Model comparison
Pixtral Large vs Qwen3.5 27B
Qwen3.5 27B is the stronger model overall, scoring 41.9 to 32.2 on the Noometry Index.
Last verified . 1 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- They share 1 benchmark with published results for both. Pixtral Large scores higher in 0 categories and Qwen3.5 27B in 3 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Qwen3.5 27B leads 59.3 to 32.9.
- Qwen3.5 27B is cheaper at $0.30 / $2.40 per million input/output tokens, against $2 / $6 for Pixtral Large.
- Qwen3.5 27B accepts more context: 262K tokens versus 128K.
Side by side
| Pixtral Large | Qwen3.5 27B | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 32.2 | 41.9 |
| Released | 2024-11-01 | 2026-02-23 |
| Weights | Open | Open |
| Context window | 128K | 262K |
| Max output | 128K | 66K |
| Input $ / M tokens | $2 | $0.30 |
| Output $ / M tokens | $6 | $2.40 |
| Results tracked | 3 | 28 |
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Category by category
Coding Not comparable
Pixtral Large: —, Qwen3.5 27B: 38.9 (#168)
| Benchmark | Pixtral Large | Qwen3.5 27B |
|---|---|---|
| LMArena WebDev | — | 1358 |
| WeirdML | — | 39.5% |
| LMArena Coding | — | 1427 |
| ALE-Bench | — | 349.45 |
Agentic & Tool Use Not comparable
Pixtral Large: —, Qwen3.5 27B: —
| Benchmark | Pixtral Large | Qwen3.5 27B |
|---|---|---|
| Vending-Bench 2 | — | 201.98 |
Reasoning Qwen3.5 27B leads
Pixtral Large: 21.7 (#218), Qwen3.5 27B: 27.5 (#117)
| Benchmark | Pixtral Large | Qwen3.5 27B |
|---|---|---|
| NYT Connections (extended) | — | 47.9% |
| EnigmaEval | 0.8% | — |
| Thematic Generalization | — | 45.5% |
| LMArena Hard Prompts | — | 1414 |
| DTBench | — | 82.4% |
| LMCA | — | 34% |
Math Not comparable
Pixtral Large: —, Qwen3.5 27B: 38.8 (#127)
| Benchmark | Pixtral Large | Qwen3.5 27B |
|---|---|---|
| MathArena Final-Answer Competitions | — | 56.7% |
| LMArena Math | — | 1429 |
Knowledge Not comparable
Pixtral Large: —, Qwen3.5 27B: 38.0 (#150)
| Benchmark | Pixtral Large | Qwen3.5 27B |
|---|---|---|
| Vectara Hallucination Rate | — | 12.1% |
| LMArena Expert | — | 1428 |
Multimodal Qwen3.5 27B leads
Pixtral Large: 30.6 (#111), Qwen3.5 27B: 39.4 (#59)
| Benchmark | Pixtral Large | Qwen3.5 27B |
|---|---|---|
| LMArena Vision | 1089 | 1241 |
Multilingual Not comparable
Pixtral Large: —, Qwen3.5 27B: 50.8 (#115)
| Benchmark | Pixtral Large | Qwen3.5 27B |
|---|---|---|
| LMArena Non-English | — | 1390 |
| LMArena Chinese | — | 1478 |
| LMArena French | — | 1410 |
| LMArena German | — | 1393 |
| LMArena Japanese | — | 1345 |
| LMArena Korean | — | 1358 |
| LMArena Russian | — | 1390 |
| LMArena Spanish | — | 1407 |
Instruction Following Not comparable
Pixtral Large: —, Qwen3.5 27B: 73.5 (#119)
| Benchmark | Pixtral Large | Qwen3.5 27B |
|---|---|---|
| LMArena Instruction Following | — | 1393 |
Long Context Not comparable
Pixtral Large: —, Qwen3.5 27B: 43.1 (#106)
| Benchmark | Pixtral Large | Qwen3.5 27B |
|---|---|---|
| LMArena Longer Query | — | 1413 |
Writing & Preference Qwen3.5 27B leads
Pixtral Large: 32.9 (#278), Qwen3.5 27B: 59.3 (#111)
| Benchmark | Pixtral Large | Qwen3.5 27B |
|---|---|---|
| LMArena Text | — | 1409 |
| LMArena Creative Writing | — | 1362 |
| EQ-Bench Creative Writing | 988 | — |
| LMArena Multi-Turn | — | 1410 |
Frequently asked questions
Is Pixtral Large better than Qwen3.5 27B?
Qwen3.5 27B is the stronger model overall, scoring 41.9 to 32.2 on the Noometry Index.
Which is cheaper, Pixtral Large or Qwen3.5 27B?
Qwen3.5 27B is cheaper. It lists at $0.30 per million input tokens and $2.40 per million output tokens; Pixtral Large lists at $2 and $6.
Which has the bigger context window?
Qwen3.5 27B does, with 262K tokens against 128K.
How many benchmarks do Pixtral Large and Qwen3.5 27B share?
1 benchmark has published results for both models. Pixtral Large has 3 scored results on Noometry and Qwen3.5 27B has 28.