Head to head
o4-mini vs GPT-5 mini
o4-mini from OpenAI against GPT-5 mini from OpenAI — specification, price and every benchmark both makers have published, in one table.
Benchmarks
GPT-5 mini leads
GPT-5 mini wins 2 of the 3 benchmarks both models report, o4-mini wins 1, though the gaps are small. The average gap across shared tests is 1.8 points.
Price
GPT-5 mini is cheaper
On a 3:1 input-to-output mix, GPT-5 mini costs $0.688 per million tokens against $1.93 for o4-mini — about 2.8× less. Remember that a reasoning model bills its thinking as output, so cost per answer can diverge much further than cost per token.
What actually differs
- GPT-5 mini takes 400K tokens of context against 200K — 2.0× more room for long documents or a large codebase.
Scorecard
Which is better at what
Maths, coding, reasoning and the rest — one line each, averaged over the benchmarks both models actually report.
| Category | o4-mini | GPT-5 mini | Better at this |
|---|---|---|---|
| ReasoningGPQA Diamond | 81.4% | 82.3% | GPT-5 mini+0.9 |
| MathsAIME 2025 | 92.7% | 91.1% | o4-mini+1.6 |
| CodingSWE-bench Verified | 68.1% | 71.0% | GPT-5 mini+2.9 |
| KnowledgeNeither model reports this | — | — | Not comparable |
| MultimodalOnly one model reports this | — | — | Not comparable |
| Instruction followingNeither model reports this | — | — | Not comparable |
| Human preferenceNeither model reports this | — | — | Not comparable |
| Categories wonOut of 3 comparable | 1 | 2 | GPT-5 mini |
Each category averages only the benchmarks every model here reports, so no one is credited for a test the other did not run. A category with no shared test is marked Not comparable rather than guessed at.
Side by side
o4-mini and GPT-5 mini, row by row
| Attribute | o4-miniOpenAI | GPT-5 miniOpenAI |
|---|---|---|
| Specification | ||
| MakerWho built it | OpenAI | OpenAI |
| Released | 2025-04 | 2025-08 |
| Architecture | Not reported | MoE |
| Context windowHow much can go in at once | 200,000 tokens | 400,000 tokens |
| Max output | 100,000 tokens | 128,000 tokens |
| Input | Text, Image | Text, Image |
| ReasoningSpends extra tokens thinking before it answers | Yes | Yes |
| Tool calling | Yes | Yes |
| Knowledge cutoff | 2024-06 | 2024-05 |
| Licence | Proprietary | Proprietary |
| Open weightsCan you download and run it yourself | No | No |
| Price | ||
| Input priceUSD per million tokens in | $1.10 | $0.25 |
| Output priceUSD per million tokens out | $4.40 | $2.00 |
| Cached input | $0.275 | $0.025 |
| Blended 3:1A 3-in-to-1-out million-token mix — a fairer single number than input price alone | $1.93 | $0.688Cheapest |
| Published benchmarks | ||
| GPQA Diamond198 graduate-level physics, chemistry and biology questions written to be Google-proof. PhD holders in the matching field score about 65%. | ||
| AIME 2025The American Invitational Mathematics Examination — 15 problems, integer answers, no partial credit. A standard test of multi-step maths reasoning. | ||
| SWE-bench Verified500 human-validated GitHub issues from real Python repositories. The model must produce a patch that makes the project's own tests pass. | ||
| MMMUCollege-level questions that require reading charts, diagrams, tables and photographs alongside the text. | Not reported | |
| Links | Full page | Full page |
| Row verified | 2026-08 | 2026-08 |
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.
Questions
o4-mini or GPT-5 mini?
Is o4-mini better than GPT-5 mini?
GPT-5 mini wins 2 of the 3 benchmarks both models report, o4-mini wins 1, though the gaps are small. The average gap across shared tests is 1.8 points.
Which is cheaper, o4-mini or GPT-5 mini?
On a 3:1 input-to-output mix, GPT-5 mini costs $0.688 per million tokens against $1.93 for o4-mini — about 2.8× less. Remember that a reasoning model bills its thinking as output, so cost per answer can diverge much further than cost per token.
What is the difference between o4-mini and GPT-5 mini?
GPT-5 mini takes 400K tokens of context against 200K — 2.0× more room for long documents or a large codebase.
