Model comparison
MiniMax-M2 vs Mistral Large
MiniMax-M2 is the stronger model overall, scoring 37.4 to 31.9 on the Noometry Index.
Last verified . 15 shared benchmarks.
MiniMax
Mistral AISummary
- They share 15 benchmarks with published results for both. MiniMax-M2 scores higher in 8 categories and Mistral Large in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where MiniMax-M2 leads 37.3 to 18.2.
- MiniMax-M2 is cheaper at $0.30 / $1.20 per million input/output tokens, against $2 / $6 for Mistral Large.
- MiniMax-M2 accepts more context: 205K tokens versus 131K.
Side by side
| MiniMax-M2 | Mistral Large | |
|---|---|---|
| Provider | MiniMax | Mistral AI |
| Noometry Index | 37.4 | 31.9 |
| Released | 2025-10-27 | 2024-02-26 |
| Weights | Open | Open |
| Context window | 205K | 131K |
| Max output | 131K | 16K |
| Input $ / M tokens | $0.30 | $2 |
| Output $ / M tokens | $1.20 | $6 |
| Results tracked | 21 | 51 |
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Category by category
Coding MiniMax-M2 leads
MiniMax-M2: 39.3 (#159), Mistral Large: 34.3 (#240)
| Benchmark | MiniMax-M2 | Mistral Large |
|---|---|---|
| LMArena Coding | 1370 | 1277 |
| SWE-bench Verified (bash only) | 61% | — |
| LMArena WebDev | 1297 | — |
| SciCode | — | 36.2% |
| BigCodeBench Instruct | — | 30% |
| LiveBench Coding | — | 47.1% |
| BigCodeBench Complete | — | 38.3% |
| ALE-Bench | — | 264.7 |
| HumanEval+ | — | 62.2% |
| MBPP+ | — | 59.5% |
Agentic & Tool Use Mistral Large leads
MiniMax-M2: 25.1 (#109), Mistral Large: 28.6 (#89)
| Benchmark | MiniMax-M2 | Mistral Large |
|---|---|---|
| Terminal-Bench | 30% | — |
| Berkeley Function Calling Leaderboard | — | 38.4% |
| Vending-Bench 2 | 160.6 | — |
Reasoning MiniMax-M2 leads
MiniMax-M2: 19.4 (#258), Mistral Large: 15.8 (#310)
| Benchmark | MiniMax-M2 | Mistral Large |
|---|---|---|
| LMArena Hard Prompts | 1357 | 1257 |
| SimpleBench | — | 22.5% |
| Kagi LLM Benchmark | 57.8% | — |
| NYT Connections (extended) | 14.8% | — |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 43.5% |
| DTBench | — | 65.1% |
| LiveBench Data Analysis | — | 50.1% |
| LMCA | — | 16.7% |
| Epoch Capabilities Index | — | 128.52 |
| ForecastBench | — | 57.1 |
| LiveBench | — | 48.4% |
Math MiniMax-M2 leads
MiniMax-M2: 37.3 (#160), Mistral Large: 18.2 (#291)
| Benchmark | MiniMax-M2 | Mistral Large |
|---|---|---|
| LMArena Math | 1352 | 1262 |
| OTIS Mock AIME 2024-2025 | — | 8.5% |
| Omni-MATH | — | 28.1% |
| LiveBench Math | — | 42.5% |
| MATH Level 5 | — | 50.3% |
| FrontierMath (Feb 2025 set) | — | 0.3% |
Knowledge MiniMax-M2 leads
MiniMax-M2: 37.0 (#163), Mistral Large: 30.1 (#230)
| Benchmark | MiniMax-M2 | Mistral Large |
|---|---|---|
| LMArena Expert | 1337 | 1232 |
| GPQA Diamond | — | 51.3% |
| MMLU-Pro | — | 59.9% |
| Confabulations | — | 21.4% |
| Vectara Hallucination Rate | — | 4.5% |
| GPQA (HELM) | — | 43.5% |
| MMLU | — | 80% |
Multilingual MiniMax-M2 leads
MiniMax-M2: 45.3 (#171), Mistral Large: 40.0 (#219)
| Benchmark | MiniMax-M2 | Mistral Large |
|---|---|---|
| LMArena Non-English | 1313 | 1237 |
| LMArena Chinese | 1366 | 1240 |
| LMArena French | 1335 | 1325 |
| LMArena German | 1355 | 1254 |
| LMArena Russian | 1331 | 1257 |
| LMArena Spanish | 1326 | 1268 |
| LMArena Japanese | — | 1188 |
| LMArena Korean | — | 1202 |
Instruction Following MiniMax-M2 leads
MiniMax-M2: 70.2 (#166), Mistral Large: 67.9 (#191)
| Benchmark | MiniMax-M2 | Mistral Large |
|---|---|---|
| LMArena Instruction Following | 1328 | 1249 |
| LiveBench Instruction Following | — | 67.9% |
| IFEval | — | 87.7% |
Long Context MiniMax-M2 leads
MiniMax-M2: 40.5 (#153), Mistral Large: 38.3 (#199)
| Benchmark | MiniMax-M2 | Mistral Large |
|---|---|---|
| LMArena Longer Query | 1331 | 1261 |
Writing & Preference MiniMax-M2 leads
MiniMax-M2: 53.0 (#162), Mistral Large: 40.7 (#242)
| Benchmark | MiniMax-M2 | Mistral Large |
|---|---|---|
| LMArena Text | 1340 | 1266 |
| LMArena Creative Writing | 1286 | 1243 |
| LMArena Multi-Turn | 1361 | 1260 |
| Short-Story Creative Writing | — | 69% |
| EQ-Bench Creative Writing | — | 985 |
| WildBench | — | 80.1% |
| LiveBench Language | — | 39.4% |
Frequently asked questions
Is MiniMax-M2 better than Mistral Large?
MiniMax-M2 is the stronger model overall, scoring 37.4 to 31.9 on the Noometry Index.
Which is cheaper, MiniMax-M2 or Mistral Large?
MiniMax-M2 is cheaper. It lists at $0.30 per million input tokens and $1.20 per million output tokens; Mistral Large lists at $2 and $6.
Is MiniMax-M2 or Mistral Large better for coding?
MiniMax-M2 scores higher on coding benchmarks: 39.3 versus 34.3 in the Noometry coding category.
Which has the bigger context window?
MiniMax-M2 does, with 205K tokens against 131K.
How many benchmarks do MiniMax-M2 and Mistral Large share?
15 benchmarks have published results for both models. MiniMax-M2 has 21 scored results on Noometry and Mistral Large has 51.