Model comparison
Mistral Large vs Qwen Turbo
Mistral Large is the stronger model overall, scoring 31.9 to 27.1 on the Noometry Index. Qwen Turbo costs 34× less per token, which makes it the better buy when Mistral Large's lead doesn't matter for your workload.
Last verified . 3 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- They share 3 benchmarks with published results for both. Mistral Large scores higher in 2 categories and Qwen Turbo in 0 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Mistral Large leads 30.1 to 22.2.
- The biggest single-benchmark swing is GPQA Diamond: 51.3% for Mistral Large and 41.8% for Qwen Turbo.
- Qwen Turbo is cheaper at $0.05 / $0.20 per million input/output tokens, against $2 / $6 for Mistral Large.
- Qwen Turbo accepts more context: 1M tokens versus 131K.
- Mistral Large has downloadable open weights; the other is API-only.
Side by side
| Mistral Large | Qwen Turbo | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 31.9 | 27.1 |
| Released | 2024-02-26 | 2024-11-01 |
| Weights | Open | Proprietary |
| Context window | 131K | 1M |
| Max output | 16K | 16K |
| Input $ / M tokens | $2 | $0.05 |
| Output $ / M tokens | $6 | $0.20 |
| Results tracked | 51 | 3 |
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Category by category
Coding Not comparable
Mistral Large: 34.3 (#240), Qwen Turbo: —
| Benchmark | Mistral Large | Qwen Turbo |
|---|---|---|
| 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 Not comparable
Mistral Large: 28.6 (#89), Qwen Turbo: —
| Benchmark | Mistral Large | Qwen Turbo |
|---|---|---|
| Berkeley Function Calling Leaderboard | 38.4% | — |
Reasoning Not comparable
Mistral Large: 15.8 (#310), Qwen Turbo: —
| Benchmark | Mistral Large | Qwen Turbo |
|---|---|---|
| SimpleBench | 22.5% | — |
| 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 Mistral Large leads
Mistral Large: 18.2 (#291), Qwen Turbo: 15.3 (#297)
| Benchmark | Mistral Large | Qwen Turbo |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 8.5% | 6.1% |
| MATH Level 5 | 50.3% | 56.2% |
| Omni-MATH | 28.1% | — |
| LiveBench Math | 42.5% | — |
| LMArena Math | 1262 | — |
| FrontierMath (Feb 2025 set) | 0.3% | — |
Knowledge Mistral Large leads
Mistral Large: 30.1 (#230), Qwen Turbo: 22.2 (#272)
| Benchmark | Mistral Large | Qwen Turbo |
|---|---|---|
| GPQA Diamond | 51.3% | 41.8% |
| MMLU-Pro | 59.9% | — |
| Confabulations | 21.4% | — |
| Vectara Hallucination Rate | 4.5% | — |
| GPQA (HELM) | 43.5% | — |
| LMArena Expert | 1232 | — |
| MMLU | 80% | — |
Multilingual Not comparable
Mistral Large: 40.0 (#219), Qwen Turbo: —
| Benchmark | Mistral Large | Qwen Turbo |
|---|---|---|
| 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), Qwen Turbo: —
| Benchmark | Mistral Large | Qwen Turbo |
|---|---|---|
| LiveBench Instruction Following | 67.9% | — |
| IFEval | 87.7% | — |
| LMArena Instruction Following | 1249 | — |
Long Context Not comparable
Mistral Large: 38.3 (#199), Qwen Turbo: —
| Benchmark | Mistral Large | Qwen Turbo |
|---|---|---|
| LMArena Longer Query | 1261 | — |
Writing & Preference Not comparable
Mistral Large: 40.7 (#242), Qwen Turbo: —
| Benchmark | Mistral Large | Qwen Turbo |
|---|---|---|
| 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 Qwen Turbo?
Mistral Large is the stronger model overall, scoring 31.9 to 27.1 on the Noometry Index. Qwen Turbo costs 34× less per token, which makes it the better buy when Mistral Large's lead doesn't matter for your workload.
Which is cheaper, Mistral Large or Qwen Turbo?
Qwen Turbo is cheaper. It lists at $0.05 per million input tokens and $0.20 per million output tokens; Mistral Large lists at $2 and $6.
Which has the bigger context window?
Qwen Turbo does, with 1M tokens against 131K.
How many benchmarks do Mistral Large and Qwen Turbo share?
3 benchmarks have published results for both models. Mistral Large has 51 scored results on Noometry and Qwen Turbo has 3.