o4-mini
by OpenAI · United StatesA small reasoning model that punches far above its price on maths and code.
Specification
The numbers
- Maker
- OpenAI
- Released
- 2025-04
- Parameters
- Not reported
- Architecture
- Not reported
- Context window
- 200,000 tokens
- Max output
- 100,000 tokens
- Input
- Text, Image
- Output
- Text
- Reasoning
- Yes
- Tool calling
- Yes
- Knowledge cutoff
- 2024-06
- Licence
- Proprietary
- Availability
- Not reported
- Weights
- Not released
Cost
Price per million tokens
- Input
- $1.10 / M tokens
- Output
- $4.40 / M tokens
- Cached input
- $0.275
- Blended 3:1
- $1.93
Standard first-party API rate, excluding batch discounts. Reasoning models bill thinking tokens as output, so cost per answer can far exceed the cost per token suggests.
Published scores
Benchmarks
Figures published by OpenAI or taken from a public leaderboard. Row last checked 2026-08.
| Category | Score | Rank |
|---|---|---|
| ReasoningGPQA Diamond | 81.4% | 13 of 83 reporting the same tests |
| MathsAIME 2025 | 92.7% | 7 of 56 reporting the same tests |
| CodingSWE-bench Verified | 68.1% | 16 of 33 reporting the same tests |
| KnowledgeBreadth of factual recall under exam conditions | Not reported | — |
| MultimodalMMMU | 81.6% | 5 of 39 reporting the same tests |
| Instruction followingObeying an exact, checkable format | Not reported | — |
| Human preferenceWhich answer people pick, blind | Not reported | — |
A category averages every benchmark in it that o4-mini reports. The rank counts only models that report the same tests, so it never compares an average over three benchmarks against an average over one.
Every reported test
| MMLU-ProKnowledge | Not reported | No figure published |
|---|---|---|
| GPQA DiamondReasoning | Rank 13 of 83 models reporting | |
| AIME 2025Maths | Rank 7 of 56 models reporting | |
| MATH-500Maths | Not reported | No figure published |
| SWE-bench VerifiedCoding | Rank 16 of 33 models reporting | |
| SWE-bench ProCoding | Not reported | No figure published |
| Terminal-Bench 2.1Coding | Not reported | No figure published |
| Frontier-Bench v0.1Reasoning | Not reported | No figure published |
| Terminal-Bench 4.0Coding | Not reported | No figure published |
| LiveCodeBenchCoding | Not reported | No figure published |
| HumanEvalCoding | Not reported | No figure published |
| MMMUMultimodal | Rank 5 of 39 models reporting | |
| IFEvalInstruction following | Not reported | No figure published |
| LMArena EloHuman preference | Not reported | No figure published |
Where these numbers come from
Every score on this page is a published figure, taken from the model's own card, system card, technical report or release post, or from a public leaderboard. CorX Labs did not run these evaluations. Most are self-reported by the lab that built the model, which means they were produced under that lab's own choice of prompt, scaffold and number of attempts — so treat them as a starting point for a shortlist, not as a settled ranking.
A score someone other than the model's maker measured is marked Independent and names its measurer. Those are the stronger numbers on this page — an outside harness has no reason to flatter anyone — and there are not many of them.
Where a figure has not been published, the cell reads Not reported rather than an estimate. Nothing here is inferred, interpolated or guessed. Each model records the month its row was last checked. Full method and caveats.
Same maker
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