Model comparison
DeepSeek-V3.1-Terminus vs Mistral 7B
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 42.6 to 22.9 on the Noometry Index. Mistral 7B costs 1.9× less per token, which makes it the better buy when DeepSeek-V3.1-Terminus's lead doesn't matter for your workload.
Last verified . 11 shared benchmarks.
DeepSeek
Mistral AISummary
- They share 11 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 7 categories and Mistral 7B in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1-Terminus leads 61.3 to 30.9.
- The biggest single-benchmark swing is DTBench: 81.3% for DeepSeek-V3.1-Terminus and 42.5% for Mistral 7B.
- Mistral 7B is cheaper at $0.25 / $0.25 per million input/output tokens, against $0.30 / $1 for DeepSeek-V3.1-Terminus.
- DeepSeek-V3.1-Terminus accepts more context: 164K tokens versus 8K.
Side by side
| DeepSeek-V3.1-Terminus | Mistral 7B | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 42.6 | 22.9 |
| Released | 2025-09-22 | 2023-09-27 |
| Weights | Open | Open |
| Context window | 164K | 8K |
| Max output | 66K | 8K |
| Input $ / M tokens | $0.30 | $0.25 |
| Output $ / M tokens | $1 | $0.25 |
| Results tracked | 16 | 37 |
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Category by category
Coding DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 41.5 (#114), Mistral 7B: 25.9 (#321)
| Benchmark | DeepSeek-V3.1-Terminus | Mistral 7B |
|---|---|---|
| LMArena Coding | 1426 | 1082 |
| SciCode | 40.6% | — |
| BigCodeBench Instruct | — | 19.5% |
| BigCodeBench Complete | — | 27.3% |
| ALE-Bench | 745.17 | — |
| HumanEval+ | — | 36% |
| MBPP+ | — | 42.1% |
Reasoning DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 25.7 (#126), Mistral 7B: 12.6 (#331)
| Benchmark | DeepSeek-V3.1-Terminus | Mistral 7B |
|---|---|---|
| LMArena Hard Prompts | 1427 | 1067 |
| DTBench | 81.3% | 42.5% |
| Kagi LLM Benchmark | 57.4% | — |
| CritPt | 1.7% | — |
| Chess Puzzles | — | 0% |
| LMCA | 28.6% | — |
| Adversarial NLI | — | 47.1% |
| BIG-Bench Hard | — | 56.1% |
| Epoch Capabilities Index | — | 111.97 |
| HellaSwag | — | 81% |
| PIQA | — | 83% |
| WinoGrande | — | 75.3% |
Math DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 35.7 (#138), Mistral 7B: 7.6 (#319)
| Benchmark | DeepSeek-V3.1-Terminus | Mistral 7B |
|---|---|---|
| LMArena Math | 1402 | 1085 |
| OTIS Mock AIME 2024-2025 | — | 0.3% |
| MATH Level 5 | — | 3.7% |
| GSM8K | — | 54.4% |
Knowledge Not comparable
DeepSeek-V3.1-Terminus: —, Mistral 7B: 7.3 (#306)
| Benchmark | DeepSeek-V3.1-Terminus | Mistral 7B |
|---|---|---|
| GPQA Diamond | — | 15.2% |
| LMArena Expert | — | 1036 |
| ARC (AI2) Challenge | — | 78.6% |
| BoolQ | — | 87.4% |
| MMLU | — | 62.5% |
| OpenBookQA | — | 79.8% |
| TriviaQA | — | 75.2% |
Multilingual DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 52.3 (#87), Mistral 7B: 26.0 (#278)
| Benchmark | DeepSeek-V3.1-Terminus | Mistral 7B |
|---|---|---|
| LMArena Non-English | 1407 | 1012 |
| LMArena Russian | 1434 | 1018 |
| LMArena Chinese | — | 1009 |
| LMArena French | — | 1036 |
| LMArena German | — | 987 |
| LMArena Japanese | — | 878 |
| LMArena Spanish | — | 1026 |
Instruction Following DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 74.4 (#96), Mistral 7B: 54.5 (#275)
| Benchmark | DeepSeek-V3.1-Terminus | Mistral 7B |
|---|---|---|
| LMArena Instruction Following | 1406 | 1060 |
Long Context DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 43.5 (#88), Mistral 7B: 32.2 (#266)
| Benchmark | DeepSeek-V3.1-Terminus | Mistral 7B |
|---|---|---|
| LMArena Longer Query | 1426 | 1060 |
Writing & Preference DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 61.3 (#87), Mistral 7B: 30.9 (#281)
| Benchmark | DeepSeek-V3.1-Terminus | Mistral 7B |
|---|---|---|
| LMArena Text | 1420 | 1090 |
| LMArena Creative Writing | 1404 | 1068 |
| LMArena Multi-Turn | 1411 | 1062 |
Frequently asked questions
Is DeepSeek-V3.1-Terminus better than Mistral 7B?
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 42.6 to 22.9 on the Noometry Index. Mistral 7B costs 1.9× less per token, which makes it the better buy when DeepSeek-V3.1-Terminus's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.1-Terminus 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-Terminus lists at $0.30 and $1.
Is DeepSeek-V3.1-Terminus or Mistral 7B better for coding?
DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 41.5 versus 25.9 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3.1-Terminus does, with 164K tokens against 8K.
How many benchmarks do DeepSeek-V3.1-Terminus and Mistral 7B share?
11 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Mistral 7B has 37.