Model comparison
Mistral 7B vs Qwen3.5-Flash
Qwen3.5-Flash is the stronger model overall, scoring 42.5 to 23.0 on the Noometry Index.
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-Flash in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3.5-Flash leads 43.2 to 7.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 0.3% for Mistral 7B and 84.4% for Qwen3.5-Flash.
- Qwen3.5-Flash is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.25 / $0.25 for Mistral 7B.
- Qwen3.5-Flash accepts more context: 1M tokens versus 8K.
- Mistral 7B has downloadable open weights; the other is API-only.
Side by side
| Mistral 7B | Qwen3.5-Flash | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 23.0 | 42.5 |
| Released | 2023-09-27 | 2026-02-23 |
| Weights | Open | Proprietary |
| Context window | 8K | 1M |
| Max output | 8K | 66K |
| Input $ / M tokens | $0.25 | $0.10 |
| Output $ / M tokens | $0.25 | $0.40 |
| Results tracked | 37 | 32 |
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Category by category
Coding Qwen3.5-Flash leads
Mistral 7B: 26.4 (#326), Qwen3.5-Flash: 34.2 (#242)
| Benchmark | Mistral 7B | Qwen3.5-Flash |
|---|---|---|
| LMArena Coding | 1082 | 1412 |
| LMArena WebDev | — | 1244 |
| BigCodeBench Instruct | 19.5% | — |
| BigCodeBench Complete | 27.3% | — |
| ALE-Bench | — | 221.8 |
| HumanEval+ | 36% | — |
| MBPP+ | 42.1% | — |
Agentic & Tool Use Not comparable
Mistral 7B: —, Qwen3.5-Flash: —
| Benchmark | Mistral 7B | Qwen3.5-Flash |
|---|---|---|
| Vending-Bench 2 | — | 462.69 |
Reasoning Qwen3.5-Flash leads
Mistral 7B: 13.1 (#336), Qwen3.5-Flash: 33.7 (#72)
| Benchmark | Mistral 7B | Qwen3.5-Flash |
|---|---|---|
| Chess Puzzles | 0% | 21% |
| LMArena Hard Prompts | 1067 | 1403 |
| DTBench | 42.5% | 82.9% |
| Epoch Capabilities Index | 112.21 | 143.98 |
| Mystery Game Puzzles | — | 20% |
| LMCA | — | 29.1% |
| Adversarial NLI | 47.1% | — |
| BIG-Bench Hard | 56.1% | — |
| HellaSwag | 81% | — |
| PIQA | 83% | — |
| WinoGrande | 75.3% | — |
Math Qwen3.5-Flash leads
Mistral 7B: 8.1 (#325), Qwen3.5-Flash: 37.4 (#158)
| Benchmark | Mistral 7B | Qwen3.5-Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 0.3% | 84.4% |
| LMArena Math | 1085 | 1407 |
| FrontierMath (Tiers 1-3) | — | 18.2% |
| MATH Level 5 | 3.7% | — |
| FrontierMath (Feb 2025 set) | — | 6.2% |
| FrontierMath Tier 4 (v1) | — | 0% |
| GSM8K | 54.4% | — |
Knowledge Qwen3.5-Flash leads
Mistral 7B: 7.4 (#311), Qwen3.5-Flash: 43.2 (#93)
| Benchmark | Mistral 7B | Qwen3.5-Flash |
|---|---|---|
| GPQA Diamond | 15.2% | 82.3% |
| LMArena Expert | 1036 | 1407 |
| SimpleQA Verified | — | 20.3% |
| Vectara Hallucination Rate | — | 10.5% |
| ARC (AI2) Challenge | 78.6% | — |
| BoolQ | 87.4% | — |
| MMLU | 62.5% | — |
| OpenBookQA | 79.8% | — |
| TriviaQA | 75.2% | — |
Multilingual Qwen3.5-Flash leads
Mistral 7B: 25.8 (#283), Qwen3.5-Flash: 50.5 (#121)
| Benchmark | Mistral 7B | Qwen3.5-Flash |
|---|---|---|
| LMArena Non-English | 1012 | 1385 |
| LMArena Chinese | 1009 | 1446 |
| LMArena French | 1037 | 1412 |
| LMArena German | 987 | 1390 |
| LMArena Japanese | 878 | 1368 |
| LMArena Russian | 1018 | 1379 |
| LMArena Spanish | 1026 | 1400 |
| LMArena Korean | — | 1344 |
Instruction Following Qwen3.5-Flash leads
Mistral 7B: 54.2 (#280), Qwen3.5-Flash: 72.6 (#139)
| Benchmark | Mistral 7B | Qwen3.5-Flash |
|---|---|---|
| LMArena Instruction Following | 1060 | 1374 |
Long Context Qwen3.5-Flash leads
Mistral 7B: 32.2 (#271), Qwen3.5-Flash: 42.4 (#124)
| Benchmark | Mistral 7B | Qwen3.5-Flash |
|---|---|---|
| LMArena Longer Query | 1060 | 1392 |
Writing & Preference Qwen3.5-Flash leads
Mistral 7B: 30.7 (#286), Qwen3.5-Flash: 57.9 (#122)
| Benchmark | Mistral 7B | Qwen3.5-Flash |
|---|---|---|
| LMArena Text | 1090 | 1397 |
| LMArena Creative Writing | 1068 | 1343 |
| LMArena Multi-Turn | 1062 | 1393 |
Frequently asked questions
Is Mistral 7B better than Qwen3.5-Flash?
Qwen3.5-Flash is the stronger model overall, scoring 42.5 to 23.0 on the Noometry Index.
Which is cheaper, Mistral 7B or Qwen3.5-Flash?
Qwen3.5-Flash is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; Mistral 7B lists at $0.25 and $0.25.
Is Mistral 7B or Qwen3.5-Flash better for coding?
Qwen3.5-Flash scores higher on coding benchmarks: 34.2 versus 26.4 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5-Flash does, with 1M tokens against 8K.
How many benchmarks do Mistral 7B and Qwen3.5-Flash share?
21 benchmarks have published results for both models. Mistral 7B has 37 scored results on Noometry and Qwen3.5-Flash has 32.