Model comparison
DeepSeek-V3.1 vs Mistral 7B
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 23.0 on the Noometry Index. Mistral 7B costs 1.7× less per token, which makes it the better buy when DeepSeek-V3.1's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
DeepSeek
Mistral AISummary
- They share 18 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 8 categories and Mistral 7B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.1 leads 43.7 to 7.4.
- The biggest single-benchmark swing is DTBench: 82.7% for DeepSeek-V3.1 and 42.5% for Mistral 7B.
- Mistral 7B is cheaper at $0.25 / $0.25 per million input/output tokens, against $0.25 / $0.95 for DeepSeek-V3.1.
- DeepSeek-V3.1 accepts more context: 164K tokens versus 8K.
Side by side
| DeepSeek-V3.1 | Mistral 7B | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 42.8 | 23.0 |
| Released | 2025-08-21 | 2023-09-27 |
| Weights | Open | Open |
| Context window | 164K | 8K |
| Max output | 8K | 8K |
| Input $ / M tokens | $0.25 | $0.25 |
| Output $ / M tokens | $0.95 | $0.25 |
| Results tracked | 27 | 37 |
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Category by category
Coding DeepSeek-V3.1 leads
DeepSeek-V3.1: 40.3 (#144), Mistral 7B: 26.4 (#326)
| Benchmark | DeepSeek-V3.1 | Mistral 7B |
|---|---|---|
| LMArena Coding | 1417 | 1082 |
| WeirdML | 38.4% | — |
| BigCodeBench Instruct | — | 19.5% |
| BigCodeBench Complete | — | 27.3% |
| HumanEval+ | — | 36% |
| MBPP+ | — | 42.1% |
Reasoning DeepSeek-V3.1 leads
DeepSeek-V3.1: 27.9 (#110), Mistral 7B: 13.1 (#336)
| Benchmark | DeepSeek-V3.1 | Mistral 7B |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1067 |
| DTBench | 82.7% | 42.5% |
| Epoch Capabilities Index | 139.92 | 112.21 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| Chess Puzzles | — | 0% |
| LMCA | 24.3% | — |
| Adversarial NLI | — | 47.1% |
| BIG-Bench Hard | — | 56.1% |
| ForecastBench | 58 | — |
| HellaSwag | — | 81% |
| PIQA | — | 83% |
| WinoGrande | — | 75.3% |
Math DeepSeek-V3.1 leads
DeepSeek-V3.1: 38.9 (#122), Mistral 7B: 8.1 (#325)
| Benchmark | DeepSeek-V3.1 | Mistral 7B |
|---|---|---|
| LMArena Math | 1420 | 1085 |
| OTIS Mock AIME 2024-2025 | — | 0.3% |
| MATH Level 5 | — | 3.7% |
| GSM8K | — | 54.4% |
Knowledge DeepSeek-V3.1 leads
DeepSeek-V3.1: 43.7 (#90), Mistral 7B: 7.4 (#311)
| Benchmark | DeepSeek-V3.1 | Mistral 7B |
|---|---|---|
| LMArena Expert | 1405 | 1036 |
| GPQA Diamond | — | 15.2% |
| Vectara Hallucination Rate | 5.5% | — |
| ARC (AI2) Challenge | — | 78.6% |
| BoolQ | — | 87.4% |
| MMLU | — | 62.5% |
| OpenBookQA | — | 79.8% |
| TriviaQA | — | 75.2% |
Multilingual DeepSeek-V3.1 leads
DeepSeek-V3.1: 51.6 (#106), Mistral 7B: 25.8 (#283)
| Benchmark | DeepSeek-V3.1 | Mistral 7B |
|---|---|---|
| LMArena Non-English | 1400 | 1012 |
| LMArena Chinese | 1469 | 1009 |
| LMArena French | 1447 | 1037 |
| LMArena German | 1411 | 987 |
| LMArena Japanese | 1378 | 878 |
| LMArena Russian | 1405 | 1018 |
| LMArena Spanish | 1431 | 1026 |
| LMArena Korean | 1337 | — |
Instruction Following DeepSeek-V3.1 leads
DeepSeek-V3.1: 73.9 (#110), Mistral 7B: 54.2 (#280)
| Benchmark | DeepSeek-V3.1 | Mistral 7B |
|---|---|---|
| LMArena Instruction Following | 1400 | 1060 |
Long Context DeepSeek-V3.1 leads
DeepSeek-V3.1: 36.3 (#232), Mistral 7B: 32.2 (#271)
| Benchmark | DeepSeek-V3.1 | Mistral 7B |
|---|---|---|
| LMArena Longer Query | 1422 | 1060 |
| Fiction.LiveBench | 52.8% | — |
Writing & Preference DeepSeek-V3.1 leads
DeepSeek-V3.1: 60.3 (#98), Mistral 7B: 30.7 (#286)
| Benchmark | DeepSeek-V3.1 | Mistral 7B |
|---|---|---|
| LMArena Text | 1420 | 1090 |
| LMArena Creative Writing | 1401 | 1068 |
| LMArena Multi-Turn | 1408 | 1062 |
| EQ-Bench Creative Writing | 1436 | — |
Frequently asked questions
Is DeepSeek-V3.1 better than Mistral 7B?
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 23.0 on the Noometry Index. Mistral 7B costs 1.7× less per token, which makes it the better buy when DeepSeek-V3.1's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.1 or Mistral 7B?
Mistral 7B is cheaper. It lists at $0.25 per million input tokens and $0.25 per million output tokens; DeepSeek-V3.1 lists at $0.25 and $0.95.
Is DeepSeek-V3.1 or Mistral 7B better for coding?
DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 26.4 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3.1 does, with 164K tokens against 8K.
How many benchmarks do DeepSeek-V3.1 and Mistral 7B share?
18 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Mistral 7B has 37.