Model comparison
DeepSeek-R1 vs Mistral Small 3.1
DeepSeek-R1 is the stronger model overall, scoring 42.3 to 31.7 on the Noometry Index. Mistral Small 3.1 costs 2.3× less per token, which makes it the better buy when DeepSeek-R1's lead doesn't matter for your workload.
Last verified . 26 shared benchmarks.
DeepSeek
Mistral AISummary
- They share 26 benchmarks with published results for both. DeepSeek-R1 scores higher in 7 categories and Mistral Small 3.1 in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek-R1 leads 43.8 to 14.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 66.4% for DeepSeek-R1 and 3.9% for Mistral Small 3.1.
- Mistral Small 3.1 is cheaper at $0.35 / $0.56 per million input/output tokens, against $0.50 / $2.15 for DeepSeek-R1.
- DeepSeek-R1 accepts more context: 164K tokens versus 128K.
- Mistral Small 3.1 has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-R1 | Mistral Small 3.1 | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 42.3 | 31.7 |
| Released | 2025-01-20 | 2025-03-17 |
| Weights | Proprietary | Open |
| Context window | 164K | 128K |
| Max output | 64K | 102K |
| Input $ / M tokens | $0.50 | $0.35 |
| Output $ / M tokens | $2.15 | $0.56 |
| Results tracked | 52 | 28 |
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Category by category
Coding DeepSeek-R1 leads
DeepSeek-R1: 46.3 (#68), Mistral Small 3.1: 38.3 (#179)
| Benchmark | DeepSeek-R1 | Mistral Small 3.1 |
|---|---|---|
| LMArena Coding | 1427 | 1309 |
| Aider Polyglot | 71.4% | — |
| SciCode | 35.7% | — |
| WeirdML | 41.6% | — |
| LiveBench Coding | 66.7% | — |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use Not comparable
DeepSeek-R1: 30.7 (#75), Mistral Small 3.1: —
| Benchmark | DeepSeek-R1 | Mistral Small 3.1 |
|---|---|---|
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |
Reasoning Mistral Small 3.1 leads
DeepSeek-R1: 18.6 (#278), Mistral Small 3.1: 19.7 (#254)
| Benchmark | DeepSeek-R1 | Mistral Small 3.1 |
|---|---|---|
| LMArena Hard Prompts | 1416 | 1278 |
| Epoch Capabilities Index | 141.29 | 127.48 |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| Kagi LLM Benchmark | 69.4% | — |
| ARC-AGI-1 | 21.2% | — |
| CritPt | 1.1% | — |
| Chess Puzzles | — | 1% |
| LiveBench Reasoning | 83.2% | — |
| LiveBench Data Analysis | 69.8% | — |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |
Math DeepSeek-R1 leads
DeepSeek-R1: 43.8 (#79), Mistral Small 3.1: 14.7 (#301)
| Benchmark | DeepSeek-R1 | Mistral Small 3.1 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | 3.9% |
| Omni-MATH | 42.4% | 24.8% |
| LMArena Math | 1400 | 1262 |
| LiveBench Math | 80.7% | — |
| MATH Level 5 | 96.6% | — |
Knowledge DeepSeek-R1 leads
DeepSeek-R1: 44.5 (#87), Mistral Small 3.1: 22.6 (#271)
| Benchmark | DeepSeek-R1 | Mistral Small 3.1 |
|---|---|---|
| GPQA Diamond | 76.3% | 41.9% |
| MMLU-Pro | 79.3% | 61% |
| GPQA (HELM) | 66.6% | 39.2% |
| LMArena Expert | 1394 | 1257 |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
Multimodal Not comparable
DeepSeek-R1: —, Mistral Small 3.1: 33.2 (#99)
| Benchmark | DeepSeek-R1 | Mistral Small 3.1 |
|---|---|---|
| LMArena Vision | — | 1136 |
Multilingual DeepSeek-R1 leads
DeepSeek-R1: 52.4 (#85), Mistral Small 3.1: 41.2 (#209)
| Benchmark | DeepSeek-R1 | Mistral Small 3.1 |
|---|---|---|
| LMArena Non-English | 1412 | 1255 |
| LMArena Chinese | 1442 | 1253 |
| LMArena French | 1417 | 1273 |
| LMArena German | 1404 | 1266 |
| LMArena Japanese | 1391 | 1208 |
| LMArena Korean | 1360 | 1206 |
| LMArena Russian | 1423 | 1263 |
| LMArena Spanish | 1411 | 1283 |
Instruction Following DeepSeek-R1 leads
DeepSeek-R1: 72.0 (#143), Mistral Small 3.1: 63.6 (#230)
| Benchmark | DeepSeek-R1 | Mistral Small 3.1 |
|---|---|---|
| IFEval | 78.4% | 75% |
| LMArena Instruction Following | 1382 | 1264 |
| LiveBench Instruction Following | 80.5% | — |
Long Context DeepSeek-R1 leads
DeepSeek-R1: 45.4 (#36), Mistral Small 3.1: 39.5 (#178)
| Benchmark | DeepSeek-R1 | Mistral Small 3.1 |
|---|---|---|
| LMArena Longer Query | 1391 | 1299 |
| Fiction.LiveBench | 75% | — |
Writing & Preference DeepSeek-R1 leads
DeepSeek-R1: 61.4 (#88), Mistral Small 3.1: 37.0 (#259)
| Benchmark | DeepSeek-R1 | Mistral Small 3.1 |
|---|---|---|
| LMArena Text | 1428 | 1277 |
| LMArena Creative Writing | 1405 | 1253 |
| EQ-Bench Creative Writing | 1500 | 761 |
| WildBench | 82.8% | 78.8% |
| LMArena Multi-Turn | 1405 | 1270 |
| Short-Story Creative Writing | 83% | — |
| LiveBench Language | 48.5% | — |
Frequently asked questions
Is DeepSeek-R1 better than Mistral Small 3.1?
DeepSeek-R1 is the stronger model overall, scoring 42.3 to 31.7 on the Noometry Index. Mistral Small 3.1 costs 2.3× 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 3.1?
Mistral Small 3.1 is cheaper. It lists at $0.35 per million input tokens and $0.56 per million output tokens; DeepSeek-R1 lists at $0.50 and $2.15.
Is DeepSeek-R1 or Mistral Small 3.1 better for coding?
DeepSeek-R1 scores higher on coding benchmarks: 46.3 versus 38.3 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-R1 does, with 164K tokens against 128K.
How many benchmarks do DeepSeek-R1 and Mistral Small 3.1 share?
26 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Mistral Small 3.1 has 28.