Model comparison
Mistral Small 3.2 vs Qwen3 14B
Qwen3 14B is the stronger model overall, scoring 35.0 to 30.8 on the Noometry Index. Mistral Small 3.2 costs 4.6× less per token, which makes it the better buy when Qwen3 14B's lead doesn't matter for your workload.
Last verified . 5 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- They share 5 benchmarks with published results for both. Mistral Small 3.2 scores higher in 0 categories and Qwen3 14B in 3 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3 14B leads 39.2 to 26.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 30.3% for Mistral Small 3.2 and 66.4% for Qwen3 14B.
- Mistral Small 3.2 is cheaper at $0.0938 / $0.25 per million input/output tokens, against $0.35 / $1.40 for Qwen3 14B.
- Mistral Small 3.2 accepts more context: 256K tokens versus 131K.
Side by side
| Mistral Small 3.2 | Qwen3 14B | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 30.8 | 35.0 |
| Released | 2025-06-20 | 2025-04 |
| Weights | Open | Open |
| Context window | 256K | 131K |
| Max output | 16K | 8K |
| Input $ / M tokens | $0.0938 | $0.35 |
| Output $ / M tokens | $0.25 | $1.40 |
| Results tracked | 6 | 12 |
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Category by category
Coding Not comparable
Mistral Small 3.2: —, Qwen3 14B: 36.7 (#191)
| Benchmark | Mistral Small 3.2 | Qwen3 14B |
|---|---|---|
| SciCode | — | 31.6% |
Agentic & Tool Use Not comparable
Mistral Small 3.2: —, Qwen3 14B: 29.4 (#78)
| Benchmark | Mistral Small 3.2 | Qwen3 14B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 41% |
Reasoning Too close to call
Mistral Small 3.2: 17.5 (#282), Qwen3 14B: 18.1 (#272)
| Benchmark | Mistral Small 3.2 | Qwen3 14B |
|---|---|---|
| Kagi LLM Benchmark | 40.4% | 49.1% |
| Chess Puzzles | 1% | 4% |
| Epoch Capabilities Index | 131.73 | 138.23 |
| CritPt | — | 0% |
| DTBench | — | 64% |
| LMCA | — | 18.2% |
Math Qwen3 14B leads
Mistral Small 3.2: 24.9 (#251), Qwen3 14B: 37.0 (#102)
| Benchmark | Mistral Small 3.2 | Qwen3 14B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 30.3% | 66.4% |
Knowledge Qwen3 14B leads
Mistral Small 3.2: 26.8 (#249), Qwen3 14B: 39.2 (#124)
| Benchmark | Mistral Small 3.2 | Qwen3 14B |
|---|---|---|
| GPQA Diamond | 49.1% | 63.8% |
| Vectara Hallucination Rate | — | 5.4% |
Long Context Not comparable
Mistral Small 3.2: —, Qwen3 14B: 38.0 (#201)
| Benchmark | Mistral Small 3.2 | Qwen3 14B |
|---|---|---|
| Fiction.LiveBench | — | 62.5% |
Writing & Preference Not comparable
Mistral Small 3.2: 45.1 (#219), Qwen3 14B: —
| Benchmark | Mistral Small 3.2 | Qwen3 14B |
|---|---|---|
| EQ-Bench Creative Writing | 1255 | — |
Frequently asked questions
Is Mistral Small 3.2 better than Qwen3 14B?
Qwen3 14B is the stronger model overall, scoring 35.0 to 30.8 on the Noometry Index. Mistral Small 3.2 costs 4.6× less per token, which makes it the better buy when Qwen3 14B's lead doesn't matter for your workload.
Which is cheaper, Mistral Small 3.2 or Qwen3 14B?
Mistral Small 3.2 is cheaper. It lists at $0.0938 per million input tokens and $0.25 per million output tokens; Qwen3 14B lists at $0.35 and $1.40.
Which has the bigger context window?
Mistral Small 3.2 does, with 256K tokens against 131K.
How many benchmarks do Mistral Small 3.2 and Qwen3 14B share?
5 benchmarks have published results for both models. Mistral Small 3.2 has 6 scored results on Noometry and Qwen3 14B has 12.