Model comparison
Mistral 7B vs Qwen3 235B-A22B
Qwen3 235B-A22B is the stronger model overall, scoring 43.2 to 22.9 on the Noometry Index. Mistral 7B costs 4.9× less per token, which makes it the better buy when Qwen3 235B-A22B's lead doesn't matter for your workload.
Last verified . 22 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- They share 22 benchmarks with published results for both. Mistral 7B scores higher in 0 categories and Qwen3 235B-A22B in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3 235B-A22B leads 49.5 to 7.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 0.3% for Mistral 7B and 86.7% for Qwen3 235B-A22B.
- Mistral 7B is cheaper at $0.25 / $0.25 per million input/output tokens, against $0.70 / $2.80 for Qwen3 235B-A22B.
- Qwen3 235B-A22B accepts more context: 131K tokens versus 8K.
Side by side
| Mistral 7B | Qwen3 235B-A22B | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 22.9 | 43.2 |
| Released | 2023-09-27 | 2025-04 |
| Weights | Open | Open |
| Context window | 8K | 131K |
| Max output | 8K | 16K |
| Input $ / M tokens | $0.25 | $0.70 |
| Output $ / M tokens | $0.25 | $2.80 |
| Results tracked | 37 | 49 |
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Category by category
Coding Qwen3 235B-A22B leads
Mistral 7B: 25.9 (#321), Qwen3 235B-A22B: 43.8 (#72)
| Benchmark | Mistral 7B | Qwen3 235B-A22B |
|---|---|---|
| LMArena Coding | 1082 | 1445 |
| Aider Polyglot | — | 59.6% |
| SciCode | — | 42.4% |
| WeirdML | — | 41% |
| BigCodeBench Instruct | 19.5% | — |
| BigCodeBench Complete | 27.3% | — |
| HumanEval+ | 36% | — |
| MBPP+ | 42.1% | — |
Agentic & Tool Use Not comparable
Mistral 7B: —, Qwen3 235B-A22B: 33.7 (#48)
| Benchmark | Mistral 7B | Qwen3 235B-A22B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 52.1% |
| Vending-Bench 2 | — | -11.34 |
Reasoning Qwen3 235B-A22B leads
Mistral 7B: 12.6 (#331), Qwen3 235B-A22B: 15.8 (#302)
| Benchmark | Mistral 7B | Qwen3 235B-A22B |
|---|---|---|
| Chess Puzzles | 0% | 12% |
| LMArena Hard Prompts | 1067 | 1433 |
| DTBench | 42.5% | 80.3% |
| Epoch Capabilities Index | 111.97 | 143.87 |
| ARC-AGI-2 | — | 1.3% |
| SimpleBench | — | 31% |
| Kagi LLM Benchmark | — | 69.4% |
| ARC-AGI-1 | — | 11% |
| CritPt | — | 0% |
| Mystery Game Puzzles | — | 9% |
| LMCA | — | 29.3% |
| Adversarial NLI | 47.1% | — |
| BIG-Bench Hard | 56.1% | — |
| ForecastBench | — | 59.7 |
| HellaSwag | 81% | — |
| PIQA | 83% | — |
| WinoGrande | 75.3% | — |
Math Qwen3 235B-A22B leads
Mistral 7B: 7.6 (#319), Qwen3 235B-A22B: 48.5 (#51)
| Benchmark | Mistral 7B | Qwen3 235B-A22B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 0.3% | 86.7% |
| LMArena Math | 1085 | 1431 |
| MATH Level 5 | 3.7% | 68.9% |
| Omni-MATH | — | 71.8% |
| FrontierMath (Feb 2025 set) | — | 8.5% |
| FrontierMath Tier 4 (v1) | — | 0% |
| GSM8K | 54.4% | — |
Knowledge Qwen3 235B-A22B leads
Mistral 7B: 7.3 (#306), Qwen3 235B-A22B: 49.5 (#67)
| Benchmark | Mistral 7B | Qwen3 235B-A22B |
|---|---|---|
| GPQA Diamond | 15.2% | 80.1% |
| LMArena Expert | 1036 | 1460 |
| SimpleQA Verified | — | 40.4% |
| MMLU-Pro | — | 84.4% |
| Confabulations | — | 15.6% |
| Vectara Hallucination Rate | — | 9.3% |
| GPQA (HELM) | — | 72.7% |
| ARC (AI2) Challenge | 78.6% | — |
| BoolQ | 87.4% | — |
| MMLU | 62.5% | — |
| OpenBookQA | 79.8% | — |
| TriviaQA | 75.2% | — |
Multilingual Qwen3 235B-A22B leads
Mistral 7B: 26.0 (#278), Qwen3 235B-A22B: 52.5 (#84)
| Benchmark | Mistral 7B | Qwen3 235B-A22B |
|---|---|---|
| LMArena Non-English | 1012 | 1410 |
| LMArena Chinese | 1009 | 1481 |
| LMArena French | 1036 | 1445 |
| LMArena German | 987 | 1432 |
| LMArena Japanese | 878 | 1400 |
| LMArena Russian | 1018 | 1411 |
| LMArena Spanish | 1026 | 1429 |
| LMArena Korean | — | 1391 |
Instruction Following Qwen3 235B-A22B leads
Mistral 7B: 54.5 (#275), Qwen3 235B-A22B: 72.8 (#132)
| Benchmark | Mistral 7B | Qwen3 235B-A22B |
|---|---|---|
| LMArena Instruction Following | 1060 | 1408 |
| IFEval | — | 83.5% |
Long Context Qwen3 235B-A22B leads
Mistral 7B: 32.2 (#266), Qwen3 235B-A22B: 46.1 (#25)
| Benchmark | Mistral 7B | Qwen3 235B-A22B |
|---|---|---|
| LMArena Longer Query | 1060 | 1426 |
| Fiction.LiveBench | — | 75% |
Writing & Preference Qwen3 235B-A22B leads
Mistral 7B: 30.9 (#281), Qwen3 235B-A22B: 59.8 (#103)
| Benchmark | Mistral 7B | Qwen3 235B-A22B |
|---|---|---|
| LMArena Text | 1090 | 1419 |
| LMArena Creative Writing | 1068 | 1384 |
| LMArena Multi-Turn | 1062 | 1432 |
| Short-Story Creative Writing | — | 83% |
| EQ-Bench Creative Writing | — | 1366 |
| WildBench | — | 86.6% |
Frequently asked questions
Is Mistral 7B better than Qwen3 235B-A22B?
Qwen3 235B-A22B is the stronger model overall, scoring 43.2 to 22.9 on the Noometry Index. Mistral 7B costs 4.9× less per token, which makes it the better buy when Qwen3 235B-A22B's lead doesn't matter for your workload.
Which is cheaper, Mistral 7B or Qwen3 235B-A22B?
Mistral 7B is cheaper. It lists at $0.25 per million input tokens and $0.25 per million output tokens; Qwen3 235B-A22B lists at $0.70 and $2.80.
Is Mistral 7B or Qwen3 235B-A22B better for coding?
Qwen3 235B-A22B scores higher on coding benchmarks: 43.8 versus 25.9 in the Noometry coding category.
Which has the bigger context window?
Qwen3 235B-A22B does, with 131K tokens against 8K.
How many benchmarks do Mistral 7B and Qwen3 235B-A22B share?
22 benchmarks have published results for both models. Mistral 7B has 37 scored results on Noometry and Qwen3 235B-A22B has 49.