Model comparison
Pixtral Large vs Qwen2.5 7B Instruct
Pixtral Large is the stronger model overall, scoring 31.7 to 28.6 on the Noometry Index. Qwen2.5 7B Instruct costs 9.8× less per token, which makes it the better buy when Pixtral Large's lead doesn't matter for your workload.
Last verified . 0 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- The widest gap is in writing & preference, where Qwen2.5 7B Instruct leads 48.8 to 32.9.
- Qwen2.5 7B Instruct is cheaper at $0.17 / $0.70 per million input/output tokens, against $2 / $6 for Pixtral Large.
- Qwen2.5 7B Instruct accepts more context: 131K tokens versus 128K.
Side by side
| Pixtral Large | Qwen2.5 7B Instruct | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 31.7 | 28.6 |
| Released | 2024-11-01 | 2024-09 |
| Weights | Open | Open |
| Context window | 128K | 131K |
| Max output | 128K | 8K |
| Input $ / M tokens | $2 | $0.17 |
| Output $ / M tokens | $6 | $0.70 |
| Results tracked | 3 | 15 |
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Category by category
Coding Not comparable
Pixtral Large: —, Qwen2.5 7B Instruct: 35.9 (#204)
| Benchmark | Pixtral Large | Qwen2.5 7B Instruct |
|---|---|---|
| BigCodeBench Instruct | — | 37.6% |
| BigCodeBench Complete | — | 46.1% |
Agentic & Tool Use Not comparable
Pixtral Large: —, Qwen2.5 7B Instruct: 23.5 (#117)
| Benchmark | Pixtral Large | Qwen2.5 7B Instruct |
|---|---|---|
| BALROG | — | 7.8% |
Reasoning Pixtral Large leads
Pixtral Large: 20.9 (#208), Qwen2.5 7B Instruct: 14.3 (#317)
| Benchmark | Pixtral Large | Qwen2.5 7B Instruct |
|---|---|---|
| Chess Puzzles | — | 0% |
| EnigmaEval | 0.8% | — |
| DTBench | — | 47.7% |
| LMCA | — | 6.4% |
| Epoch Capabilities Index | — | 118.44 |
Math Not comparable
Pixtral Large: —, Qwen2.5 7B Instruct: 11.8 (#300)
| Benchmark | Pixtral Large | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 2.5% |
| Omni-MATH | — | 29.4% |
Knowledge Not comparable
Pixtral Large: —, Qwen2.5 7B Instruct: 17.0 (#281)
| Benchmark | Pixtral Large | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | — | 35.5% |
| MMLU-Pro | — | 53.9% |
| GPQA (HELM) | — | 34.1% |
| MMLU | — | 72.9% |
Multimodal Not comparable
Pixtral Large: 30.0 (#105), Qwen2.5 7B Instruct: —
| Benchmark | Pixtral Large | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Vision | 1089 | — |
Instruction Following Not comparable
Pixtral Large: —, Qwen2.5 7B Instruct: 63.4 (#226)
| Benchmark | Pixtral Large | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | — | 74.1% |
Writing & Preference Qwen2.5 7B Instruct leads
Pixtral Large: 32.9 (#273), Qwen2.5 7B Instruct: 48.8 (#194)
| Benchmark | Pixtral Large | Qwen2.5 7B Instruct |
|---|---|---|
| EQ-Bench Creative Writing | 988 | — |
| WildBench | — | 73.1% |
Frequently asked questions
Is Pixtral Large better than Qwen2.5 7B Instruct?
Pixtral Large is the stronger model overall, scoring 31.7 to 28.6 on the Noometry Index. Qwen2.5 7B Instruct costs 9.8× less per token, which makes it the better buy when Pixtral Large's lead doesn't matter for your workload.
Which is cheaper, Pixtral Large or Qwen2.5 7B Instruct?
Qwen2.5 7B Instruct is cheaper. It lists at $0.17 per million input tokens and $0.70 per million output tokens; Pixtral Large lists at $2 and $6.
Which has the bigger context window?
Qwen2.5 7B Instruct does, with 131K tokens against 128K.
How many benchmarks do Pixtral Large and Qwen2.5 7B Instruct share?
0 benchmarks have published results for both models. Pixtral Large has 3 scored results on Noometry and Qwen2.5 7B Instruct has 15.