Model comparison
DeepSeek-R1 vs Mistral Small 3.2
DeepSeek-R1 is the stronger model overall, scoring 42.3 to 31.2 on the Noometry Index. Mistral Small 3.2 costs 6.9× less per token, which makes it the better buy when DeepSeek-R1's lead doesn't matter for your workload.
Last verified . 5 shared benchmarks.
DeepSeek
Mistral AISummary
- They share 5 benchmarks with published results for both. DeepSeek-R1 scores higher in 4 categories and Mistral Small 3.2 in 0 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-R1 leads 44.5 to 26.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 66.4% for DeepSeek-R1 and 30.3% for Mistral Small 3.2.
- Mistral Small 3.2 is cheaper at $0.0938 / $0.25 per million input/output tokens, against $0.50 / $2.15 for DeepSeek-R1.
- Mistral Small 3.2 accepts more context: 256K tokens versus 164K.
- Mistral Small 3.2 has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-R1 | Mistral Small 3.2 | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 42.3 | 31.2 |
| Released | 2025-01-20 | 2025-06-20 |
| Weights | Proprietary | Open |
| Context window | 164K | 256K |
| Max output | 64K | 16K |
| Input $ / M tokens | $0.50 | $0.0938 |
| Output $ / M tokens | $2.15 | $0.25 |
| Results tracked | 52 | 6 |
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Category by category
Coding Not comparable
DeepSeek-R1: 46.3 (#68), Mistral Small 3.2: —
| Benchmark | DeepSeek-R1 | Mistral Small 3.2 |
|---|---|---|
| Aider Polyglot | 71.4% | — |
| SciCode | 35.7% | — |
| WeirdML | 41.6% | — |
| LiveBench Coding | 66.7% | — |
| LMArena Coding | 1427 | — |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use Not comparable
DeepSeek-R1: 30.7 (#75), Mistral Small 3.2: —
| Benchmark | DeepSeek-R1 | Mistral Small 3.2 |
|---|---|---|
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |
Reasoning Too close to call
DeepSeek-R1: 18.6 (#278), Mistral Small 3.2: 18.1 (#287)
| Benchmark | DeepSeek-R1 | Mistral Small 3.2 |
|---|---|---|
| Kagi LLM Benchmark | 69.4% | 40.4% |
| Epoch Capabilities Index | 141.29 | 131.74 |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| ARC-AGI-1 | 21.2% | — |
| CritPt | 1.1% | — |
| Chess Puzzles | — | 1% |
| LiveBench Reasoning | 83.2% | — |
| LMArena Hard Prompts | 1416 | — |
| LiveBench Data Analysis | 69.8% | — |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |
Math DeepSeek-R1 leads
DeepSeek-R1: 43.8 (#79), Mistral Small 3.2: 26.3 (#260)
| Benchmark | DeepSeek-R1 | Mistral Small 3.2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | 30.3% |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| LMArena Math | 1400 | — |
| MATH Level 5 | 96.6% | — |
Knowledge DeepSeek-R1 leads
DeepSeek-R1: 44.5 (#87), Mistral Small 3.2: 26.7 (#256)
| Benchmark | DeepSeek-R1 | Mistral Small 3.2 |
|---|---|---|
| GPQA Diamond | 76.3% | 49.1% |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |
| LMArena Expert | 1394 | — |
Multilingual Not comparable
DeepSeek-R1: 52.4 (#85), Mistral Small 3.2: —
| Benchmark | DeepSeek-R1 | Mistral Small 3.2 |
|---|---|---|
| LMArena Non-English | 1412 | — |
| LMArena Chinese | 1442 | — |
| LMArena French | 1417 | — |
| LMArena German | 1404 | — |
| LMArena Japanese | 1391 | — |
| LMArena Korean | 1360 | — |
| LMArena Russian | 1423 | — |
| LMArena Spanish | 1411 | — |
Instruction Following Not comparable
DeepSeek-R1: 72.0 (#143), Mistral Small 3.2: —
| Benchmark | DeepSeek-R1 | Mistral Small 3.2 |
|---|---|---|
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
| LMArena Instruction Following | 1382 | — |
Long Context Not comparable
DeepSeek-R1: 45.4 (#36), Mistral Small 3.2: —
| Benchmark | DeepSeek-R1 | Mistral Small 3.2 |
|---|---|---|
| Fiction.LiveBench | 75% | — |
| LMArena Longer Query | 1391 | — |
Writing & Preference DeepSeek-R1 leads
DeepSeek-R1: 61.4 (#88), Mistral Small 3.2: 45.0 (#224)
| Benchmark | DeepSeek-R1 | Mistral Small 3.2 |
|---|---|---|
| EQ-Bench Creative Writing | 1500 | 1255 |
| LMArena Text | 1428 | — |
| LMArena Creative Writing | 1405 | — |
| Short-Story Creative Writing | 83% | — |
| WildBench | 82.8% | — |
| LMArena Multi-Turn | 1405 | — |
| LiveBench Language | 48.5% | — |
Frequently asked questions
Is DeepSeek-R1 better than Mistral Small 3.2?
DeepSeek-R1 is the stronger model overall, scoring 42.3 to 31.2 on the Noometry Index. Mistral Small 3.2 costs 6.9× less per token, which makes it the better buy when DeepSeek-R1's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-R1 or Mistral Small 3.2?
Mistral Small 3.2 is cheaper. It lists at $0.0938 per million input tokens and $0.25 per million output tokens; DeepSeek-R1 lists at $0.50 and $2.15.
Which has the bigger context window?
Mistral Small 3.2 does, with 256K tokens against 164K.
How many benchmarks do DeepSeek-R1 and Mistral Small 3.2 share?
5 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Mistral Small 3.2 has 6.