Model comparison
Mistral Small 3.2 vs Qwen3-30B-A3B
Qwen3-30B-A3B is the stronger model overall, scoring 38.5 to 30.8 on the Noometry Index. Mistral Small 3.2 costs 1.6× less per token, which makes it the better buy when Qwen3-30B-A3B's lead doesn't matter for your workload.
Last verified . 5 shared benchmarks.
Summary
- They share 5 benchmarks with published results for both. Mistral Small 3.2 scores higher in 0 categories and Qwen3-30B-A3B in 4 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3-30B-A3B leads 41.7 to 26.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 30.3% for Mistral Small 3.2 and 70.3% for Qwen3-30B-A3B.
- Mistral Small 3.2 is cheaper at $0.0938 / $0.25 per million input/output tokens, against $0.12 / $0.50 for Qwen3-30B-A3B.
- Mistral Small 3.2 accepts more context: 256K tokens versus 41K.
Side by side
| Mistral Small 3.2 | Qwen3-30B-A3B | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 30.8 | 38.5 |
| Released | 2025-06-20 | 2025-04-28 |
| Weights | Open | Open |
| Context window | 256K | 41K |
| Max output | 16K | 16K |
| Input $ / M tokens | $0.0938 | $0.12 |
| Output $ / M tokens | $0.25 | $0.50 |
| Results tracked | 6 | 32 |
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Category by category
Coding Not comparable
Mistral Small 3.2: —, Qwen3-30B-A3B: 37.0 (#188)
| Benchmark | Mistral Small 3.2 | Qwen3-30B-A3B |
|---|---|---|
| SciCode | — | 33.3% |
| WeirdML | — | 29.8% |
| LMArena Coding | — | 1416 |
Agentic & Tool Use Not comparable
Mistral Small 3.2: —, Qwen3-30B-A3B: 29.5 (#75)
| Benchmark | Mistral Small 3.2 | Qwen3-30B-A3B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 41.4% |
Reasoning Qwen3-30B-A3B leads
Mistral Small 3.2: 17.5 (#282), Qwen3-30B-A3B: 21.6 (#193)
| Benchmark | Mistral Small 3.2 | Qwen3-30B-A3B |
|---|---|---|
| Kagi LLM Benchmark | 40.4% | 54.9% |
| Chess Puzzles | 1% | 8% |
| Epoch Capabilities Index | 131.73 | 139.63 |
| CritPt | — | 0.3% |
| LMArena Hard Prompts | — | 1398 |
| DTBench | — | 69.3% |
| LMCA | — | 22.4% |
Math Qwen3-30B-A3B leads
Mistral Small 3.2: 24.9 (#251), Qwen3-30B-A3B: 36.0 (#133)
| Benchmark | Mistral Small 3.2 | Qwen3-30B-A3B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 30.3% | 70.3% |
| MathArena Final-Answer Competitions | — | 47.8% |
| LMArena Math | — | 1394 |
Knowledge Qwen3-30B-A3B leads
Mistral Small 3.2: 26.8 (#249), Qwen3-30B-A3B: 41.7 (#96)
| Benchmark | Mistral Small 3.2 | Qwen3-30B-A3B |
|---|---|---|
| GPQA Diamond | 49.1% | 70.1% |
| Confabulations | — | 12.3% |
| LMArena Expert | — | 1395 |
Multilingual Not comparable
Mistral Small 3.2: —, Qwen3-30B-A3B: 49.8 (#126)
| Benchmark | Mistral Small 3.2 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Non-English | — | 1372 |
| LMArena Chinese | — | 1434 |
| LMArena French | — | 1419 |
| LMArena German | — | 1379 |
| LMArena Japanese | — | 1338 |
| LMArena Korean | — | 1333 |
| LMArena Russian | — | 1369 |
| LMArena Spanish | — | 1403 |
Instruction Following Not comparable
Mistral Small 3.2: —, Qwen3-30B-A3B: 72.2 (#137)
| Benchmark | Mistral Small 3.2 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Instruction Following | — | 1364 |
Long Context Not comparable
Mistral Small 3.2: —, Qwen3-30B-A3B: 31.0 (#278)
| Benchmark | Mistral Small 3.2 | Qwen3-30B-A3B |
|---|---|---|
| Fiction.LiveBench | — | 40.6% |
| LMArena Longer Query | — | 1379 |
Writing & Preference Qwen3-30B-A3B leads
Mistral Small 3.2: 45.1 (#219), Qwen3-30B-A3B: 55.9 (#138)
| Benchmark | Mistral Small 3.2 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Text | — | 1384 |
| LMArena Creative Writing | — | 1318 |
| Short-Story Creative Writing | — | 75.3% |
| EQ-Bench Creative Writing | 1255 | — |
| LMArena Multi-Turn | — | 1378 |
Frequently asked questions
Is Mistral Small 3.2 better than Qwen3-30B-A3B?
Qwen3-30B-A3B is the stronger model overall, scoring 38.5 to 30.8 on the Noometry Index. Mistral Small 3.2 costs 1.6× less per token, which makes it the better buy when Qwen3-30B-A3B's lead doesn't matter for your workload.
Which is cheaper, Mistral Small 3.2 or Qwen3-30B-A3B?
Mistral Small 3.2 is cheaper. It lists at $0.0938 per million input tokens and $0.25 per million output tokens; Qwen3-30B-A3B lists at $0.12 and $0.50.
Which has the bigger context window?
Mistral Small 3.2 does, with 256K tokens against 41K.
How many benchmarks do Mistral Small 3.2 and Qwen3-30B-A3B share?
5 benchmarks have published results for both models. Mistral Small 3.2 has 6 scored results on Noometry and Qwen3-30B-A3B has 32.