Model comparison
Pixtral Large vs Qwen2.5-Coder-32B
Qwen2.5-Coder-32B is the stronger model overall, scoring 33.4 to 32.2 on the Noometry Index.
Last verified . 0 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- The widest gap is in writing & preference, where Qwen2.5-Coder-32B leads 41.6 to 32.9.
- Qwen2.5-Coder-32B is cheaper at $0.66 / $1 per million input/output tokens, against $2 / $6 for Pixtral Large.
- Pixtral Large accepts more context: 128K tokens versus 33K.
Side by side
| Pixtral Large | Qwen2.5-Coder-32B | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 32.2 | 33.4 |
| Released | 2024-11-01 | 2024-09-18 |
| Weights | Open | Open |
| Context window | 128K | 33K |
| Max output | 128K | 29K |
| Input $ / M tokens | $2 | $0.66 |
| Output $ / M tokens | $6 | $1 |
| Results tracked | 3 | 31 |
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Category by category
Coding Not comparable
Pixtral Large: —, Qwen2.5-Coder-32B: 22.6 (#333)
| Benchmark | Pixtral Large | Qwen2.5-Coder-32B |
|---|---|---|
| SWE-bench Verified (bash only) | — | 9% |
| Aider Polyglot | — | 16.4% |
| BigCodeBench Instruct | — | 49% |
| LiveBench Coding | — | 56.9% |
| LMArena Coding | — | 1276 |
| BigCodeBench Complete | — | 58% |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 77% |
Reasoning Too close to call
Pixtral Large: 21.7 (#218), Qwen2.5-Coder-32B: 21.2 (#225)
| Benchmark | Pixtral Large | Qwen2.5-Coder-32B |
|---|---|---|
| EnigmaEval | 0.8% | — |
| LiveBench Reasoning | — | 42.1% |
| LMArena Hard Prompts | — | 1251 |
| LiveBench Data Analysis | — | 49.9% |
| Epoch Capabilities Index | — | 119.49 |
| HellaSwag | — | 83% |
| LiveBench | — | 46.2% |
| WinoGrande | — | 80.8% |
Math Not comparable
Pixtral Large: —, Qwen2.5-Coder-32B: 33.3 (#204)
| Benchmark | Pixtral Large | Qwen2.5-Coder-32B |
|---|---|---|
| LiveBench Math | — | 46.6% |
| LMArena Math | — | 1251 |
| GSM8K | — | 93% |
Knowledge Not comparable
Pixtral Large: —, Qwen2.5-Coder-32B: 33.4 (#203)
| Benchmark | Pixtral Large | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Expert | — | 1221 |
| ARC (AI2) Challenge | — | 70.5% |
| MMLU | — | 79.1% |
Multimodal Not comparable
Pixtral Large: 30.6 (#111), Qwen2.5-Coder-32B: —
| Benchmark | Pixtral Large | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Vision | 1089 | — |
Multilingual Not comparable
Pixtral Large: —, Qwen2.5-Coder-32B: 37.8 (#235)
| Benchmark | Pixtral Large | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Non-English | — | 1205 |
| LMArena Chinese | — | 1222 |
| LMArena Russian | — | 1228 |
Instruction Following Not comparable
Pixtral Large: —, Qwen2.5-Coder-32B: 61.4 (#245)
| Benchmark | Pixtral Large | Qwen2.5-Coder-32B |
|---|---|---|
| LiveBench Instruction Following | — | 58.7% |
| LMArena Instruction Following | — | 1223 |
Long Context Not comparable
Pixtral Large: —, Qwen2.5-Coder-32B: 38.0 (#208)
| Benchmark | Pixtral Large | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Longer Query | — | 1251 |
Writing & Preference Qwen2.5-Coder-32B leads
Pixtral Large: 32.9 (#278), Qwen2.5-Coder-32B: 41.6 (#240)
| Benchmark | Pixtral Large | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Text | — | 1230 |
| LMArena Creative Writing | — | 1174 |
| EQ-Bench Creative Writing | 988 | — |
| LMArena Multi-Turn | — | 1222 |
| LiveBench Language | — | 23.3% |
Frequently asked questions
Is Pixtral Large better than Qwen2.5-Coder-32B?
Qwen2.5-Coder-32B is the stronger model overall, scoring 33.4 to 32.2 on the Noometry Index.
Which is cheaper, Pixtral Large or Qwen2.5-Coder-32B?
Qwen2.5-Coder-32B is cheaper. It lists at $0.66 per million input tokens and $1 per million output tokens; Pixtral Large lists at $2 and $6.
Which has the bigger context window?
Pixtral Large does, with 128K tokens against 33K.
How many benchmarks do Pixtral Large and Qwen2.5-Coder-32B share?
0 benchmarks have published results for both models. Pixtral Large has 3 scored results on Noometry and Qwen2.5-Coder-32B has 31.