Model comparison
MiMo-V2-Omni vs Mistral Large
MiMo-V2-Omni is the stronger model overall, scoring 43.6 to 31.9 on the Noometry Index.
Last verified . 17 shared benchmarks.
Mistral AISummary
- They share 17 benchmarks with published results for both. MiMo-V2-Omni scores higher in 8 categories and Mistral Large in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where MiMo-V2-Omni leads 39.1 to 18.2.
- MiMo-V2-Omni is cheaper at $0.14 / $0.28 per million input/output tokens, against $2 / $6 for Mistral Large.
- MiMo-V2-Omni accepts more context: 262K tokens versus 131K.
- Mistral Large has downloadable open weights; the other is API-only.
Side by side
| MiMo-V2-Omni | Mistral Large | |
|---|---|---|
| Provider | Xiaomi | Mistral AI |
| Noometry Index | 43.6 | 31.9 |
| Released | 2026-03-18 | 2024-02-26 |
| Weights | Proprietary | Open |
| Context window | 262K | 131K |
| Max output | 131K | 16K |
| Input $ / M tokens | $0.14 | $2 |
| Output $ / M tokens | $0.28 | $6 |
| Results tracked | 18 | 51 |
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Category by category
Coding MiMo-V2-Omni leads
MiMo-V2-Omni: 43.3 (#89), Mistral Large: 34.3 (#240)
| Benchmark | MiMo-V2-Omni | Mistral Large |
|---|---|---|
| LMArena Coding | 1466 | 1277 |
| 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 Not comparable
MiMo-V2-Omni: —, Mistral Large: 28.6 (#89)
| Benchmark | MiMo-V2-Omni | Mistral Large |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 38.4% |
Reasoning MiMo-V2-Omni leads
MiMo-V2-Omni: 29.7 (#88), Mistral Large: 15.8 (#310)
| Benchmark | MiMo-V2-Omni | Mistral Large |
|---|---|---|
| LMArena Hard Prompts | 1445 | 1257 |
| SimpleBench | — | 22.5% |
| 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 MiMo-V2-Omni leads
MiMo-V2-Omni: 39.1 (#115), Mistral Large: 18.2 (#291)
| Benchmark | MiMo-V2-Omni | Mistral Large |
|---|---|---|
| LMArena Math | 1430 | 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 MiMo-V2-Omni leads
MiMo-V2-Omni: 40.5 (#118), Mistral Large: 30.1 (#230)
| Benchmark | MiMo-V2-Omni | Mistral Large |
|---|---|---|
| LMArena Expert | 1449 | 1232 |
| GPQA Diamond | — | 51.3% |
| MMLU-Pro | — | 59.9% |
| Confabulations | — | 21.4% |
| Vectara Hallucination Rate | — | 4.5% |
| GPQA (HELM) | — | 43.5% |
| MMLU | — | 80% |
Multimodal Not comparable
MiMo-V2-Omni: 38.6 (#63), Mistral Large: —
| Benchmark | MiMo-V2-Omni | Mistral Large |
|---|---|---|
| LMArena Vision | 1228 | — |
Multilingual MiMo-V2-Omni leads
MiMo-V2-Omni: 51.8 (#102), Mistral Large: 40.0 (#219)
| Benchmark | MiMo-V2-Omni | Mistral Large |
|---|---|---|
| LMArena Non-English | 1404 | 1237 |
| LMArena Chinese | 1465 | 1240 |
| LMArena French | 1447 | 1325 |
| LMArena German | 1399 | 1254 |
| LMArena Japanese | 1317 | 1188 |
| LMArena Korean | 1355 | 1202 |
| LMArena Russian | 1412 | 1257 |
| LMArena Spanish | 1434 | 1268 |
Instruction Following MiMo-V2-Omni leads
MiMo-V2-Omni: 75.2 (#66), Mistral Large: 67.9 (#191)
| Benchmark | MiMo-V2-Omni | Mistral Large |
|---|---|---|
| LMArena Instruction Following | 1428 | 1249 |
| LiveBench Instruction Following | — | 67.9% |
| IFEval | — | 87.7% |
Long Context MiMo-V2-Omni leads
MiMo-V2-Omni: 44.1 (#76), Mistral Large: 38.3 (#199)
| Benchmark | MiMo-V2-Omni | Mistral Large |
|---|---|---|
| LMArena Longer Query | 1442 | 1261 |
Writing & Preference MiMo-V2-Omni leads
MiMo-V2-Omni: 61.4 (#87), Mistral Large: 40.7 (#242)
| Benchmark | MiMo-V2-Omni | Mistral Large |
|---|---|---|
| LMArena Text | 1423 | 1266 |
| LMArena Creative Writing | 1392 | 1243 |
| LMArena Multi-Turn | 1445 | 1260 |
| Short-Story Creative Writing | — | 69% |
| EQ-Bench Creative Writing | — | 985 |
| WildBench | — | 80.1% |
| LiveBench Language | — | 39.4% |
Frequently asked questions
Is MiMo-V2-Omni better than Mistral Large?
MiMo-V2-Omni is the stronger model overall, scoring 43.6 to 31.9 on the Noometry Index.
Which is cheaper, MiMo-V2-Omni or Mistral Large?
MiMo-V2-Omni is cheaper. It lists at $0.14 per million input tokens and $0.28 per million output tokens; Mistral Large lists at $2 and $6.
Is MiMo-V2-Omni or Mistral Large better for coding?
MiMo-V2-Omni scores higher on coding benchmarks: 43.3 versus 34.3 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2-Omni does, with 262K tokens against 131K.
How many benchmarks do MiMo-V2-Omni and Mistral Large share?
17 benchmarks have published results for both models. MiMo-V2-Omni has 18 scored results on Noometry and Mistral Large has 51.