Model comparison
Mistral vs Qwen3 14B
Qwen3 14B is the stronger model overall, scoring 35.5 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 14B leads 39.3 to 16.6.
- Qwen3 14B has downloadable open weights; the other is API-only.
Side by side
| Mistral | Qwen3 14B | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 29.9 | 35.5 |
| Released | — | 2025-04 |
| Weights | Proprietary | Open |
| Context window | — | 131K |
| Max output | — | 8K |
| Input $ / M tokens | — | $0.35 |
| Output $ / M tokens | — | $1.40 |
| Results tracked | 22 | 12 |
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Category by category
Coding Qwen3 14B leads
Mistral: 33.8 (#250), Qwen3 14B: 37.3 (#195)
| Benchmark | Mistral | Qwen3 14B |
|---|---|---|
| SciCode | — | 31.6% |
| LMArena Coding | 1162 | — |
Agentic & Tool Use Not comparable
Mistral: —, Qwen3 14B: 29.6 (#83)
| Benchmark | Mistral | Qwen3 14B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 41% |
Reasoning Mistral leads
Mistral: 22.2 (#200), Qwen3 14B: 18.5 (#280)
| Benchmark | Mistral | Qwen3 14B |
|---|---|---|
| Kagi LLM Benchmark | — | 49.1% |
| CritPt | — | 0% |
| Chess Puzzles | — | 4% |
| LMArena Hard Prompts | 1149 | — |
| DTBench | — | 64% |
| LMCA | — | 18.2% |
| Epoch Capabilities Index | — | 138.23 |
Math Qwen3 14B leads
Mistral: 22.3 (#278), Qwen3 14B: 38.6 (#133)
| Benchmark | Mistral | Qwen3 14B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 66.4% |
| Omni-MATH | 7.2% | — |
| LMArena Math | 1180 | — |
Knowledge Qwen3 14B leads
Mistral: 16.6 (#288), Qwen3 14B: 39.3 (#134)
| Benchmark | Mistral | Qwen3 14B |
|---|---|---|
| GPQA Diamond | — | 63.8% |
| MMLU-Pro | 27.7% | — |
| Vectara Hallucination Rate | — | 5.4% |
| GPQA (HELM) | 30.3% | — |
| LMArena Expert | 1125 | — |
Multilingual Not comparable
Mistral: 32.8 (#254), Qwen3 14B: —
| Benchmark | Mistral | Qwen3 14B |
|---|---|---|
| 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 14B: —
| Benchmark | Mistral | Qwen3 14B |
|---|---|---|
| IFEval | 56.8% | — |
| LMArena Instruction Following | 1152 | — |
Long Context Qwen3 14B leads
Mistral: 35.0 (#245), Qwen3 14B: 38.1 (#204)
| Benchmark | Mistral | Qwen3 14B |
|---|---|---|
| Fiction.LiveBench | — | 62.5% |
| LMArena Longer Query | 1153 | — |
Writing & Preference Not comparable
Mistral: 37.0 (#260), Qwen3 14B: —
| Benchmark | Mistral | Qwen3 14B |
|---|---|---|
| LMArena Text | 1165 | — |
| LMArena Creative Writing | 1158 | — |
| WildBench | 66% | — |
| LMArena Multi-Turn | 1147 | — |
Frequently asked questions
Is Mistral better than Qwen3 14B?
Qwen3 14B is the stronger model overall, scoring 35.5 to 29.9 on the Noometry Index.
Is Mistral or Qwen3 14B better for coding?
Qwen3 14B scores higher on coding benchmarks: 37.3 versus 33.8 in the Noometry coding category.
How many benchmarks do Mistral and Qwen3 14B share?
0 benchmarks have published results for both models. Mistral has 22 scored results on Noometry and Qwen3 14B has 12.