How to Pick the Right OpenAI Model Without the Headache in May 2025 ?
- Alick Mouriesse
- Apr 26
- 6 min read
Updated: May 8
Confused by GPT-4o, 4.1, 4.5, o3 & friends? This lecture shows you exactly which model to choose for every task in May 2025, if you decide to use OpenAI LLMs via ChatGPT and/or Playground.
Introduction
At University 365 we live by “Become Superhuman, All Year Long.” The first step to superhuman productivity is matching the right Large Language Model (LLM) to the right cognitive load—just as UNOP aligns study methods to brain states. Today’s LLM landscape looks like alphabet soup on YouTube; influencers disagree, prices shift overnight, and OpenAI keeps shipping.
Honestly, it’s really not easy to navigate through this, and our experience shows that way too many users make mistakes, choosing the wrong model for the wrong questions or problems and, guess what? They get bad answers, obviously.
This micro-lecture untangles the mess so beginners can choose confidently, slash costs, and unlock agent-level results.
The 2025 OpenAI Model Zoo: Two Families, Two Mindsets
Family | Mindset | Flagships (2025) | Best For | Costs |
GPT-4 series | World-model depth & intuition | 4o → 4.1 | rich conversation, writing, long reads | mid |
o-series | Deliberate reasoning & tool use | o3 → o4-mini | chain-of-thought, STEM, code, agent flows | low–mid |
Try-it-Now: In Playground, ask both “How many distinct colors are on a Rubik’s Cube?” Watch o3 chain through vision reasoning, then note 4o’s concise answer.
Key takeaways
"GPT-4" Family models chase breadth; "o-series" Family models chase depth.
Every major OpenAI release now lands in one of those tracks.
Choose based on thinking pattern your task demands, not buzz-level.
Always check the model that will be used by ChatGPT (https://chatgpt.com) or OpenAI's Playground (https://platform.openai.com/) before asking your question. Do not leave the default chosen model without deciding on the best model to use based on your question and the type of task you want to be performed. For more information, we recommend to read our comprehensive analysis and test of OpenAI models.
Meet the Players
OpenAI "GPT-4" Family
GPT-4o (default ChatGPT) – the All-Rounder
Natively multimodal; latency ~ 1/3 of GPT-4; cheaper token pricing.
Continues to absorb incremental improvements (March & April releases).
Use when: you need images + text, solid code help (but only help), fast conversations.
Mini-exercise: Ask 4o to describe a meme image you drag-and-drop.
GPT-4.5 – the Maxed-Out Preview
Largest unsupervised model; “EQ” & writing flair; $75 / M input tokens.
Being sunset July 14 as 4.1 outperforms it cheaper.
Use when: you’re on legacy code waiting to migrate—otherwise skip. Our Smart Advise = It's almost dead, FORGET-IT !!!!! “Largest unsupervised model” means GPT-4.5 was trained on the biggest raw data set OpenAI has used so far without human-curated instruction tuning or reinforcement learning steps. In other words, it learned purely from vast amounts of text, giving it an especially broad knowledge base—but also making it heavier, costlier, and less strategically aligned than later, instruction-tuned models like 4.1. “EQ & writing flair" means GPT-4.5 tends to generate text with higher “emotional intelligence” (empathetic, tone-aware responses) and a more polished, creative writing style—hence “EQ” (emotional quotient) and “writing flair.”
GPT-4.1 – the Context Titan
1 M-token window; +21 pp coding jump over 4o; 10 % better instruction following.
Three SKUs: main, mini, nano (nano = fastest & cheapest > 3.5-turbo).
Use when: reading whole doc vaults, writing books, autonomous agents. "+21 pp coding jump over 4o" means GPT-4.1 solves coding benchmarks 21 percentage points better than GPT-4o—for example, if 4o answered 60 % of test problems correctly, 4.1 scores about 81 %. SKU stands for Stock-Keeping Unit—a product-catalog term that denotes a distinct version or configuration of an item. In OpenAI’s context, each “SKU” (main, mini, nano) is a separate GPT-4.1 variant with its own performance, context window, and pricing tier.
Mini-exercise: Feed 100 k-token PDF and ask 4.1-mini to summarise each section in one sentence.
OpenAI "o-series" Family
OpenAI o3 – the Reasoning Sledgehammer that can use Tools
New SOTA on Codeforces, SWE-bench; 20 % fewer major errors than o1.
Full ChatGPT tool orchestration (search + python + vision + image-gen).
Use when: multi-step analysis (finance models, lab data, advanced coding). “New SOTA on Codeforces” means the o3 model has achieved State-Of-The-Art (record-setting) performance on tasks from Codeforces, a popular competitive-programming benchmark. In other words, it now scores higher than any previous model on those coding challenges. SWE-bench is a software-engineering benchmark: it gives the model a real GitHub bug report plus the project’s codebase and asks it to produce the exact code change that fixes the bug—so higher scores mean better, end-to-end bug-fixing skill.
OpenAI o4-mini & o4-mini-high – the Budget Ninjas
Optimised for throughput; beats o3-mini on non-STEM too; “-high” dials more thinking steps.
Best pass@1 on AIME 2025 with Python tool.
Use when: batch Q&A, customer-support triage, classroom autograding.
Key takeaways
Smartest ≠ best for you: latency, price, context, and tool usage decide.
The API lets you hot-swap models; design with abstraction.
Keep an eye on deprecations (GPT-4 end-of-life Apr 30; 4.5 preview July 14).
The U365 Decision Matrix
Define Output Form (text, code, image, data frame).
Estimate Cognitive Depth
Quick factual ↔ templated → 4o mini / o4-mini
Multi-step reasoning, STEM, tool chaining → o3 / o4-mini-high
Vast context or book-length summarising → 4.1 or 4.1 mini
Check Budget & Latency (see API pricing page).(OpenAI)
Prototype in Playground – time a few calls; compare token counts.
Lock-in & Monitor – schedule quarterly reviews—models evolve!
Mini-exercise: Build a spreadsheet with 10 daily tasks; map each to a model using the 5 rules above.
Key takeaways
Decision matrices cut YouTube noise; data beats opinions.
Always benchmark on your workload—OpenAI even encourages this.
UNOP principle: reduce cognitive load by standardising choices.
Scenario Playbook - Examples
Scenario | Recommended Model | Why? |
Daily brainstorming, social captions | 4o | balanced creativity + cost |
50 k customer-support emails nightly | o4-mini-high with Flex processing | cheapest asynchronous pipeline(TechCrunch) |
Full-text legal discovery (300 k tokens) | GPT-4.1 main | 1 M context, reliable retrieval |
Advanced math tutoring video + code | o3 | vision + python tools |
Long-form novel outline | 4.1 mini | huge context at lower price |
Try-it-Now*: Deploy two parallel API calls (o3 vs 4.1) on the same 5-step coding challenge and compare runtime + cost.
UNOP Hacks for Model Mastery
Pomodoro pairing: Deep-work pomodoro with o3 ensures your brain mirrors the model’s deliberate chain-of-thought.
Mind-mapping prompts: Before a 4.1 context marathon, mind-map sections so the model can anchor chunks.
LIPS lesson logs: Store prompt-chain experiments in your Digital Second Brain; CARE-review weekly to track token spend trends.
Conclusion
Picking an LLM in 2025 is less about “smartest” and more about situational fit. GPT-4o remains a solid default, but o3 can out-reason it, and 4.1 crushes long-context jobs. Use the Decision Matrix, benchmark briefly, and you’ll move from confused consumer to U365-style Superhuman.
Interactive Q&A
Q: Can I just switch every ChatGPT conversation to o3? A: Not yet—o3 is API‑only (April 2025) and costs more tokens per step than 4o; use it when you need its deeper reasoning.
Q: Will GPT‑4.5 stick around for my legacy app? A: The preview API is scheduled to shut down July 14 2025; migrate to 4.1 or 4o mini before then.
Q: Is 4.1 always better than 4o? A: For coding and 1 M‑token tasks, yes; For real‑time chat with images, 4o still wins on latency and multimodal polish.
Q: I run nightly batch jobs—should I pick o4‑mini‑high or 4o mini to save money? A: For large asynchronous workloads, o4‑mini‑high is ~30‑40 % cheaper per successful token and scales better under Flex processing; choose 4o mini only when lower latency matters.
Q: Is any model safer for sending sensitive data (like PII)? A: All OpenAI models share the same SOC 2–compliant security layer; model choice doesn’t change policy. For extra control, deploy 4.1 or o3 through Azure OpenAI or encrypt data client‑side before sending.
References
OpenAI. (2025, Apr 16). Introducing OpenAI o3 and o4-mini.(OpenAI)
OpenAI. (2025, Apr 14). Introducing GPT-4.1 in the API.(OpenAI)
OpenAI. (2025, Feb 27). Introducing GPT-4.5 (Research Preview).(OpenAI)
OpenAI Help Center. (2025, Apr 10). Sunsetting GPT-4 in ChatGPT.(OpenAI Help Center)
OpenAI API. (2025). Pricing overview.(OpenAI)TechCrunch. (2025, Apr 17).
OpenAI launches Flex processing for cheaper, slower AI tasks.(TechCrunch)TechCrunch. (2025, Apr 11).
OpenAI will phase out GPT-4 from ChatGPT.(TechCrunch)
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