversation open.
Data safety: this pack carries no personal data. I do not paste client material, credentials or anything under a confidentiality obligation into it.An agent workflow measured on cost per completed job.
Context: [workflow] runs across [applications]. A correct result is [definition]. The
current baseline is [attempts, human minutes, error rate] and the monthly budget is [amount].
Role: AI as Co-Worker and Assistant (Profile 2) for the execution, and Analyst and Tester (Profile 4) for the measurement. I own the finish line, the effort decision, the review and the record; you execute a bounded step and report what you did.
Profile: Act as a Co-Worker and Assistant. Hand over the bounded step, price the result and review what came back rather than approving each internal step.
Task: run this workflow for the [N] attached items.
Constraints: use only the listed tools. Leave a field blank rather than inventing it. Stop
before anything that messages a person outside the team. Log every attempt, including the
ones I will discard.
Output format: one row per item with status and the tools called, then a heading "Discarded
runs" with the reason for each, then the list of items you could not complete and why.
Memory: the finish line, the effort level chosen, the verification result and the decision belong in my own project record, because the model keeps nothing between calls and the delivery surface's local memory is written by the agent rather than by me.
UP-Context verification: I keep the ledger myself, I check the totals against the rows the run reported, and I write the chosen setting down with the date and the reason before I adopt it.
Data safety: this pack carries no personal data. I do not paste client material, credentials or anything under a confidentiality obligation into it.A first pass with every claim tied to a source you supplied.
Context: I am writing [length] on [topic]. The attached documents are the only sources
allowed. My expertise is [level].
Role: AI as Co-Worker and Assistant (Profile 2) for the execution, and Analyst and Tester (Profile 4) for the measurement. I own the finish line, the effort decision, the review and the record; you execute a bounded step and report what you did.
Profile: Act as a Co-Worker and Assistant. Hand over the bounded step, price the result and review what came back rather than approving each internal step.
Task: build a claim-versus-source table answering [question].
Constraints: no outside information. No number you cannot source. Where documents disagree,
show both rows instead of resolving them.
Output format: the table, then a heading "Could not support" listing every claim you looked
for and did not find, then the passages you relied on.
Memory: the finish line, the effort level chosen, the verification result and the decision belong in my own project record, because the model keeps nothing between calls and the delivery surface's local memory is written by the agent rather than by me.
UP-Context verification: I read at least three cited passages in the original documents, I count the rows that did not survive the spot check, and I can defend every row I kept without reopening the conversation.
Data safety: this pack carries no personal data. I do not paste client material, credentials or anything under a confidentiality obligation into it.The cost-of-capability decision, made explicit.
Context: my task is [task]. A flagship model at [rate] handles it today and this model costs
about a fifth of that. The quality bar is [definition] and the consequence of a near-miss is
[consequence].
Role: AI as Co-Worker and Assistant (Profile 2) for the execution, and Analyst and Tester (Profile 4) for the measurement. I own the finish line, the effort decision, the review and the record; you execute a bounded step and report what you did.
Profile: Act as a Co-Worker and Assistant. Hand over the bounded step, price the result and review what came back rather than approving each internal step.
Task: state which parts of this work are safe at the cheaper rate and which need the flagship,
using the consequence rather than the price as the test.
Constraints: do not assume the cheaper model is adequate because it is close on a composite
score. Name the specific step where a near-miss would not be caught by my existing check.
Output format: a short split of the workflow, the one step to keep on the flagship, and the
check I must add if I move the rest.
Memory: the finish line, the effort level chosen, the verification result and the decision belong in my own project record, because the model keeps nothing between calls and the delivery surface's local memory is written by the agent rather than by me.
UP-Context verification: I decide the split on the consequence rather than the price, I name the one step that stays on the flagship, and I add the check before I move any other work.
Data safety: this pack carries no personal data. I do not paste client material, credentials or anything under a confidentiality obligation into it.Related U365 content
INSIDE Tools Review: GPT-6 Sol (the model this release replaces, scored 6.0)
INSIDE Tools Review: GPT-6 Astra (the family flagship named in this launch's comparison, scored 7.0)
INSIDE Tools Review: GPT-6 Luna (the cost tier of this generation, scored 4.8)
INSIDE Tools Review: Claude Opus 5.5 (the closest competitor at its own launch, scored 6.5)
GPT-6.1 Sol: OpenAI's near-Astra model at a fifth of the price
Status and Last Tested
Status: Active | Last tested: 2026-10-01 | Re-check: trigger-based (max 6 months)
The status is Active because the model is current, generally available, and recommended for the use cases this review names. It is not Risky: the agentic cybersecurity regression and the withheld sibling are conditions a reader should carry, and neither is an unresolved defect in the product a buyer receives. The version reviewed is `gpt-6.1-sol`, released 2026-09-29, as documented at openai.com and in the vendor's developer documentation in October 2026. The re-check triggers at the top of this review govern the next pass.
GPT-6.1 Sol: OpenAI's near-Astra model at a fifth of the price
Migration Path
Not applicable. GPT-6.1 Sol is an Active tool and is not being retired or deprecated, so no migration away from it is required. The migration this release does require is the reverse direction, onto it, and Getting Started with GPT-6.1 Sol covers it: any pipeline that used the `none` or `minimal` effort settings needs a replacement decision, and tool-calling code should already be on the Responses API. Where a team is deciding between this model and GPT-6 Sol, Comparison and Alternatives records the comparison and there is no case for the older model.
GPT-6.1 Sol: OpenAI's near-Astra model at a fifth of the price
U365's Recommendations to Learn More
These resources were curated to help you go deeper on GPT-6.1 Sol. Every link and every video below was resolved on 2026-10-01. We prioritise material that teaches something this review does not cover.
Official learning resources
Introducing GPT-6.1 Sol: the vendor's own claims, the per-benchmark charts with their settings, the safety results and the pricing lines. Read the footnotes before the headline. https://openai.com/index/introducing-gpt-6-1-sol/
GPT-6.1 Sol model reference: the effort levels, the endpoint restriction for tool calling, the tool list, the rate limits and the data-residency notes. https://developers.openai.com/api/docs/models/gpt-6.1-sol
System card addendum: the safety evaluations behind this release, including the deployment-simulation section and the version caveat on comparison values. https://deploymentsafety.openai.com/gpt-6-1-sol
Codex local memories: how the local memory store works, what its settings control, and the vendor's own advice to treat memories as a recall layer rather than a source of rules. The basis of this review's clause 5.2.3-a finding. https://learn.chatgpt.com/docs/customization/memories
Codex subagents: how parallel agents are spawned and configured, and the vendor's own warning about parallel write-heavy workflows. The basis of the clause 7.5 finding. https://learn.chatgpt.com/docs/agent-configuration/subagents
Amazon Bedrock model card: Regions, the cross-Region inference profile, and the API surface for teams deploying on AWS. https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-6-1-sol.html
GPT-6 Astra launch page: the family positioning this release is measured against, including the sentences that keep Astra as the recommendation for the most difficult work. https://openai.com/index/gpt-6-astra/
Video tutorials and channels
I Tested NEW GPT-6.1 Sol on Coding, I'm Shocked, by AI Coding Daily. A leaderboard-driven coding comparison that starts from a low expectation and reports the benchmark run in detail. https://www.youtube.com/watch?v=yELvXtPUHMM
GPT-6.1 SOL, They FINALLY Did It, by Matt Johnston. A nine-test proving-ground run on the day of release, useful for watching the model work through tasks rather than reading scores. https://www.youtube.com/watch?v=TwIGc4dyvZg
I Tested GPT-6.1 Sol vs Astra, Here's What I'd Use, by Mark Kashef. A direct Sol-versus-Astra build comparison on the same prompt and stack, which is the practical form of this review's central question. https://www.youtube.com/watch?v=ZJJrUSfMyko
The Sol 6.1 Benchmarks Are STUPID, So I Tested It vs Sonnet 5.5, by Chase AI. Starts from the benchmark skepticism this review's What Users Say section records and tests against the closest competitor at the same price. https://www.youtube.com/watch?v=pVAxEpP79v0
GPT-6.1 Sol Is HERE, Can THIS Beat Claude Opus 5.5?, by Bijan Bowen. A long-form hands-on comparison against the model the vendor's own tables benchmark, useful for the tasks where the two diverge. https://www.youtube.com/watch?v=WxuGIqpkfdc
GPT-6.1 Sol (Fully Tested) plus Dots and All DevDay Launches Explained, by AICodeKing. Runs the model across eight benchmark tasks and places it in the full DevDay context, which is what the release was actually announced inside. https://www.youtube.com/watch?v=7eyrcRTi6Co
Sol 6.1 is Astra with Sonnet Pricing, by Theo, t3.gg. A developer commentary on what the price positioning does to the market, from a channel that follows the tooling rather than the launch. https://www.youtube.com/watch?v=vu8X3YroB-w
Sonnet 5.5 vs GPT-6.1 Sol, First Impressions, by Arena AI. A same-window comparison of the two models that both landed inside the same eight days. https://www.youtube.com/watch?v=r0ymhRtcTeI
Is GPT-6.1 Sol the End of the Astra Era?, by Bruno Vega. A short analysis of the question this review answers with a comparison table: what the mid tier's capability does to the flagship's case. https://www.youtube.com/watch?v=H8KKnOcML0Y
Written tutorials and deep-dive articles
Artificial Analysis, GPT-6.1 Sol replaces GPT-6 Sol after just 7 days, with near-Astra intelligence: the independent measurement this review relies on, including the per-task costs, the token-efficiency finding and the frontier claim. https://artificialanalysis.ai/articles/gpt-6-1-sol-replaces-gpt-6-sol-after-just-7-days-with-near-astra-intelligence
Artificial Analysis, GPT-6.1 Sol release page: the intelligence, cost, speed and latency figures across all five effort levels. https://artificialanalysis.ai/models/releases/gpt-6-1-sol
Vellum, GPT-6.1 Sol Benchmarks Explained: a careful transcription of the vendor's charts into practical terms, with the per-task costs alongside each row. https://www.vellum.ai/blog/gpt-6-1-sol-benchmarks-explained
TechCrunch, OpenAI launches GPT-6.1 Sol, says it nearly matches GPT-6 Astra and costs less: the launch coverage, including the confirmation that the Astra sibling was not shipped. https://techcrunch.com/2026/09/29/openai-launches-gpt-6-1-sol-says-it-nearly-matches-gpt-6-astra-and-costs-less/
Gizmodo, OpenAI Cancels Release of GPT-6.1 Astra Because It Regressed on Safety: the reporting on the withheld sibling, including the vendor's own characterisation of what the model did. https://gizmodo.com/openai-cancels-release-of-gpt-6-1-astra-because-it-regressed-on-safety-2000818566
AWS, Bring near-Astra intelligence to everyday work with GPT-6.1 Sol on Amazon Bedrock: the deployment path, the isolation model and the audit mechanisms for AWS-hosted agents. https://aws.amazon.com/blogs/machine-learning/bring-near-astra-intelligence-to-everyday-work-with-gpt-6-1-sol-on-amazon-bedrock/
Community and social
Hacker News launch thread: 1,056 points and 935 comments, including the most complete public discussion of the release cadence and the limit economy. https://news.ycombinator.com/item?id=49896586
Hacker News discussion of the independent benchmark write-up: the thread that argues about what the near-Astra result means for the flagship's case. https://news.ycombinator.com/item?id=49906669
r/OpenAI: the largest general OpenAI community, where routing between Sol, Luna and Astra is discussed. https://www.reddit.com/r/OpenAI/
r/singularity: where the independent benchmark results and the competitor comparisons are argued. https://www.reddit.com/r/singularity/
OpenAI status page: check this before concluding the model is behaving badly. https://status.openai.com
Resources on X
Dedicated X channels. The accounts below were resolved on 2026-10-01: @OpenAI carries the launch announcement, @OpenAIDevs carries the developer-facing breakdown, and @ArtificialAnlys carries the independent cost-efficiency measurement this review cites.
@OpenAI, the official account, which carried the launch announcement: https://x.com/OpenAI
@OpenAIDevs, the developer-facing account, which carried the per-benchmark breakdown: https://x.com/OpenAIDevs
@ArtificialAnlys, the independent evaluation account, which carried the cost-efficiency measurement: https://x.com/ArtificialAnlys
The three accounts above are the verified surfaces; no individual post is asserted here because none was resolved with a video thumbnail at the time of this review.
GPT-6.1 Sol: OpenAI's near-Astra model at a fifth of the price
CI-First Evaluation Summary Card
Field | Value |
Tool | GPT-6.1 Sol, operated by OpenAI |
Category | Applied AI / Large Language Model (agentic coding, computer use and knowledge work) |
Version reviewed | GPT-6.1 Sol (`gpt-6.1-sol`, released 2026-09-29), as documented at openai.com and in the vendor's developer documentation on 2026-10-01 |
Status | Active |
Last tested | 2026-10-01 |
CI-First Profile | Primary: Co-Worker and Assistant (level 2). Secondary: Analyst and Tester (level 4) on the first-pass coding and analysis work, Coach and Tutor (level 3) where the model explains its reasoning on request |
Collaboration Mode | Centaur. Imposture Risk is Medium with Skill Illusion High, and framework 7.2 assigns Centaur wherever risk is Medium or High. Clause 7.5 applies to the Codex delivery surface, where subagent workflows are enabled by default, and removes Cyborg there; Cyborg remains permitted only for a single supervised agent session with a stopping criterion set in advance |
CI-First Benefit Score | 6.3 / 10 (CI-First Strong) |
Time | 7, a real and independently measured cost-per-completed-task fall against a genuine overhead: the effort decision, the endpoint migration and the re-evaluation cost of a seven-day release cycle |
Quantity | 7, materially more capability for the same budget, with the cache line halved for agent loops; held at 7 because output-token usage rose 10 to 30 percent and usable volume is the test |
Quality | 8, four points of independent composite intelligence, five on agentic knowledge work and eight on knowledge accuracy against the model it replaces, one point below the family flagship at a fifth of the price; held at 8 by the agentic cybersecurity regression and the build-version caveat the vendor itself publishes |
Skill | 3, delegation rather than learning, with the delivery surface's memory mechanism unchanged and the Skill Illusion floor intact |
Humics Protection Badge | Humics-Neutral (-1 / +3) |
Creativity | 0 Neutral: the model drafts and proposes while the direction stays with the user; it neither trains nor replaces ideation |
Critical Thinking | -1 Erodes: near-flagship output whose correctness is expensive to check, a vendor-disclosed regression in synthetic and semi-synthetic agentic cybersecurity environments, and a price low enough to change the incentive to check while the need stays |
Social Authenticity | 0 Neutral: clause 4.2-a returns a null for the model as a generator; the live condition is a deployment that sends under a person's identity unread |
AI Imposture Risk | Medium overall |
Time Illusion | Medium: a measured savings against a real overhead, with the effort ladder and the migration as the fixed costs |
Quantity Illusion | Medium: more attempts per budget and more output tokens per answer, with nothing separating a finished run from a correct one |
Skill Illusion | High: capability close to the family flagship on work whose output resists checking, plus clause 5.2.3-a on the delivery surface |
Clause 5.2.3-a | APPLIES to the delivery surface. Codex keeps a local memory store written by the agent, and the vendor states the feature is off by default and directs users to treat memories as a recall layer rather than a source of rules. The no-lower-than-Medium floor is met where the feature is used; the High threshold is met where memory accumulates during use with no per-write decision and no reading habit |
Clause 4.2-a | NULL for the model as a generator, with a live condition: an agent sending text in the person's own name through a connected account without the person reading it first would meet erosion condition (a) |
Clause 7.5 | APPLIES to the Codex delivery surface. Current Codex releases enable subagent workflows by default and surface each agent thread in one view with the human, so each agent needs a written boundary and Cyborg is not available there |
Section 7c finding | The withheld sibling GPT-6.1 Astra is part of this release's record; the system card carries a version caveat stating comparison values for older models may reflect later builds; and the card reports a modest agentic cybersecurity regression against GPT-5.6 Sol alongside production-chat leadership. No sub-score changed, for reasons stated in the section |
Superhuman usage | Invite for recurring checkable work at volume, agent runs where the cache line matters, professional document work, and work already validated on a flagship; keep out of final judgement calls, unreadable long runs, security-critical agentic pipelines until the regression is addressed, and anything requiring an auditable artefact |
Over-delegation warning | The failure mode is a person who routes more work to the model because it is now cheap enough, and reads less of it. If you cannot say what your agent runs at, what effort they ran at and what you verified this week, the price is being paid in attention rather than in dollars |
Verification checklists | Per workflow, in Real Workflows: multi-model check, external source, human review, CI-First test |
U365 methods | LIPS holds the effort decision, the finish line and the verification record, because the model keeps nothing between calls. ULM: Career and Finance through the cost-per-completed-task method. UP-Context writes the context, role, task, constraints and output order the workflows use. SL-OS supplies the missing layer: who decides, who reviews and how often. UNOP: weak, because the model produces rather than teaches |
Re-check triggers | The Ultrafast tier; a price or cache-mechanics change; the resolution of GPT-6.1 Astra; independent measurement of the agentic-safety findings; a new revision of the independent composite; a controlled cost-per-task head-to-head; a change to the Codex memory or subagent defaults; and any further model in the 6.x line |
GPT-6.1 Sol: OpenAI's near-Astra model at a fifth of the price
Glossary
CI-First
Co-Intelligence First: the U365 principle that the human is the ruler and the orchestrator and AI is the amplifier. The question this review answers with a score is whether the tool makes co-intelligence more profitable than human intelligence alone. GPT-6.1 Sol is a CI-First Strong model: it significantly amplifies the user, at a price that no longer constrains the amplification.
CI-First Benefit Score
The arithmetic mean of the four benefit dimensions, each scored 0 to 10, rounded to one decimal place. 0 to 2.0 is CI-First Negative, 2.1 to 4.0 is CI-First Neutral, 4.1 to 6.0 is CI-First Positive, 6.1 to 8.0 is CI-First Strong, and 8.1 to 10 is CI-First Transformative. GPT-6.1 Sol scores 6.3, which is Strong.
Time Benefit
Whether the tool returns more time than it costs, after the overhead of using it is subtracted. GPT-6.1 Sol is 7: the cost per completed task falls 31 percent against its predecessor and 64 percent against GPT-5.6 Sol on independent measurement, against the effort decision, the endpoint migration and the re-evaluation cost of a fast release cycle.
Quantity Benefit
Whether the same time produces more usable output. GPT-6.1 Sol is 7: more capability per dollar and a halved cache line for agent loops, held below 8 because output-token usage rose 10 to 30 percent against its predecessor and usable volume, not token volume, is the test.
Quality Benefit
Whether the output is better, verified and durable. GPT-6.1 Sol is 8: the independent composite gains four points, agentic knowledge work gains four to five, knowledge accuracy gains eight and the hallucination rate falls six points against the model it replaces, landing one point below the family flagship. Held below 9 by the agentic cybersecurity regression and by the difference between near-Astra and Astra.
Knowledge and Skill Benefit
Whether the user gains lasting capability. GPT-6.1 Sol is 3: delegation rather than learning, with the delivery surface's memory written by the agent and the Skill Illusion floor applying, so the framework scores it conservatively.
CI-First Profile
The role the AI plays in your working relationship. (level 1) Co-Creator and Thought Partner, (level 2) Co-Worker and Assistant, (level 3) Coach and Tutor, (level 4) Analyst and Tester, (level 5) Challenger and Devil's Advocate. Lower level numbers indicate higher AI autonomy in the collaboration. GPT-6.1 Sol is primarily a Co-Worker and Assistant (level 2).
The recommended collaboration mode
How the work should be split between you and the tool. Centaur mode is a clear division of labour: you handle judgement, and the tool handles a bounded execution task you review. Cyborg mode is tight iteration inside one loop, and it requires a single agent and a clean stopping point. GPT-6.1 Sol takes Centaur mode, with the mode derived from the risk rating and from clause 7.5 on the delivery surface.
Humics
The set of capabilities that are uniquely human, as defined by Pascal Bornet: creativity, critical thinking and social authenticity. The framework asks whether a tool strengthens, leaves neutral or erodes each one.
Humics Protection Badge
A rating of whether a tool protects, leaves neutral or erodes the three human capabilities: Creativity, Critical Thinking and Social Authenticity. Each is scored +1, 0 or -1 and the sum gives the badge. +2 to +3 is Humics-Friendly, -1 to +1 is Humics-Neutral, -2 to -3 is Humics-Risky. GPT-6.1 Sol is Humics-Neutral at -1 / +3.
AI Imposture Risk
The likelihood that a tool traps you in one of three illusions. The Time Illusion is the appearance of saving time when net time is lost. The Quantity Illusion is high volume that does not survive inspection. The Skill Illusion is the appearance of competence while the underlying skill is absent or eroding. Each trap is rated Low, Medium or High with cited evidence, and the overall level is Low when all three are Low and High when two or more are High. GPT-6.1 Sol is Medium overall, with Skill Illusion High.
User Sentiment
The aggregate of what users report about a tool, kept separate from the framework's own findings. No review platform carries model-level data for GPT-6.1 Sol; the signal is two large practitioner threads and a set of launch-week hands-on reviews, all directional.
Review Status
The badge at the top of this review. The vocabulary is: Active, the tool is current and recommended; Active (updated), recently re-checked and refreshed; Changed, a re-check trigger has fired and an update is pending; Risky, the tool has significant unresolved issues or has been clearly surpassed, so use it with caution; Stale, not re-checked in over six months, so pricing and features are unverified; Retired, the tool still works but is no longer recommended; Deprecated, the tool has been shut down or fundamentally changed.
Last tested and Re-check
The date this review's evidence was gathered and the condition that forces a new pass. GPT-6.1 Sol was tested on 2026-10-01 and re-checks on any of the eight triggers listed at the top of this document, and in any case within six months.
GPT-6.1 Sol: OpenAI's near-Astra model at a fifth of the price
Sources
Vendor primary sources
The launch announcement, for the benchmark charts, the pricing lines, the Ultrafast plan and the availability statement: https://openai.com/index/introducing-gpt-6-1-sol/
The model reference, for the effort levels, the endpoint restriction, the tool list, the rate limits and the data-residency notes: https://developers.openai.com/api/docs/models/gpt-6.1-sol
The system card addendum, for the safety evaluations, the deployment-simulation section and the version caveat: https://deploymentsafety.openai.com/gpt-6-1-sol
The API pricing page, for the rate card and the long-context threshold: https://developers.openai.com/api/docs/pricing
The GPT-6 Sol and Luna launch page, for the predecessor's rate card and its own comparison set: https://openai.com/index/introducing-gpt-6-sol-and-luna/
The GPT-6 Astra launch page, for the family positioning and the frontier claim: https://openai.com/index/gpt-6-astra/
Codex local memories documentation, covering the local memory store and its settings: https://learn.chatgpt.com/docs/customization/memories
Codex subagents documentation, covering parallel agents and their configuration: https://learn.chatgpt.com/docs/agent-configuration/subagents
Amazon Bedrock model card, for Regions, the inference profile and the API surface: https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-6-1-sol.html
OpenAI status page: https://status.openai.com
Independent sources
Artificial Analysis, GPT-6.1 Sol replaces GPT-6 Sol after just 7 days, with near-Astra intelligence: the intelligence, cost, token-efficiency and coding-agent measurements this review relies on: https://artificialanalysis.ai/articles/gpt-6-1-sol-replaces-gpt-6-sol-after-just-7-days-with-near-astra-intelligence
Artificial Analysis, GPT-6.1 Sol release intelligence, performance and price: the per-effort-level figures: https://artificialanalysis.ai/models/releases/gpt-6-1-sol
Vellum, GPT-6.1 Sol Benchmarks Explained: a transcription of the vendor charts with per-task costs, used to corroborate the vendor rows: https://www.vellum.ai/blog/gpt-6-1-sol-benchmarks-explained
TechCrunch, OpenAI launches GPT-6.1 Sol, says it nearly matches GPT-6 Astra and costs less: launch coverage including the withheld sibling: https://techcrunch.com/2026/09/29/openai-launches-gpt-6-1-sol-says-it-nearly-matches-gpt-6-astra-and-costs-less/
Gizmodo, OpenAI Cancels Release of GPT-6.1 Astra Because It Regressed on Safety: the withheld sibling, quoting the vendor's safety lead and the Wall Street Journal reporting: https://gizmodo.com/openai-cancels-release-of-gpt-6-1-astra-because-it-regressed-on-safety-2000818566
AWS, Bring near-Astra intelligence to everyday work with GPT-6.1 Sol on Amazon Bedrock: the deployment and isolation model for AWS-hosted use: https://aws.amazon.com/blogs/machine-learning/bring-near-astra-intelligence-to-everyday-work-with-gpt-6-1-sol-on-amazon-bedrock/
Anthropic pricing pages for the comparison section, Claude Opus 5.5 and Claude Sonnet 5.5: https://www.anthropic.com/claude-opus-5-5 and https://platform.claude.com/docs/en/models/sonnet-5-5/overview
Community and community-reported evidence
Hacker News, the GPT-6.1 Sol launch thread, 1,056 points and 935 comments, read directly on 2026-10-01: https://news.ycombinator.com/item?id=49896586
Hacker News, the independent benchmark discussion, 80 points and 98 comments, read on 2026-10-01: https://news.ycombinator.com/item?id=49906669
Reddit, r/OpenAI and r/singularity discussions of the release and its benchmark results, read at platform level through their publication: https://www.reddit.com/r/OpenAI/ and https://www.reddit.com/r/singularity/
The hands-on video reviews listed in the Learn More section above, each linked to its own resolved video page
Review platform sources
No software-review directory carries a corpus for an individual language model. The platforms that rate AI products, including G2, Capterra and Trustpilot, rate the ChatGPT product or OpenAI the company, and none publishes a model-level figure for GPT-6.1 Sol. This review states that rather than inventing a rating.
Framework and method
The U365 CI-First Evaluation Framework, version 1.2, which is the scoring method used here. It sets the benefit dimensions, the Humics protection rating, the AI Imposture risk assessment and the collaboration modes applied in this review, including the three v1.2 clauses assessed in the clause note: https://www.university-365.com/ci-first
The U365 INSIDE Tools review template, which sets the structure of this post and the tool-type variants applied in it: https://www.university-365.com/tools
Published U365 INSIDE Tools reviews, read as comparisons and linked where they are named in this post, including the GPT-6 Sol, GPT-6 Astra and Claude Opus 5.5 reviews cited in the comparison section: https://www.university-365.com/tools
GPT-6.1 Sol: OpenAI's near-Astra model at a fifth of the price
Faculty Note on Evidence Quality
Five things should be said plainly about the evidence behind this review, because they change how much weight a reader should put on each part of it.
First, the release is two days old and the independent picture is one organisation deep. The composite this review relies on comes from a single measurement house, Artificial Analysis, and its index is one composite among several defensible ways to weight tasks. The vendor's own rows are the vendor's, measured in its research environment. Where the two agree, the review says so; where only one exists, it says that too. The comparison row this review leans on most heavily, one point between this model and Astra on the composite, is exactly the kind of gap that a re-run or an index revision can move, which is why the fifth re-check trigger exists.
Second, the intra-vendor comparison rows carry a build-version caveat the vendor itself publishes. The system card states that comparison values for previously launched models "may reflect later versions of those models, and may vary from the values published at launch". That note is rare and useful, and it means a regression against GPT-5.6 Sol in the agentic cybersecurity section may be a comparison against a revision rather than against the launch build. The review reports the vendor's own sentence rather than resolving it, because the vendor is the only party who can.
Third, the community record is a launch-week signal and nothing more. Two large threads and a set of hands-on videos published within 48 hours of release measure the launch as much as the model. The sharpest, most repeated criticism in the record is about the release cadence rather than about the model, and that is a fact about the vendor's behaviour with real consequences, not a defect a score can carry. This review uses the record for what it is and does not aggregate it into a rating.
Fourth, the vendor's most valuable disclosures are the ones that qualify its own claims. OpenAI states that Astra holds the top score on the scientific benchmark and should still be used for the hardest work; that its adversarial evaluations do not measure typical-usage failure rates; and that its comparison values may reflect later model revisions. Each of those sentences narrows the launch's own headline, and all three are published by the vendor. A reader who wants to understand this release rather than the marketing around it should read the footnotes first.
Fifth, one favourable finding is stated as prominently as the adverse ones. The safety record on tool honesty is a genuine improvement: the failure to disclose a broken search tool fell from 4.9 percent to 2.1 percent, the model recorded no attempts to bypass the automated safety reviewer, and the agentic environment protections apply the same safeguards stack as the flagship. Those facts are why this review's overall judgement is positive, and they sit in the same sections as the regression and the withheld sibling rather than in a separate list.
Review conducted by URC under the CI-First Evaluation Framework, version 1.2. Scoring date 2026-10-01. Tool version reviewed: GPT-6.1 Sol (`gpt-6.1-sol`, released 2026-09-29). Framework version applied: 1.2.
GPT-6.1 Sol: OpenAI's near-Astra model at a fifth of the price
Status and Re-check
Status: Active | Last tested: 2026-10-01 (GPT-6.1 Sol, `gpt-6.1-sol`, released 2026-09-29) | Re-check: trigger-based (max 6 months)
Active: the tool is current and recommended.
For detailed explanations of the CI-First evaluation terms used in this review, including the Humics Protection Badge and the AI Imposture Risk levels, see the Glossary at the end of this post.
Re-check triggers:
The arrival of GPT-6.1 Sol Ultrafast. OpenAI states that an Ultrafast tier, with up to eight times faster token generation than standard speed in Codex, arrives in the coming days. If it ships, the Getting Started advice and the Time sub-score both need a fresh pass.
A change to the price or the cache mechanics. The standard rates are unchanged from GPT-6 Sol at $2 and $10 per million tokens, and the movement in this release is the cache read falling from $0.20 to $0.10 per million. A further change to either line, or to the 272,000-token long-context threshold, changes the bill arithmetic this review relies on.
The resolution of GPT-6.1 Astra. OpenAI withheld that model days before this launch over safety findings. GPT-6.1 Sol is the sibling that shipped in its place. If GPT-6.1 Astra ships, is cancelled outright, or the two lines merge, the governance section and the family positioning in this review both need a new pass.
Any independent measurement of the agentic-safety findings. The vendor's own system card reports modest cybersecurity regressions against GPT-5.6 Sol in synthetic and semi-synthetic agentic environments, and records 28 deployment-simulation flags at severity three or above. Any independent security evaluation, or any public incident involving this model in an agentic pipeline, belongs in a re-scoring pass.
A new revision of the independent composite. Artificial Analysis scored GPT-6.1 Sol one point below GPT-6 Astra on Intelligence Index v4.3.2. That organisation has revised its index before, and a movement of two points in either direction would change the near-Astra framing this review reports rather than asserts.
A controlled head-to-head on cost per completed task. No one has yet run GPT-6.1 Sol, Claude Opus 5.5 and Claude Sonnet 5.5 inside one evaluation setup on cost per completed job. All three launched within eight days of each other, and the first controlled comparison settles a question this review can only report as open.
A change to the Codex memory defaults or the subagent defaults. Both framework clause findings in this review rest on the delivery surface: local Codex memories ship off by default, and current Codex releases enable subagent workflows by default. A change to either default moves the clause note and the Skill Illusion rating.
Any further model in the 6.x line. GPT-6 Sol arrived on 2026-09-22 and GPT-6.1 Sol replaced it seven days later. If the vendor keeps a weekly cadence, the family table in this review goes stale faster than the six-month window normally allows, and the next release is a re-check on its own.
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