Head to head
MiniMax-M3 vs MiniMax-M2
MiniMax-M3 from MiniMax against MiniMax-M2 from MiniMax — specification, price and every benchmark both makers have published, in one table.
At a glance
Benchmarks
No shared benchmarks
These two models have no benchmark in common with published figures for both, so there is nothing to compare directly. The specification and price rows below are still like for like.
Price
Same price
Both cost $0.525 per million tokens on a 3:1 input-to-output mix.
What actually differs
- MiniMax-M3 takes 1M tokens of context against 205K — 4.9× more room for long documents or a large codebase.
- Only MiniMax-M3 reads images. If your input includes screenshots, charts or documents, that decides it.
Scorecard
Which is better at what
Maths, coding, reasoning and the rest — one line each, averaged over the benchmarks both models actually report.
| Category | MiniMax-M3 | MiniMax-M2 | Better at this |
|---|---|---|---|
| ReasoningOnly one model reports this | — | — | Not comparable |
| MathsOnly one model reports this | — | — | Not comparable |
| CodingOnly one model reports this | — | — | Not comparable |
| KnowledgeNeither model reports this | — | — | Not comparable |
| MultimodalNeither model reports this | — | — | Not comparable |
| Instruction followingNeither model reports this | — | — | Not comparable |
| Human preferenceNeither model reports this | — | — | Not comparable |
| Categories won | No category has a test both models report | ||
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
MiniMax-M3 and MiniMax-M2, row by row
| Attribute | MiniMax-M3MiniMax | MiniMax-M2MiniMax |
|---|---|---|
| Specification | ||
| MakerWho built it | MiniMax | MiniMax |
| Released | 2026-06 | 2025-10 |
| ParametersTotal, and active per token for a mixture of experts | 428B total / 23B active | 230B total / 10B active |
| Architecture | MoE | MoE |
| Context windowHow much can go in at once | 1,000,000 tokens | 204,800 tokens |
| Max output | Not reported | 131,072 tokens |
| Input | Text, Image | Text |
| ReasoningSpends extra tokens thinking before it answers | Not reported | Yes |
| Tool calling | Yes | Yes |
| Licence | MiniMax Community License | MIT |
| Open weightsCan you download and run it yourself | Yes | Yes |
| Price | ||
| Input priceUSD per million tokens in | $0.30 | $0.30 |
| Output priceUSD per million tokens out | $1.20 | $1.20 |
| Blended 3:1A 3-in-to-1-out million-token mix — a fairer single number than input price alone | $0.525 | $0.525 |
| Price note | First-party API rate. | Open weights — this is a representative hosting rate, not a first-party price. Running it yourself costs only hardware. |
| 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%. | Not reported | |
| AIME 2025The American Invitational Mathematics Examination — 15 problems, integer answers, no partial credit. A standard test of multi-step maths reasoning. | Not reported | |
| 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. | Not reported | |
| SWE-bench ProA harder, contamination-resistant successor to SWE-bench Verified, drawn from commercial and copyleft repositories that were never public training data. | Not reported | |
| Terminal-Bench 2.1The 2.1 revision of the terminal agent benchmark. Scores on it are not comparable with Terminal-Bench 2.0 or 4.0 — the task set changed. | Not reported | |
| Links | Full page | Hugging Face · Full page |
| Row verified | 2026-10 | 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
MiniMax-M3 or MiniMax-M2?
Is MiniMax-M3 better than MiniMax-M2?
These two models have no benchmark in common with published figures for both, so there is nothing to compare directly. The specification and price rows below are still like for like.
Which is cheaper, MiniMax-M3 or MiniMax-M2?
Both cost $0.525 per million tokens on a 3:1 input-to-output mix.
What is the difference between MiniMax-M3 and MiniMax-M2?
MiniMax-M3 takes 1M tokens of context against 205K — 4.9× more room for long documents or a large codebase. Only MiniMax-M3 reads images. If your input includes screenshots, charts or documents, that decides it.
Related
