Model comparison
MiniMax-M2.5 vs Mistral Large
MiniMax-M2.5 is the stronger model overall, scoring 38.3 to 31.9 on the Noometry Index.
Last verified . 21 shared benchmarks.
Summary
- They share 21 benchmarks with published results for both. MiniMax-M2.5 scores higher in 8 categories and Mistral Large in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in coding, where MiniMax-M2.5 leads 48.1 to 34.3.
- MiniMax-M2.5 is cheaper at $0.30 / $1.20 per million input/output tokens, against $2 / $6 for Mistral Large.
- MiniMax-M2.5 accepts more context: 205K tokens versus 131K.
Side by side
| MiniMax-M2.5 | Mistral Large | |
|---|---|---|
| Provider | MiniMax | Mistral AI |
| Noometry Index | 38.3 | 31.9 |
| Released | 2026-02-12 | 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 | 33 | 51 |
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Category by category
Coding MiniMax-M2.5 leads
MiniMax-M2.5: 48.1 (#58), Mistral Large: 34.3 (#240)
| Benchmark | MiniMax-M2.5 | Mistral Large |
|---|---|---|
| LMArena Coding | 1381 | 1277 |
| ALE-Bench | 618.17 | 264.7 |
| SWE-bench Verified (bash only) | 75.8% | — |
| LMArena WebDev | 1387 | — |
| SWE-bench Multilingual | 68.3% | — |
| SciCode | — | 36.2% |
| BigCodeBench Instruct | — | 30% |
| LiveBench Coding | — | 47.1% |
| BigCodeBench Complete | — | 38.3% |
| HumanEval+ | — | 62.2% |
| MBPP+ | — | 59.5% |
Agentic & Tool Use MiniMax-M2.5 leads
MiniMax-M2.5: 30.4 (#77), Mistral Large: 28.6 (#89)
| Benchmark | MiniMax-M2.5 | Mistral Large |
|---|---|---|
| Terminal-Bench | 42.7% | — |
| Berkeley Function Calling Leaderboard | — | 38.4% |
| Vending-Bench 2 | -23.16 | — |
Reasoning MiniMax-M2.5 leads
MiniMax-M2.5: 17.5 (#292), Mistral Large: 15.8 (#310)
| Benchmark | MiniMax-M2.5 | Mistral Large |
|---|---|---|
| LMArena Hard Prompts | 1372 | 1257 |
| Epoch Capabilities Index | 146.68 | 128.52 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | — | 22.5% |
| Kagi LLM Benchmark | 55.2% | — |
| NYT Connections (extended) | 16.8% | — |
| ARC-AGI-1 | 63.7% | — |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 43.5% |
| DTBench | — | 65.1% |
| LiveBench Data Analysis | — | 50.1% |
| LMCA | — | 16.7% |
| ForecastBench | — | 57.1 |
| LiveBench | — | 48.4% |
Math MiniMax-M2.5 leads
MiniMax-M2.5: 26.9 (#253), Mistral Large: 18.2 (#291)
| Benchmark | MiniMax-M2.5 | Mistral Large |
|---|---|---|
| LMArena Math | 1378 | 1262 |
| OTIS Mock AIME 2024-2025 | — | 8.5% |
| ProofBench | 4% | — |
| Omni-MATH | — | 28.1% |
| LiveBench Math | — | 42.5% |
| MATH Level 5 | — | 50.3% |
| FrontierMath (Feb 2025 set) | — | 0.3% |
Knowledge MiniMax-M2.5 leads
MiniMax-M2.5: 39.2 (#135), Mistral Large: 30.1 (#230)
| Benchmark | MiniMax-M2.5 | Mistral Large |
|---|---|---|
| Vectara Hallucination Rate | 9.1% | 4.5% |
| LMArena Expert | 1379 | 1232 |
| GPQA Diamond | — | 51.3% |
| MMLU-Pro | — | 59.9% |
| Confabulations | — | 21.4% |
| GPQA (HELM) | — | 43.5% |
| MMLU | — | 80% |
Multilingual MiniMax-M2.5 leads
MiniMax-M2.5: 47.1 (#152), Mistral Large: 40.0 (#219)
| Benchmark | MiniMax-M2.5 | Mistral Large |
|---|---|---|
| LMArena Non-English | 1338 | 1237 |
| LMArena Chinese | 1393 | 1240 |
| LMArena French | 1362 | 1325 |
| LMArena German | 1362 | 1254 |
| LMArena Japanese | 1171 | 1188 |
| LMArena Korean | 1232 | 1202 |
| LMArena Russian | 1358 | 1257 |
| LMArena Spanish | 1354 | 1268 |
Instruction Following MiniMax-M2.5 leads
MiniMax-M2.5: 71.5 (#148), Mistral Large: 67.9 (#191)
| Benchmark | MiniMax-M2.5 | Mistral Large |
|---|---|---|
| LMArena Instruction Following | 1353 | 1249 |
| LiveBench Instruction Following | — | 67.9% |
| IFEval | — | 87.7% |
Long Context Too close to call
MiniMax-M2.5: 37.5 (#216), Mistral Large: 38.3 (#199)
| Benchmark | MiniMax-M2.5 | Mistral Large |
|---|---|---|
| LMArena Longer Query | 1366 | 1261 |
| CL-bench | 11.4% | — |
| CL-bench Life | 6.3% | — |
Writing & Preference MiniMax-M2.5 leads
MiniMax-M2.5: 53.9 (#153), Mistral Large: 40.7 (#242)
| Benchmark | MiniMax-M2.5 | Mistral Large |
|---|---|---|
| LMArena Text | 1359 | 1266 |
| LMArena Creative Writing | 1331 | 1243 |
| EQ-Bench Creative Writing | 1361 | 985 |
| LMArena Multi-Turn | 1364 | 1260 |
| Short-Story Creative Writing | — | 69% |
| WildBench | — | 80.1% |
| LiveBench Language | — | 39.4% |
Frequently asked questions
Is MiniMax-M2.5 better than Mistral Large?
MiniMax-M2.5 is the stronger model overall, scoring 38.3 to 31.9 on the Noometry Index.
Which is cheaper, MiniMax-M2.5 or Mistral Large?
MiniMax-M2.5 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-M2.5 or Mistral Large better for coding?
MiniMax-M2.5 scores higher on coding benchmarks: 48.1 versus 34.3 in the Noometry coding category.
Which has the bigger context window?
MiniMax-M2.5 does, with 205K tokens against 131K.
How many benchmarks do MiniMax-M2.5 and Mistral Large share?
21 benchmarks have published results for both models. MiniMax-M2.5 has 33 scored results on Noometry and Mistral Large has 51.