Head to head
MiniMax-M2 vs GLM-4.6
MiniMax-M2 from MiniMax against GLM-4.6 from Z.ai (Zhipu) — specification, price and every benchmark both makers have published, in one table.
Benchmarks
GLM-4.6 leads
GLM-4.6 wins 2 of the 3 benchmarks both models report, MiniMax-M2 wins 1, consistently. The average gap across shared tests is 7.4 points.
Price
MiniMax-M2 is cheaper
On a 3:1 input-to-output mix, MiniMax-M2 costs $0.525 per million tokens against $1.00 for GLM-4.6 — about 1.9× 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
- 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.
| Category | MiniMax-M2 | GLM-4.6 | Better at this |
|---|---|---|---|
| ReasoningGPQA Diamond | 78.0% | 82.9% | GLM-4.6+4.9 |
| MathsAIME 2025 | 78.0% | 93.9% | GLM-4.6+15.9 |
| CodingSWE-bench Verified | 69.4% | 68.0% | MiniMax-M2+1.4 |
| 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 3 comparable | 1 | 2 | GLM-4.6 |
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 and GLM-4.6, row by row
| Attribute | MiniMax-M2MiniMax | GLM-4.6Z.ai (Zhipu) |
|---|---|---|
| Specification | ||
| MakerWho built it | MiniMax | Z.ai (Zhipu) |
| Released | 2025-10 | 2025-09 |
| ParametersTotal, and active per token for a mixture of experts | 230B total / 10B active | 357B total / 32B active |
| Architecture | MoE | MoE |
| Context windowHow much can go in at once | 204,800 tokens | 204,800 tokens |
| Max output | 131,072 tokens | 131,072 tokens |
| Input | Text | Text |
| ReasoningSpends extra tokens thinking before it answers | Yes | Yes |
| Tool calling | Yes | Yes |
| Licence | MIT | MIT |
| Open weightsCan you download and run it yourself | Yes | Yes |
| Price | ||
| Input priceUSD per million tokens in | $0.30 | $0.60 |
| Output priceUSD per million tokens out | $1.20 | $2.20 |
| Blended 3:1A 3-in-to-1-out million-token mix — a fairer single number than input price alone | $0.525Cheapest | $1.00 |
| Price note | Open weights — this is a representative hosting rate, not a first-party price. Running it yourself costs only hardware. | 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%. | ||
| 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. | ||
| LiveCodeBenchCompetitive-programming problems collected after each model's training cutoff, so contamination cannot inflate the score. | Not reported | |
| Links | Hugging Face · Full page | Hugging Face · 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
MiniMax-M2 or GLM-4.6?
Is MiniMax-M2 better than GLM-4.6?
GLM-4.6 wins 2 of the 3 benchmarks both models report, MiniMax-M2 wins 1, consistently. The average gap across shared tests is 7.4 points.
Which is cheaper, MiniMax-M2 or GLM-4.6?
On a 3:1 input-to-output mix, MiniMax-M2 costs $0.525 per million tokens against $1.00 for GLM-4.6 — about 1.9× 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 MiniMax-M2 and GLM-4.6?
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.
