Model comparison
Mistral Large vs Qwen2.5 32B Instruct
Mistral Large is the stronger model overall, scoring 31.9 to 30.1 on the Noometry Index. Qwen2.5 32B Instruct costs 2.4× less per token, which makes it the better buy when Mistral Large's lead doesn't matter for your workload.
Last verified . 6 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- They share 6 benchmarks with published results for both. Mistral Large scores higher in 2 categories and Qwen2.5 32B Instruct in 2 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Mistral Large leads 30.1 to 24.9.
- The biggest single-benchmark swing is BigCodeBench Instruct: 30% for Mistral Large and 45% for Qwen2.5 32B Instruct.
- Qwen2.5 32B Instruct is cheaper at $0.70 / $2.80 per million input/output tokens, against $2 / $6 for Mistral Large.
Side by side
| Mistral Large | Qwen2.5 32B Instruct | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 31.9 | 30.1 |
| Released | 2024-02-26 | 2024-09 |
| Weights | Open | Open |
| Context window | 131K | 131K |
| Max output | 16K | 8K |
| Input $ / M tokens | $2 | $0.70 |
| Output $ / M tokens | $6 | $2.80 |
| Results tracked | 51 | 7 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Qwen2.5 32B Instruct leads
Mistral Large: 34.3 (#240), Qwen2.5 32B Instruct: 38.7 (#169)
| Benchmark | Mistral Large | Qwen2.5 32B Instruct |
|---|---|---|
| BigCodeBench Instruct | 30% | 45% |
| BigCodeBench Complete | 38.3% | 52.3% |
| SciCode | 36.2% | — |
| LiveBench Coding | 47.1% | — |
| LMArena Coding | 1277 | — |
| ALE-Bench | 264.7 | — |
| HumanEval+ | 62.2% | — |
| MBPP+ | 59.5% | — |
Agentic & Tool Use Not comparable
Mistral Large: 28.6 (#89), Qwen2.5 32B Instruct: —
| Benchmark | Mistral Large | Qwen2.5 32B Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | 38.4% | — |
Reasoning Qwen2.5 32B Instruct leads
Mistral Large: 15.8 (#310), Qwen2.5 32B Instruct: 19.2 (#266)
| Benchmark | Mistral Large | Qwen2.5 32B Instruct |
|---|---|---|
| Epoch Capabilities Index | 128.52 | 128.52 |
| SimpleBench | 22.5% | — |
| CritPt | 0% | — |
| Chess Puzzles | — | 0% |
| LiveBench Reasoning | 43.5% | — |
| LMArena Hard Prompts | 1257 | — |
| DTBench | 65.1% | — |
| LiveBench Data Analysis | 50.1% | — |
| LMCA | 16.7% | — |
| ForecastBench | 57.1 | — |
| LiveBench | 48.4% | — |
Math Mistral Large leads
Mistral Large: 18.2 (#291), Qwen2.5 32B Instruct: 16.2 (#296)
| Benchmark | Mistral Large | Qwen2.5 32B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 8.5% | 7.4% |
| MATH Level 5 | 50.3% | 56.1% |
| Omni-MATH | 28.1% | — |
| LiveBench Math | 42.5% | — |
| LMArena Math | 1262 | — |
| FrontierMath (Feb 2025 set) | 0.3% | — |
Knowledge Mistral Large leads
Mistral Large: 30.1 (#230), Qwen2.5 32B Instruct: 24.9 (#266)
| Benchmark | Mistral Large | Qwen2.5 32B Instruct |
|---|---|---|
| GPQA Diamond | 51.3% | 46.1% |
| MMLU-Pro | 59.9% | — |
| Confabulations | 21.4% | — |
| Vectara Hallucination Rate | 4.5% | — |
| GPQA (HELM) | 43.5% | — |
| LMArena Expert | 1232 | — |
| MMLU | 80% | — |
Multilingual Not comparable
Mistral Large: 40.0 (#219), Qwen2.5 32B Instruct: —
| Benchmark | Mistral Large | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Non-English | 1237 | — |
| LMArena Chinese | 1240 | — |
| LMArena French | 1325 | — |
| LMArena German | 1254 | — |
| LMArena Japanese | 1188 | — |
| LMArena Korean | 1202 | — |
| LMArena Russian | 1257 | — |
| LMArena Spanish | 1268 | — |
Instruction Following Not comparable
Mistral Large: 67.9 (#191), Qwen2.5 32B Instruct: —
| Benchmark | Mistral Large | Qwen2.5 32B Instruct |
|---|---|---|
| LiveBench Instruction Following | 67.9% | — |
| IFEval | 87.7% | — |
| LMArena Instruction Following | 1249 | — |
Long Context Not comparable
Mistral Large: 38.3 (#199), Qwen2.5 32B Instruct: —
| Benchmark | Mistral Large | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Longer Query | 1261 | — |
Writing & Preference Not comparable
Mistral Large: 40.7 (#242), Qwen2.5 32B Instruct: —
| Benchmark | Mistral Large | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Text | 1266 | — |
| LMArena Creative Writing | 1243 | — |
| Short-Story Creative Writing | 69% | — |
| EQ-Bench Creative Writing | 985 | — |
| WildBench | 80.1% | — |
| LMArena Multi-Turn | 1260 | — |
| LiveBench Language | 39.4% | — |
Frequently asked questions
Is Mistral Large better than Qwen2.5 32B Instruct?
Mistral Large is the stronger model overall, scoring 31.9 to 30.1 on the Noometry Index. Qwen2.5 32B Instruct costs 2.4× less per token, which makes it the better buy when Mistral Large's lead doesn't matter for your workload.
Which is cheaper, Mistral Large or Qwen2.5 32B Instruct?
Qwen2.5 32B Instruct is cheaper. It lists at $0.70 per million input tokens and $2.80 per million output tokens; Mistral Large lists at $2 and $6.
Is Mistral Large or Qwen2.5 32B Instruct better for coding?
Qwen2.5 32B Instruct scores higher on coding benchmarks: 38.7 versus 34.3 in the Noometry coding category.
Which has the bigger context window?
Both accept 131K tokens.
How many benchmarks do Mistral Large and Qwen2.5 32B Instruct share?
6 benchmarks have published results for both models. Mistral Large has 51 scored results on Noometry and Qwen2.5 32B Instruct has 7.