Model comparison
Mistral Large vs Qwen2.5-VL 72B Instruct
Mistral Large is the stronger model overall, scoring 31.9 to 29.9 on the Noometry Index.
Last verified . 0 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- The widest gap is in agentic & tool use, where Mistral Large leads 28.6 to 18.6.
- Mistral Large is cheaper at $2 / $6 per million input/output tokens, against $2.80 / $8.40 for Qwen2.5-VL 72B Instruct.
Side by side
| Mistral Large | Qwen2.5-VL 72B Instruct | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 31.9 | 29.9 |
| Released | 2024-02-26 | 2024-09 |
| Weights | Open | Open |
| Context window | 131K | 131K |
| Max output | 16K | 8K |
| Input $ / M tokens | $2 | $2.80 |
| Output $ / M tokens | $6 | $8.40 |
| Results tracked | 51 | 6 |
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Category by category
Coding Not comparable
Mistral Large: 34.3 (#240), Qwen2.5-VL 72B Instruct: —
| Benchmark | Mistral Large | Qwen2.5-VL 72B Instruct |
|---|---|---|
| SciCode | 36.2% | — |
| BigCodeBench Instruct | 30% | — |
| LiveBench Coding | 47.1% | — |
| LMArena Coding | 1277 | — |
| BigCodeBench Complete | 38.3% | — |
| ALE-Bench | 264.7 | — |
| HumanEval+ | 62.2% | — |
| MBPP+ | 59.5% | — |
Agentic & Tool Use Mistral Large leads
Mistral Large: 28.6 (#89), Qwen2.5-VL 72B Instruct: 18.6 (#144)
| Benchmark | Mistral Large | Qwen2.5-VL 72B Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | 38.4% | — |
| OSWorld | — | 5% |
Reasoning Qwen2.5-VL 72B Instruct leads
Mistral Large: 15.8 (#310), Qwen2.5-VL 72B Instruct: 20.7 (#233)
| Benchmark | Mistral Large | Qwen2.5-VL 72B Instruct |
|---|---|---|
| SimpleBench | 22.5% | — |
| Kagi LLM Benchmark | — | 36% |
| CritPt | 0% | — |
| LiveBench Reasoning | 43.5% | — |
| LMArena Hard Prompts | 1257 | — |
| DTBench | 65.1% | — |
| LiveBench Data Analysis | 50.1% | — |
| LMCA | 16.7% | — |
| Epoch Capabilities Index | 128.52 | — |
| ForecastBench | 57.1 | — |
| LiveBench | 48.4% | — |
Math Not comparable
Mistral Large: 18.2 (#291), Qwen2.5-VL 72B Instruct: —
| Benchmark | Mistral Large | Qwen2.5-VL 72B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 8.5% | — |
| Omni-MATH | 28.1% | — |
| LiveBench Math | 42.5% | — |
| LMArena Math | 1262 | — |
| MATH Level 5 | 50.3% | — |
| FrontierMath (Feb 2025 set) | 0.3% | — |
Knowledge Not comparable
Mistral Large: 30.1 (#230), Qwen2.5-VL 72B Instruct: —
| Benchmark | Mistral Large | Qwen2.5-VL 72B Instruct |
|---|---|---|
| GPQA Diamond | 51.3% | — |
| MMLU-Pro | 59.9% | — |
| Confabulations | 21.4% | — |
| Vectara Hallucination Rate | 4.5% | — |
| GPQA (HELM) | 43.5% | — |
| LMArena Expert | 1232 | — |
| MMLU | 80% | — |
Multimodal Not comparable
Mistral Large: —, Qwen2.5-VL 72B Instruct: 33.5 (#97)
| Benchmark | Mistral Large | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Vision | — | 1107 |
| Video-MME | — | 73.5% |
| GeoBench | — | 62% |
| SpatialViz-Bench | — | 33.3% |
Multilingual Not comparable
Mistral Large: 40.0 (#219), Qwen2.5-VL 72B Instruct: —
| Benchmark | Mistral Large | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Non-English | 1237 | — |
| LMArena Chinese | 1240 | — |
| LMArena French | 1325 | — |
| LMArena German | 1254 | — |
| LMArena Japanese | 1188 | — |
| LMArena Korean | 1202 | — |
| LMArena Russian | 1257 | — |
| LMArena Spanish | 1268 | — |
Instruction Following Not comparable
Mistral Large: 67.9 (#191), Qwen2.5-VL 72B Instruct: —
| Benchmark | Mistral Large | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LiveBench Instruction Following | 67.9% | — |
| IFEval | 87.7% | — |
| LMArena Instruction Following | 1249 | — |
Long Context Not comparable
Mistral Large: 38.3 (#199), Qwen2.5-VL 72B Instruct: —
| Benchmark | Mistral Large | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Longer Query | 1261 | — |
Writing & Preference Not comparable
Mistral Large: 40.7 (#242), Qwen2.5-VL 72B Instruct: —
| Benchmark | Mistral Large | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Text | 1266 | — |
| LMArena Creative Writing | 1243 | — |
| Short-Story Creative Writing | 69% | — |
| EQ-Bench Creative Writing | 985 | — |
| WildBench | 80.1% | — |
| LMArena Multi-Turn | 1260 | — |
| LiveBench Language | 39.4% | — |
Frequently asked questions
Is Mistral Large better than Qwen2.5-VL 72B Instruct?
Mistral Large is the stronger model overall, scoring 31.9 to 29.9 on the Noometry Index.
Which is cheaper, Mistral Large or Qwen2.5-VL 72B Instruct?
Mistral Large is cheaper. It lists at $2 per million input tokens and $6 per million output tokens; Qwen2.5-VL 72B Instruct lists at $2.80 and $8.40.
Which has the bigger context window?
Both accept 131K tokens.
How many benchmarks do Mistral Large and Qwen2.5-VL 72B Instruct share?
0 benchmarks have published results for both models. Mistral Large has 51 scored results on Noometry and Qwen2.5-VL 72B Instruct has 6.