OpenAI sells a ladder of models, not one model. In ChatGPT the picker shows a handful of options and the app often auto-routes for you. In the API you choose explicitly and pay per token. Either way, the choice matters: the gap between the cheapest and the most expensive model in the current line-up is well over a hundred times on output price.
The tiers, in plain language
- Flagship (GPT-5.5) — the strongest general model. Use it for hard reasoning, long multi-step work, and anything where a wrong answer is expensive.
- Standard (GPT-5.4, GPT-5.6 Sol / Terra, GPT-4.1) — the daily driver. Good enough for most writing, analysis and coding at a fraction of flagship output price.
- Mini (GPT-5.4 mini, GPT-5 Mini, GPT-4.1 Mini) — fast and cheap. Summaries, classification, extraction, first drafts, chat features in your own app.
- Nano (GPT-5.4 Nano, GPT-5 Nano, GPT-4.1 Nano, GPT-5.6 Luna) — the cheapest hosted tier. High-volume, low-stakes jobs: tagging, routing, simple rewrites.
- Pro (GPT-5.5 Pro, GPT-5.4 Pro) — the flagship allowed to think much longer. Priced far above everything else; reserve for problems you would otherwise pay a specialist to solve.
- Open-weight (gpt-oss-120b, gpt-oss-20b) — models OpenAI released for anyone to host. Available through the API-compatible catalogue at very low prices, or run on your own hardware.
- Reasoning line (o3, o4 Mini) — older dedicated reasoning models. Still listed; the GPT-5.x line has largely absorbed their role.
| Model | Input $/1M | Output $/1M | Context window | Pick it for |
|---|---|---|---|---|
| GPT-5.5 | 5.00 | 30.00 | 1,050,000 | Hardest reasoning, high-stakes output |
| GPT-5.4 | 2.50 | 15.00 | 1,050,000 | Everyday professional work |
| GPT-5.6 Sol | 2.00 | 10.00 | 1,050,000 | Standard-tier alternative |
| GPT-4.1 | 2.00 | 8.00 | 1,047,576 | Long-document work on a budget |
| GPT-5.4 mini | 0.75 | 4.50 | 400,000 | Summaries, extraction, app features |
| GPT-5 Mini | 0.25 | 2.00 | 400,000 | Cheap general tasks |
| GPT-5.4 Nano | 0.20 | 1.25 | 400,000 | High-volume classification |
| GPT-5 Nano | 0.05 | 0.40 | 400,000 | Cheapest hosted OpenAI text model |
| gpt-oss-120b | 0.037 | 0.17 | 131,072 | Open-weight, bulk jobs, self-hosting |
Context windows: what the number means
The context window is the maximum amount of text (prompt plus reply) a model can hold at once, measured in tokens. A token is roughly three-quarters of an English word. A 400,000-token window fits a few novels; the 1,050,000-token window on GPT-5.4 and GPT-5.5 fits a small codebase or a year of meeting transcripts.
Bigger is not free. Every token in the window is billed as input on every turn, and models get slower and sometimes less accurate as the window fills. The practical rule: paste what the model needs, not everything you have. Lesson 7 covers the token maths in detail.
Inside ChatGPT you do not see per-token prices; you see a model picker and usage limits per plan. The lesson still applies. Picking a lighter model inside ChatGPT stretches your plan's limits, and picking a heavier one burns through them faster. Compare the full catalogue, including other providers, on /models.
Knowledge check
You need to tag 50,000 support tickets by topic. Which OpenAI tier is the sensible starting point?