Model comparison
Mistral Large vs Qwen3.5-9B
Qwen3.5-9B is the stronger model overall, scoring 33.8 to 31.9 on the Noometry Index.
Last verified . 7 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- They share 7 benchmarks with published results for both. Mistral Large scores higher in 1 category and Qwen3.5-9B in 4 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.5-9B leads 34.8 to 18.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 8.5% for Mistral Large and 61.7% for Qwen3.5-9B.
- Qwen3.5-9B is cheaper at $0.10 / $0.15 per million input/output tokens, against $2 / $6 for Mistral Large.
- Qwen3.5-9B accepts more context: 262K tokens versus 131K.
Side by side
| Mistral Large | Qwen3.5-9B | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 31.9 | 33.8 |
| Released | 2024-02-26 | 2026-02-23 |
| Weights | Open | Open |
| Context window | 131K | 262K |
| Max output | 16K | 66K |
| Input $ / M tokens | $2 | $0.10 |
| Output $ / M tokens | $6 | $0.15 |
| Results tracked | 51 | 10 |
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Category by category
Coding Qwen3.5-9B leads
Mistral Large: 34.3 (#240), Qwen3.5-9B: 35.9 (#217)
| Benchmark | Mistral Large | Qwen3.5-9B |
|---|---|---|
| SciCode | 36.2% | 27.5% |
| 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), Qwen3.5-9B: 14.5 (#151)
| Benchmark | Mistral Large | Qwen3.5-9B |
|---|---|---|
| Terminal-Bench | — | 9.2% |
| Berkeley Function Calling Leaderboard | 38.4% | — |
Reasoning Qwen3.5-9B leads
Mistral Large: 15.8 (#310), Qwen3.5-9B: 23.1 (#182)
| Benchmark | Mistral Large | Qwen3.5-9B |
|---|---|---|
| CritPt | 0% | 0.3% |
| DTBench | 65.1% | 71.2% |
| LMCA | 16.7% | 24.5% |
| Epoch Capabilities Index | 128.52 | 139.46 |
| SimpleBench | 22.5% | — |
| Chess Puzzles | — | 12% |
| LiveBench Reasoning | 43.5% | — |
| LMArena Hard Prompts | 1257 | — |
| LiveBench Data Analysis | 50.1% | — |
| ForecastBench | 57.1 | — |
| LiveBench | 48.4% | — |
Math Qwen3.5-9B leads
Mistral Large: 18.2 (#291), Qwen3.5-9B: 34.8 (#192)
| Benchmark | Mistral Large | Qwen3.5-9B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 8.5% | 61.7% |
| MathArena Final-Answer Competitions | — | 48.5% |
| Omni-MATH | 28.1% | — |
| LiveBench Math | 42.5% | — |
| LMArena Math | 1262 | — |
| MATH Level 5 | 50.3% | — |
| FrontierMath (Feb 2025 set) | 0.3% | — |
Knowledge Qwen3.5-9B leads
Mistral Large: 30.1 (#230), Qwen3.5-9B: 46.0 (#84)
| Benchmark | Mistral Large | Qwen3.5-9B |
|---|---|---|
| GPQA Diamond | 51.3% | 79% |
| 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), Qwen3.5-9B: —
| Benchmark | Mistral Large | Qwen3.5-9B |
|---|---|---|
| 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), Qwen3.5-9B: —
| Benchmark | Mistral Large | Qwen3.5-9B |
|---|---|---|
| LiveBench Instruction Following | 67.9% | — |
| IFEval | 87.7% | — |
| LMArena Instruction Following | 1249 | — |
Long Context Not comparable
Mistral Large: 38.3 (#199), Qwen3.5-9B: —
| Benchmark | Mistral Large | Qwen3.5-9B |
|---|---|---|
| LMArena Longer Query | 1261 | — |
Writing & Preference Not comparable
Mistral Large: 40.7 (#242), Qwen3.5-9B: —
| Benchmark | Mistral Large | Qwen3.5-9B |
|---|---|---|
| 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 Qwen3.5-9B?
Qwen3.5-9B is the stronger model overall, scoring 33.8 to 31.9 on the Noometry Index.
Which is cheaper, Mistral Large or Qwen3.5-9B?
Qwen3.5-9B is cheaper. It lists at $0.10 per million input tokens and $0.15 per million output tokens; Mistral Large lists at $2 and $6.
Is Mistral Large or Qwen3.5-9B better for coding?
Qwen3.5-9B scores higher on coding benchmarks: 35.9 versus 34.3 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5-9B does, with 262K tokens against 131K.
How many benchmarks do Mistral Large and Qwen3.5-9B share?
7 benchmarks have published results for both models. Mistral Large has 51 scored results on Noometry and Qwen3.5-9B has 10.