CorX Labs

Head to head

MiniMax-M2.5 vs MiniMax-M2

MiniMax-M2.5 from MiniMax against MiniMax-M2 from MiniMax — specification, price and every benchmark both makers have published, in one table.

At a glance

Benchmarks

MiniMax-M2.5 leads

MiniMax-M2.5 wins 1 of the 1 benchmarks both models report, MiniMax-M2 wins 0, by a wide margin. The average gap across shared tests is 10.8 points.

Price

Not directly comparable

At least one of these does not have a published per-token price, so cost cannot be compared like for like. The open-weight model can be run on your own hardware instead.

What actually differs

  • These two are closely matched on the specification side — same broad capabilities, same licensing posture. The decision comes down to the benchmark rows and the price.

Scorecard

Which is better at what

Maths, coding, reasoning and the rest — one line each, averaged over the benchmarks both models actually report.

Which model scores higher in each capability category, averaged over the benchmarks all of them report.
CategoryMiniMax-M2.5MiniMax-M2Better at this
ReasoningOnly one model reports this——Not comparable
MathsOnly one model reports this——Not comparable
CodingSWE-bench Verified80.2%69.4%MiniMax-M2.5+10.8
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 wonOut of 1 comparable10MiniMax-M2.5

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-M2.5 and MiniMax-M2, row by row

AttributeMiniMax-M2.5MiniMaxMiniMax-M2MiniMax
Specification
MakerWho built itMiniMaxMiniMax
Released2026-022025-10
ParametersTotal, and active per token for a mixture of expertsNot reported230B total / 10B active
ArchitectureNot reportedMoE
Context windowHow much can go in at onceNot reported204,800 tokens
Max outputNot reported131,072 tokens
InputNot reportedText
ReasoningSpends extra tokens thinking before it answersNot reportedYes
Tool callingYesYes
LicenceNot statedMIT
Open weightsCan you download and run it yourselfNot reportedYes
Price
Input priceUSD per million tokens inNot reported$0.30
Output priceUSD per million tokens outNot reported$1.20
Blended 3:1A 3-in-to-1-out million-token mix — a fairer single number than input price aloneNot reported$0.525
Price noteFirst-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 reported78%
AIME 2025The American Invitational Mathematics Examination — 15 problems, integer answers, no partial credit. A standard test of multi-step maths reasoning.Not reported78%
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.80.2%Best69.4%
LinksFull pageHugging Face · Full page
Row verified2026-102026-08

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-M2.5 or MiniMax-M2?

Is MiniMax-M2.5 better than MiniMax-M2?

MiniMax-M2.5 wins 1 of the 1 benchmarks both models report, MiniMax-M2 wins 0, by a wide margin. The average gap across shared tests is 10.8 points.

Which is cheaper, MiniMax-M2.5 or MiniMax-M2?

At least one of these does not have a published per-token price, so cost cannot be compared like for like. The open-weight model can be run on your own hardware instead.

What is the difference between MiniMax-M2.5 and MiniMax-M2?

These two are closely matched on the specification side — same broad capabilities, same licensing posture. The decision comes down to the benchmark rows and the price.