Model comparison
Mistral Large 4 vs Qwen3.5 397B-A17B
Qwen3.5 397B-A17B is the stronger model overall, scoring 46.0 to 43.1 on the Noometry Index.
Last verified . 14 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- They share 14 benchmarks with published results for both. Mistral Large 4 scores higher in 2 categories and Qwen3.5 397B-A17B in 6 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3.5 397B-A17B leads 53.3 to 36.6.
- The biggest single-benchmark swing is NYT Connections (extended): 27.4% for Mistral Large 4 and 58.9% for Qwen3.5 397B-A17B.
- Mistral Large 4 is cheaper at $0.68 / $2.09 per million input/output tokens, against $0.60 / $3.60 for Qwen3.5 397B-A17B.
- Mistral Large 4 accepts more context: 1.05M tokens versus 262K.
- Qwen3.5 397B-A17B has downloadable open weights; the other is API-only.
Side by side
| Mistral Large 4 | Qwen3.5 397B-A17B | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 43.1 | 46.0 |
| Released | 2026-10-06 | 2026-02-01 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 262K |
| Max output | 262K | 66K |
| Input $ / M tokens | $0.68 | $0.60 |
| Output $ / M tokens | $2.09 | $3.60 |
| Results tracked | 15 | 36 |
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Category by category
Coding Mistral Large 4 leads
Mistral Large 4: 48.6 (#57), Qwen3.5 397B-A17B: 42.0 (#114)
| Benchmark | Mistral Large 4 | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena WebDev | 1541 | 1400 |
| LMArena Coding | 1475 | 1465 |
Agentic & Tool Use Not comparable
Mistral Large 4: —, Qwen3.5 397B-A17B: 33.3 (#53)
| Benchmark | Mistral Large 4 | 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 Large 4: 22.5 (#192), Qwen3.5 397B-A17B: 34.5 (#70)
| Benchmark | Mistral Large 4 | Qwen3.5 397B-A17B |
|---|---|---|
| NYT Connections (extended) | 27.4% | 58.9% |
| LMArena Hard Prompts | 1444 | 1448 |
| Kagi LLM Benchmark | — | 73.7% |
| Chess Puzzles | — | 13% |
| Thematic Generalization | — | 65.1% |
| Mystery Game Puzzles | — | 18% |
| DTBench | — | 87.5% |
| LMCA | — | 37.9% |
| Epoch Capabilities Index | — | 146.65 |
Math Qwen3.5 397B-A17B leads
Mistral Large 4: 40.4 (#91), Qwen3.5 397B-A17B: 46.1 (#73)
| Benchmark | Mistral Large 4 | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Math | 1488 | 1454 |
| FrontierMath (Tiers 1-3) | — | 31.2% |
| OTIS Mock AIME 2024-2025 | — | 88.9% |
Knowledge Qwen3.5 397B-A17B leads
Mistral Large 4: 36.6 (#166), Qwen3.5 397B-A17B: 53.3 (#58)
| Benchmark | Mistral Large 4 | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Expert | 1447 | 1462 |
| GPQA Diamond | — | 86.4% |
| SimpleQA Verified | 20% | — |
Multimodal Not comparable
Mistral Large 4: —, Qwen3.5 397B-A17B: 40.7 (#44)
| Benchmark | Mistral Large 4 | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Vision | — | 1263 |
Multilingual Qwen3.5 397B-A17B leads
Mistral Large 4: 52.6 (#82), Qwen3.5 397B-A17B: 53.7 (#59)
| Benchmark | Mistral Large 4 | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Non-English | 1415 | 1430 |
| LMArena Chinese | 1491 | 1500 |
| LMArena Russian | 1414 | 1429 |
| LMArena French | — | 1461 |
| LMArena German | — | 1447 |
| LMArena Japanese | — | 1426 |
| LMArena Korean | — | 1384 |
| LMArena Spanish | — | 1441 |
Instruction Following Too close to call
Mistral Large 4: 75.0 (#76), Qwen3.5 397B-A17B: 75.0 (#77)
| Benchmark | Mistral Large 4 | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Instruction Following | 1424 | 1424 |
Long Context Too close to call
Mistral Large 4: 43.6 (#89), Qwen3.5 397B-A17B: 44.1 (#74)
| Benchmark | Mistral Large 4 | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Longer Query | 1429 | 1442 |
Writing & Preference Qwen3.5 397B-A17B leads
Mistral Large 4: 60.4 (#97), Qwen3.5 397B-A17B: 62.3 (#79)
| Benchmark | Mistral Large 4 | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Text | 1427 | 1438 |
| LMArena Creative Writing | 1361 | 1401 |
| LMArena Multi-Turn | 1424 | 1446 |
| EQ-Bench Creative Writing | — | 1478 |
Frequently asked questions
Is Mistral Large 4 better than Qwen3.5 397B-A17B?
Qwen3.5 397B-A17B is the stronger model overall, scoring 46.0 to 43.1 on the Noometry Index.
Which is cheaper, Mistral Large 4 or Qwen3.5 397B-A17B?
Mistral Large 4 is cheaper. It lists at $0.68 per million input tokens and $2.09 per million output tokens; Qwen3.5 397B-A17B lists at $0.60 and $3.60.
Is Mistral Large 4 or Qwen3.5 397B-A17B better for coding?
Mistral Large 4 scores higher on coding benchmarks: 48.6 versus 42.0 in the Noometry coding category.
Which has the bigger context window?
Mistral Large 4 does, with 1.05M tokens against 262K.
How many benchmarks do Mistral Large 4 and Qwen3.5 397B-A17B share?
14 benchmarks have published results for both models. Mistral Large 4 has 15 scored results on Noometry and Qwen3.5 397B-A17B has 36.