Model comparison
DeepSeek-V2.5 (Sep 2024) vs Mistral Small 3.2
DeepSeek-V2.5 (Sep 2024) is the stronger model overall, scoring 37.6 to 31.2 on the Noometry Index.
Last verified . 0 shared benchmarks.
DeepSeek
Mistral AISummary
- The widest gap is in math, where DeepSeek-V2.5 (Sep 2024) leads 35.9 to 26.3.
Side by side
| DeepSeek-V2.5 (Sep 2024) | Mistral Small 3.2 | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 37.6 | 31.2 |
| Released | 2024-09-06 | 2025-06-20 |
| Weights | Open | Open |
| Context window | — | 256K |
| Max output | — | 16K |
| Input $ / M tokens | — | $0.0938 |
| Output $ / M tokens | — | $0.25 |
| Results tracked | 22 | 6 |
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Category by category
Coding Not comparable
DeepSeek-V2.5 (Sep 2024): 31.7 (#281), Mistral Small 3.2: —
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Mistral Small 3.2 |
|---|---|---|
| Aider Polyglot | 17.8% | — |
| BigCodeBench Instruct | 48.6% | — |
| LMArena Coding | 1309 | — |
| BigCodeBench Complete | 53.2% | — |
| HumanEval+ | 83.5% | — |
| MBPP+ | 74.1% | — |
Reasoning DeepSeek-V2.5 (Sep 2024) leads
DeepSeek-V2.5 (Sep 2024): 25.6 (#145), Mistral Small 3.2: 18.1 (#287)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Mistral Small 3.2 |
|---|---|---|
| Kagi LLM Benchmark | — | 40.4% |
| Chess Puzzles | — | 1% |
| LMArena Hard Prompts | 1289 | — |
| Epoch Capabilities Index | — | 131.74 |
Math DeepSeek-V2.5 (Sep 2024) leads
DeepSeek-V2.5 (Sep 2024): 35.9 (#177), Mistral Small 3.2: 26.3 (#260)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Mistral Small 3.2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 30.3% |
| LMArena Math | 1288 | — |
Knowledge DeepSeek-V2.5 (Sep 2024) leads
DeepSeek-V2.5 (Sep 2024): 34.8 (#193), Mistral Small 3.2: 26.7 (#256)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Mistral Small 3.2 |
|---|---|---|
| GPQA Diamond | — | 49.1% |
| LMArena Expert | 1266 | — |
Multilingual Not comparable
DeepSeek-V2.5 (Sep 2024): 42.5 (#193), Mistral Small 3.2: —
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Mistral Small 3.2 |
|---|---|---|
| LMArena Non-English | 1273 | — |
| LMArena Chinese | 1318 | — |
| LMArena French | 1289 | — |
| LMArena German | 1258 | — |
| LMArena Japanese | 1228 | — |
| LMArena Korean | 1209 | — |
| LMArena Russian | 1289 | — |
| LMArena Spanish | 1248 | — |
Instruction Following Not comparable
DeepSeek-V2.5 (Sep 2024): 67.5 (#194), Mistral Small 3.2: —
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Mistral Small 3.2 |
|---|---|---|
| LMArena Instruction Following | 1280 | — |
Long Context Not comparable
DeepSeek-V2.5 (Sep 2024): 39.5 (#174), Mistral Small 3.2: —
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Mistral Small 3.2 |
|---|---|---|
| LMArena Longer Query | 1301 | — |
Writing & Preference DeepSeek-V2.5 (Sep 2024) leads
DeepSeek-V2.5 (Sep 2024): 49.8 (#187), Mistral Small 3.2: 45.0 (#224)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Mistral Small 3.2 |
|---|---|---|
| LMArena Text | 1294 | — |
| LMArena Creative Writing | 1285 | — |
| EQ-Bench Creative Writing | — | 1255 |
| LMArena Multi-Turn | 1297 | — |
Frequently asked questions
Is DeepSeek-V2.5 (Sep 2024) better than Mistral Small 3.2?
DeepSeek-V2.5 (Sep 2024) is the stronger model overall, scoring 37.6 to 31.2 on the Noometry Index.
How many benchmarks do DeepSeek-V2.5 (Sep 2024) and Mistral Small 3.2 share?
0 benchmarks have published results for both models. DeepSeek-V2.5 (Sep 2024) has 22 scored results on Noometry and Mistral Small 3.2 has 6.