Model comparison
Mistral vs Qwen3.5-9B
Qwen3.5-9B is the stronger model overall, scoring 33.8 to 29.9 on the Noometry Index.
Last verified . 0 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- The widest gap is in knowledge, where Qwen3.5-9B leads 46.0 to 16.6.
- Qwen3.5-9B has downloadable open weights; the other is API-only.
Side by side
| Mistral | Qwen3.5-9B | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 29.9 | 33.8 |
| Released | — | 2026-02-23 |
| Weights | Proprietary | Open |
| Context window | — | 262K |
| Max output | — | 66K |
| Input $ / M tokens | — | $0.10 |
| Output $ / M tokens | — | $0.15 |
| Results tracked | 22 | 10 |
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Category by category
Coding Qwen3.5-9B leads
Mistral: 33.8 (#250), Qwen3.5-9B: 35.9 (#217)
| Benchmark | Mistral | Qwen3.5-9B |
|---|---|---|
| SciCode | — | 27.5% |
| LMArena Coding | 1162 | — |
Agentic & Tool Use Not comparable
Mistral: —, Qwen3.5-9B: 14.5 (#151)
| Benchmark | Mistral | Qwen3.5-9B |
|---|---|---|
| Terminal-Bench | — | 9.2% |
Reasoning Too close to call
Mistral: 22.2 (#200), Qwen3.5-9B: 23.1 (#182)
| Benchmark | Mistral | Qwen3.5-9B |
|---|---|---|
| CritPt | — | 0.3% |
| Chess Puzzles | — | 12% |
| LMArena Hard Prompts | 1149 | — |
| DTBench | — | 71.2% |
| LMCA | — | 24.5% |
| Epoch Capabilities Index | — | 139.46 |
Math Qwen3.5-9B leads
Mistral: 22.3 (#278), Qwen3.5-9B: 34.8 (#192)
| Benchmark | Mistral | Qwen3.5-9B |
|---|---|---|
| MathArena Final-Answer Competitions | — | 48.5% |
| OTIS Mock AIME 2024-2025 | — | 61.7% |
| Omni-MATH | 7.2% | — |
| LMArena Math | 1180 | — |
Knowledge Qwen3.5-9B leads
Mistral: 16.6 (#288), Qwen3.5-9B: 46.0 (#84)
| Benchmark | Mistral | Qwen3.5-9B |
|---|---|---|
| GPQA Diamond | — | 79% |
| MMLU-Pro | 27.7% | — |
| GPQA (HELM) | 30.3% | — |
| LMArena Expert | 1125 | — |
Multilingual Not comparable
Mistral: 32.8 (#254), Qwen3.5-9B: —
| Benchmark | Mistral | Qwen3.5-9B |
|---|---|---|
| LMArena Non-English | 1129 | — |
| LMArena Chinese | 1109 | — |
| LMArena French | 1180 | — |
| LMArena German | 1155 | — |
| LMArena Japanese | 1013 | — |
| LMArena Korean | 1032 | — |
| LMArena Russian | 1168 | — |
| LMArena Spanish | 1143 | — |
Instruction Following Not comparable
Mistral: 52.6 (#288), Qwen3.5-9B: —
| Benchmark | Mistral | Qwen3.5-9B |
|---|---|---|
| IFEval | 56.8% | — |
| LMArena Instruction Following | 1152 | — |
Long Context Not comparable
Mistral: 35.0 (#245), Qwen3.5-9B: —
| Benchmark | Mistral | Qwen3.5-9B |
|---|---|---|
| LMArena Longer Query | 1153 | — |
Writing & Preference Not comparable
Mistral: 37.0 (#260), Qwen3.5-9B: —
| Benchmark | Mistral | Qwen3.5-9B |
|---|---|---|
| LMArena Text | 1165 | — |
| LMArena Creative Writing | 1158 | — |
| WildBench | 66% | — |
| LMArena Multi-Turn | 1147 | — |
Frequently asked questions
Is Mistral better than Qwen3.5-9B?
Qwen3.5-9B is the stronger model overall, scoring 33.8 to 29.9 on the Noometry Index.
Is Mistral or Qwen3.5-9B better for coding?
Qwen3.5-9B scores higher on coding benchmarks: 35.9 versus 33.8 in the Noometry coding category.
How many benchmarks do Mistral and Qwen3.5-9B share?
0 benchmarks have published results for both models. Mistral has 22 scored results on Noometry and Qwen3.5-9B has 10.