Model comparison
Mistral Large vs Qwen3-30B-A3B
Qwen3-30B-A3B is the stronger model overall, scoring 38.9 to 31.9 on the Noometry Index.
Last verified . 27 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- They share 27 benchmarks with published results for both. Mistral Large scores higher in 1 category and Qwen3-30B-A3B in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3-30B-A3B leads 37.4 to 18.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 8.5% for Mistral Large and 70.3% for Qwen3-30B-A3B.
- Qwen3-30B-A3B is cheaper at $0.12 / $0.50 per million input/output tokens, against $2 / $6 for Mistral Large.
- Mistral Large accepts more context: 131K tokens versus 41K.
Side by side
| Mistral Large | Qwen3-30B-A3B | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 31.9 | 38.9 |
| Released | 2024-02-26 | 2025-04-28 |
| Weights | Open | Open |
| Context window | 131K | 41K |
| Max output | 16K | 16K |
| Input $ / M tokens | $2 | $0.12 |
| Output $ / M tokens | $6 | $0.50 |
| Results tracked | 51 | 32 |
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Category by category
Coding Qwen3-30B-A3B leads
Mistral Large: 34.3 (#240), Qwen3-30B-A3B: 37.5 (#194)
| Benchmark | Mistral Large | Qwen3-30B-A3B |
|---|---|---|
| SciCode | 36.2% | 33.3% |
| LMArena Coding | 1277 | 1416 |
| WeirdML | — | 29.8% |
| BigCodeBench Instruct | 30% | — |
| LiveBench Coding | 47.1% | — |
| BigCodeBench Complete | 38.3% | — |
| ALE-Bench | 264.7 | — |
| HumanEval+ | 62.2% | — |
| MBPP+ | 59.5% | — |
Agentic & Tool Use Qwen3-30B-A3B leads
Mistral Large: 28.6 (#89), Qwen3-30B-A3B: 29.8 (#82)
| Benchmark | Mistral Large | Qwen3-30B-A3B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 38.4% | 41.4% |
Reasoning Qwen3-30B-A3B leads
Mistral Large: 15.8 (#310), Qwen3-30B-A3B: 22.2 (#204)
| Benchmark | Mistral Large | Qwen3-30B-A3B |
|---|---|---|
| CritPt | 0% | 0.3% |
| LMArena Hard Prompts | 1257 | 1398 |
| DTBench | 65.1% | 69.3% |
| LMCA | 16.7% | 22.4% |
| Epoch Capabilities Index | 128.52 | 139.63 |
| SimpleBench | 22.5% | — |
| Kagi LLM Benchmark | — | 54.9% |
| Chess Puzzles | — | 8% |
| LiveBench Reasoning | 43.5% | — |
| LiveBench Data Analysis | 50.1% | — |
| ForecastBench | 57.1 | — |
| LiveBench | 48.4% | — |
Math Qwen3-30B-A3B leads
Mistral Large: 18.2 (#291), Qwen3-30B-A3B: 37.4 (#157)
| Benchmark | Mistral Large | Qwen3-30B-A3B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 8.5% | 70.3% |
| LMArena Math | 1262 | 1394 |
| MathArena Final-Answer Competitions | — | 47.8% |
| Omni-MATH | 28.1% | — |
| LiveBench Math | 42.5% | — |
| MATH Level 5 | 50.3% | — |
| FrontierMath (Feb 2025 set) | 0.3% | — |
Knowledge Qwen3-30B-A3B leads
Mistral Large: 30.1 (#230), Qwen3-30B-A3B: 41.8 (#105)
| Benchmark | Mistral Large | Qwen3-30B-A3B |
|---|---|---|
| GPQA Diamond | 51.3% | 70.1% |
| Confabulations | 21.4% | 12.3% |
| LMArena Expert | 1232 | 1396 |
| MMLU-Pro | 59.9% | — |
| Vectara Hallucination Rate | 4.5% | — |
| GPQA (HELM) | 43.5% | — |
| MMLU | 80% | — |
Multilingual Qwen3-30B-A3B leads
Mistral Large: 40.0 (#219), Qwen3-30B-A3B: 49.5 (#132)
| Benchmark | Mistral Large | Qwen3-30B-A3B |
|---|---|---|
| LMArena Non-English | 1237 | 1372 |
| LMArena Chinese | 1240 | 1433 |
| LMArena French | 1325 | 1418 |
| LMArena German | 1254 | 1380 |
| LMArena Japanese | 1188 | 1337 |
| LMArena Korean | 1202 | 1331 |
| LMArena Russian | 1257 | 1370 |
| LMArena Spanish | 1268 | 1404 |
Instruction Following Qwen3-30B-A3B leads
Mistral Large: 67.9 (#191), Qwen3-30B-A3B: 72.0 (#142)
| Benchmark | Mistral Large | Qwen3-30B-A3B |
|---|---|---|
| LMArena Instruction Following | 1249 | 1363 |
| LiveBench Instruction Following | 67.9% | — |
| IFEval | 87.7% | — |
Long Context Mistral Large leads
Mistral Large: 38.3 (#199), Qwen3-30B-A3B: 31.0 (#283)
| Benchmark | Mistral Large | Qwen3-30B-A3B |
|---|---|---|
| LMArena Longer Query | 1261 | 1379 |
| Fiction.LiveBench | — | 40.6% |
Writing & Preference Qwen3-30B-A3B leads
Mistral Large: 40.7 (#242), Qwen3-30B-A3B: 55.6 (#143)
| Benchmark | Mistral Large | Qwen3-30B-A3B |
|---|---|---|
| LMArena Text | 1266 | 1384 |
| LMArena Creative Writing | 1243 | 1317 |
| Short-Story Creative Writing | 69% | 75.3% |
| LMArena Multi-Turn | 1260 | 1378 |
| EQ-Bench Creative Writing | 985 | — |
| WildBench | 80.1% | — |
| LiveBench Language | 39.4% | — |
Frequently asked questions
Is Mistral Large better than Qwen3-30B-A3B?
Qwen3-30B-A3B is the stronger model overall, scoring 38.9 to 31.9 on the Noometry Index.
Which is cheaper, Mistral Large 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 Large lists at $2 and $6.
Is Mistral Large or Qwen3-30B-A3B better for coding?
Qwen3-30B-A3B scores higher on coding benchmarks: 37.5 versus 34.3 in the Noometry coding category.
Which has the bigger context window?
Mistral Large does, with 131K tokens against 41K.
How many benchmarks do Mistral Large and Qwen3-30B-A3B share?
27 benchmarks have published results for both models. Mistral Large has 51 scored results on Noometry and Qwen3-30B-A3B has 32.