Model comparison
DeepSeek-V3.1 vs Mistral Large 3
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 39.1 on the Noometry Index.
Last verified . 20 shared benchmarks.
DeepSeek
Mistral AISummary
- They share 20 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 5 categories and Mistral Large 3 in 3 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek-V3.1 leads 27.9 to 15.2.
- The biggest single-benchmark swing is Vectara Hallucination Rate: 5.5% for DeepSeek-V3.1 and 14.5% for Mistral Large 3.
- Mistral Large 3 is cheaper at $0.25 / $0.75 per million input/output tokens, against $0.25 / $0.95 for DeepSeek-V3.1.
- Mistral Large 3 accepts more context: 262K tokens versus 164K.
Side by side
| DeepSeek-V3.1 | Mistral Large 3 | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 42.8 | 39.1 |
| Released | 2025-08-21 | 2025-12-02 |
| Weights | Open | Open |
| Context window | 164K | 262K |
| Max output | 8K | 8K |
| Input $ / M tokens | $0.25 | $0.25 |
| Output $ / M tokens | $0.95 | $0.75 |
| Results tracked | 27 | 24 |
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Category by category
Coding DeepSeek-V3.1 leads
DeepSeek-V3.1: 40.3 (#144), Mistral Large 3: 34.4 (#237)
| Benchmark | DeepSeek-V3.1 | Mistral Large 3 |
|---|---|---|
| LMArena Coding | 1417 | 1448 |
| LMArena WebDev | — | 1230 |
| WeirdML | 38.4% | — |
Reasoning DeepSeek-V3.1 leads
DeepSeek-V3.1: 27.9 (#110), Mistral Large 3: 15.2 (#319)
| Benchmark | DeepSeek-V3.1 | Mistral Large 3 |
|---|---|---|
| Kagi LLM Benchmark | 53.2% | 50.9% |
| LMArena Hard Prompts | 1417 | 1429 |
| SimpleBench | 40% | — |
| NYT Connections (extended) | — | 7.5% |
| Thematic Generalization | — | 23% |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| Epoch Capabilities Index | 139.92 | — |
| ForecastBench | 58 | — |
Math Too close to call
DeepSeek-V3.1: 38.9 (#122), Mistral Large 3: 38.7 (#129)
| Benchmark | DeepSeek-V3.1 | Mistral Large 3 |
|---|---|---|
| LMArena Math | 1420 | 1414 |
Knowledge DeepSeek-V3.1 leads
DeepSeek-V3.1: 43.7 (#90), Mistral Large 3: 36.0 (#177)
| Benchmark | DeepSeek-V3.1 | Mistral Large 3 |
|---|---|---|
| Vectara Hallucination Rate | 5.5% | 14.5% |
| LMArena Expert | 1405 | 1421 |
Multimodal Not comparable
DeepSeek-V3.1: —, Mistral Large 3: 38.2 (#66)
| Benchmark | DeepSeek-V3.1 | Mistral Large 3 |
|---|---|---|
| LMArena Vision | — | 1221 |
Multilingual Too close to call
DeepSeek-V3.1: 51.6 (#106), Mistral Large 3: 52.5 (#84)
| Benchmark | DeepSeek-V3.1 | Mistral Large 3 |
|---|---|---|
| LMArena Non-English | 1400 | 1413 |
| LMArena Chinese | 1469 | 1447 |
| LMArena French | 1447 | 1455 |
| LMArena German | 1411 | 1437 |
| LMArena Japanese | 1378 | 1394 |
| LMArena Korean | 1337 | 1384 |
| LMArena Russian | 1405 | 1411 |
| LMArena Spanish | 1431 | 1440 |
Instruction Following Too close to call
DeepSeek-V3.1: 73.9 (#110), Mistral Large 3: 74.0 (#108)
| Benchmark | DeepSeek-V3.1 | Mistral Large 3 |
|---|---|---|
| LMArena Instruction Following | 1400 | 1403 |
Long Context Mistral Large 3 leads
DeepSeek-V3.1: 36.3 (#232), Mistral Large 3: 43.1 (#105)
| Benchmark | DeepSeek-V3.1 | Mistral Large 3 |
|---|---|---|
| LMArena Longer Query | 1422 | 1413 |
| Fiction.LiveBench | 52.8% | — |
Writing & Preference Too close to call
DeepSeek-V3.1: 60.3 (#98), Mistral Large 3: 60.0 (#101)
| Benchmark | DeepSeek-V3.1 | Mistral Large 3 |
|---|---|---|
| LMArena Text | 1420 | 1428 |
| LMArena Creative Writing | 1401 | 1386 |
| EQ-Bench Creative Writing | 1436 | 1412 |
| LMArena Multi-Turn | 1408 | 1429 |
Frequently asked questions
Is DeepSeek-V3.1 better than Mistral Large 3?
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 39.1 on the Noometry Index.
Which is cheaper, DeepSeek-V3.1 or Mistral Large 3?
Mistral Large 3 is cheaper. It lists at $0.25 per million input tokens and $0.75 per million output tokens; DeepSeek-V3.1 lists at $0.25 and $0.95.
Is DeepSeek-V3.1 or Mistral Large 3 better for coding?
DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 34.4 in the Noometry coding category.
Which has the bigger context window?
Mistral Large 3 does, with 262K tokens against 164K.
How many benchmarks do DeepSeek-V3.1 and Mistral Large 3 share?
20 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Mistral Large 3 has 24.