Model comparison
Mistral Small 3.1 vs Qwen3-30B-A3B
Qwen3-30B-A3B is the stronger model overall, scoring 38.9 to 31.7 on the Noometry Index.
Last verified . 21 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- They share 21 benchmarks with published results for both. Mistral Small 3.1 scores higher in 2 categories and Qwen3-30B-A3B in 6 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3-30B-A3B leads 37.4 to 14.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 3.9% for Mistral Small 3.1 and 70.3% for Qwen3-30B-A3B.
- Qwen3-30B-A3B is cheaper at $0.12 / $0.50 per million input/output tokens, against $0.35 / $0.56 for Mistral Small 3.1.
- Mistral Small 3.1 accepts more context: 128K tokens versus 41K.
Side by side
| Mistral Small 3.1 | Qwen3-30B-A3B | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 31.7 | 38.9 |
| Released | 2025-03-17 | 2025-04-28 |
| Weights | Open | Open |
| Context window | 128K | 41K |
| Max output | 102K | 16K |
| Input $ / M tokens | $0.35 | $0.12 |
| Output $ / M tokens | $0.56 | $0.50 |
| Results tracked | 28 | 32 |
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Category by category
Coding Too close to call
Mistral Small 3.1: 38.3 (#179), Qwen3-30B-A3B: 37.5 (#194)
| Benchmark | Mistral Small 3.1 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Coding | 1309 | 1416 |
| SciCode | — | 33.3% |
| WeirdML | — | 29.8% |
Agentic & Tool Use Not comparable
Mistral Small 3.1: —, Qwen3-30B-A3B: 29.8 (#82)
| Benchmark | Mistral Small 3.1 | Qwen3-30B-A3B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 41.4% |
Reasoning Qwen3-30B-A3B leads
Mistral Small 3.1: 19.7 (#254), Qwen3-30B-A3B: 22.2 (#204)
| Benchmark | Mistral Small 3.1 | Qwen3-30B-A3B |
|---|---|---|
| Chess Puzzles | 1% | 8% |
| LMArena Hard Prompts | 1278 | 1398 |
| Epoch Capabilities Index | 127.48 | 139.63 |
| Kagi LLM Benchmark | — | 54.9% |
| CritPt | — | 0.3% |
| DTBench | — | 69.3% |
| LMCA | — | 22.4% |
Math Qwen3-30B-A3B leads
Mistral Small 3.1: 14.7 (#301), Qwen3-30B-A3B: 37.4 (#157)
| Benchmark | Mistral Small 3.1 | Qwen3-30B-A3B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 3.9% | 70.3% |
| LMArena Math | 1262 | 1394 |
| MathArena Final-Answer Competitions | — | 47.8% |
| Omni-MATH | 24.8% | — |
Knowledge Qwen3-30B-A3B leads
Mistral Small 3.1: 22.6 (#271), Qwen3-30B-A3B: 41.8 (#105)
| Benchmark | Mistral Small 3.1 | Qwen3-30B-A3B |
|---|---|---|
| GPQA Diamond | 41.9% | 70.1% |
| LMArena Expert | 1257 | 1396 |
| MMLU-Pro | 61% | — |
| Confabulations | — | 12.3% |
| GPQA (HELM) | 39.2% | — |
Multimodal Not comparable
Mistral Small 3.1: 33.2 (#99), Qwen3-30B-A3B: —
| Benchmark | Mistral Small 3.1 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Vision | 1136 | — |
Multilingual Qwen3-30B-A3B leads
Mistral Small 3.1: 41.2 (#209), Qwen3-30B-A3B: 49.5 (#132)
| Benchmark | Mistral Small 3.1 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Non-English | 1255 | 1372 |
| LMArena Chinese | 1253 | 1433 |
| LMArena French | 1273 | 1418 |
| LMArena German | 1266 | 1380 |
| LMArena Japanese | 1208 | 1337 |
| LMArena Korean | 1206 | 1331 |
| LMArena Russian | 1263 | 1370 |
| LMArena Spanish | 1283 | 1404 |
Instruction Following Qwen3-30B-A3B leads
Mistral Small 3.1: 63.6 (#230), Qwen3-30B-A3B: 72.0 (#142)
| Benchmark | Mistral Small 3.1 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Instruction Following | 1264 | 1363 |
| IFEval | 75% | — |
Long Context Mistral Small 3.1 leads
Mistral Small 3.1: 39.5 (#178), Qwen3-30B-A3B: 31.0 (#283)
| Benchmark | Mistral Small 3.1 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Longer Query | 1299 | 1379 |
| Fiction.LiveBench | — | 40.6% |
Writing & Preference Qwen3-30B-A3B leads
Mistral Small 3.1: 37.0 (#259), Qwen3-30B-A3B: 55.6 (#143)
| Benchmark | Mistral Small 3.1 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Text | 1277 | 1384 |
| LMArena Creative Writing | 1253 | 1317 |
| LMArena Multi-Turn | 1270 | 1378 |
| Short-Story Creative Writing | — | 75.3% |
| EQ-Bench Creative Writing | 761 | — |
| WildBench | 78.8% | — |
Frequently asked questions
Is Mistral Small 3.1 better than Qwen3-30B-A3B?
Qwen3-30B-A3B is the stronger model overall, scoring 38.9 to 31.7 on the Noometry Index.
Which is cheaper, Mistral Small 3.1 or Qwen3-30B-A3B?
Qwen3-30B-A3B is cheaper. It lists at $0.12 per million input tokens and $0.50 per million output tokens; Mistral Small 3.1 lists at $0.35 and $0.56.
Is Mistral Small 3.1 or Qwen3-30B-A3B better for coding?
They score almost the same on coding (38.3 vs 37.5); test both on your own repository before choosing.
Which has the bigger context window?
Mistral Small 3.1 does, with 128K tokens against 41K.
How many benchmarks do Mistral Small 3.1 and Qwen3-30B-A3B share?
21 benchmarks have published results for both models. Mistral Small 3.1 has 28 scored results on Noometry and Qwen3-30B-A3B has 32.