Model comparison
Mistral 7B vs Qwen3.5 397B-A17B
Qwen3.5 397B-A17B is the stronger model overall, scoring 45.7 to 22.9 on the Noometry Index. Mistral 7B costs 5.4× less per token, which makes it the better buy when Qwen3.5 397B-A17B's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- They share 21 benchmarks with published results for both. Mistral 7B scores higher in 0 categories and Qwen3.5 397B-A17B in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3.5 397B-A17B leads 53.3 to 7.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 0.3% for Mistral 7B and 88.9% for Qwen3.5 397B-A17B.
- Mistral 7B is cheaper at $0.25 / $0.25 per million input/output tokens, against $0.60 / $3.60 for Qwen3.5 397B-A17B.
- Qwen3.5 397B-A17B accepts more context: 262K tokens versus 8K.
Side by side
| Mistral 7B | Qwen3.5 397B-A17B | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 22.9 | 45.7 |
| Released | 2023-09-27 | 2026-02-01 |
| Weights | Open | Open |
| Context window | 8K | 262K |
| Max output | 8K | 66K |
| Input $ / M tokens | $0.25 | $0.60 |
| Output $ / M tokens | $0.25 | $3.60 |
| Results tracked | 37 | 36 |
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Category by category
Coding Qwen3.5 397B-A17B leads
Mistral 7B: 25.9 (#321), Qwen3.5 397B-A17B: 41.7 (#108)
| Benchmark | Mistral 7B | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Coding | 1082 | 1464 |
| LMArena WebDev | — | 1400 |
| BigCodeBench Instruct | 19.5% | — |
| BigCodeBench Complete | 27.3% | — |
| HumanEval+ | 36% | — |
| MBPP+ | 42.1% | — |
Agentic & Tool Use Not comparable
Mistral 7B: —, Qwen3.5 397B-A17B: 33.2 (#51)
| Benchmark | Mistral 7B | Qwen3.5 397B-A17B |
|---|---|---|
| APEX-Agents | — | 24.9% |
| τ²-bench Airline | — | 81.5% |
| τ²-bench Banking | — | 9.8% |
| τ²-bench Retail | — | 84.4% |
| τ²-bench Telecom | — | 97.8% |
Reasoning Qwen3.5 397B-A17B leads
Mistral 7B: 12.6 (#331), Qwen3.5 397B-A17B: 33.6 (#62)
| Benchmark | Mistral 7B | Qwen3.5 397B-A17B |
|---|---|---|
| Chess Puzzles | 0% | 13% |
| LMArena Hard Prompts | 1067 | 1448 |
| DTBench | 42.5% | 87.5% |
| Epoch Capabilities Index | 111.97 | 146.64 |
| Kagi LLM Benchmark | — | 73.7% |
| NYT Connections (extended) | — | 58.9% |
| Thematic Generalization | — | 65.1% |
| Mystery Game Puzzles | — | 18% |
| LMCA | — | 37.9% |
| Adversarial NLI | 47.1% | — |
| BIG-Bench Hard | 56.1% | — |
| HellaSwag | 81% | — |
| PIQA | 83% | — |
| WinoGrande | 75.3% | — |
Math Qwen3.5 397B-A17B leads
Mistral 7B: 7.6 (#319), Qwen3.5 397B-A17B: 44.9 (#64)
| Benchmark | Mistral 7B | Qwen3.5 397B-A17B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 0.3% | 88.9% |
| LMArena Math | 1085 | 1455 |
| FrontierMath (Tiers 1-3) | — | 31.2% |
| MATH Level 5 | 3.7% | — |
| GSM8K | 54.4% | — |
Knowledge Qwen3.5 397B-A17B leads
Mistral 7B: 7.3 (#306), Qwen3.5 397B-A17B: 53.3 (#53)
| Benchmark | Mistral 7B | Qwen3.5 397B-A17B |
|---|---|---|
| GPQA Diamond | 15.2% | 86.4% |
| LMArena Expert | 1036 | 1461 |
| ARC (AI2) Challenge | 78.6% | — |
| BoolQ | 87.4% | — |
| MMLU | 62.5% | — |
| OpenBookQA | 79.8% | — |
| TriviaQA | 75.2% | — |
Multimodal Not comparable
Mistral 7B: —, Qwen3.5 397B-A17B: 40.0 (#42)
| Benchmark | Mistral 7B | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Vision | — | 1262 |
Multilingual Qwen3.5 397B-A17B leads
Mistral 7B: 26.0 (#278), Qwen3.5 397B-A17B: 54.0 (#56)
| Benchmark | Mistral 7B | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Non-English | 1012 | 1431 |
| LMArena Chinese | 1009 | 1501 |
| LMArena French | 1036 | 1461 |
| LMArena German | 987 | 1447 |
| LMArena Japanese | 878 | 1427 |
| LMArena Russian | 1018 | 1429 |
| LMArena Spanish | 1026 | 1443 |
| LMArena Korean | — | 1382 |
Instruction Following Qwen3.5 397B-A17B leads
Mistral 7B: 54.5 (#275), Qwen3.5 397B-A17B: 75.2 (#73)
| Benchmark | Mistral 7B | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Instruction Following | 1060 | 1424 |
Long Context Qwen3.5 397B-A17B leads
Mistral 7B: 32.2 (#266), Qwen3.5 397B-A17B: 44.1 (#71)
| Benchmark | Mistral 7B | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Longer Query | 1060 | 1442 |
Writing & Preference Qwen3.5 397B-A17B leads
Mistral 7B: 30.9 (#281), Qwen3.5 397B-A17B: 62.5 (#74)
| Benchmark | Mistral 7B | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Text | 1090 | 1438 |
| LMArena Creative Writing | 1068 | 1401 |
| LMArena Multi-Turn | 1062 | 1447 |
| EQ-Bench Creative Writing | — | 1478 |
Frequently asked questions
Is Mistral 7B better than Qwen3.5 397B-A17B?
Qwen3.5 397B-A17B is the stronger model overall, scoring 45.7 to 22.9 on the Noometry Index. Mistral 7B costs 5.4× less per token, which makes it the better buy when Qwen3.5 397B-A17B's lead doesn't matter for your workload.
Which is cheaper, Mistral 7B or Qwen3.5 397B-A17B?
Mistral 7B is cheaper. It lists at $0.25 per million input tokens and $0.25 per million output tokens; Qwen3.5 397B-A17B lists at $0.60 and $3.60.
Is Mistral 7B or Qwen3.5 397B-A17B better for coding?
Qwen3.5 397B-A17B scores higher on coding benchmarks: 41.7 versus 25.9 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5 397B-A17B does, with 262K tokens against 8K.
How many benchmarks do Mistral 7B and Qwen3.5 397B-A17B share?
21 benchmarks have published results for both models. Mistral 7B has 37 scored results on Noometry and Qwen3.5 397B-A17B has 36.