Model comparison
DeepSeek V4 Pro vs Mistral 7B
DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 23.0 on the Noometry Index. Mistral 7B costs 4.0× less per token, which makes it the better buy when DeepSeek V4 Pro's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
DeepSeek
Mistral AISummary
- They share 21 benchmarks with published results for both. DeepSeek V4 Pro scores higher in 8 categories and Mistral 7B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek V4 Pro leads 64.8 to 8.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.6% for DeepSeek V4 Pro and 0.3% for Mistral 7B.
- Mistral 7B is cheaper at $0.25 / $0.25 per million input/output tokens, against $0.66 / $1.98 for DeepSeek V4 Pro.
- DeepSeek V4 Pro accepts more context: 1M tokens versus 8K.
Side by side
| DeepSeek V4 Pro | Mistral 7B | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 54.3 | 23.0 |
| Released | 2026-04-24 | 2023-09-27 |
| Weights | Open | Open |
| Context window | 1M | 8K |
| Max output | 393K | 8K |
| Input $ / M tokens | $0.66 | $0.25 |
| Output $ / M tokens | $1.98 | $0.25 |
| Results tracked | 48 | 37 |
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Category by category
Coding DeepSeek V4 Pro leads
DeepSeek V4 Pro: 52.4 (#34), Mistral 7B: 26.4 (#326)
| Benchmark | DeepSeek V4 Pro | Mistral 7B |
|---|---|---|
| LMArena Coding | 1470 | 1082 |
| SWE-bench Verified | 77.6% | — |
| FrontierCode | 28.6% | — |
| LMArena WebDev | 1582 | — |
| SciCode | 51% | — |
| WeirdML | 66.2% | — |
| BigCodeBench Instruct | — | 19.5% |
| BigCodeBench Complete | — | 27.3% |
| ALE-Bench | 1,403 | — |
| HumanEval+ | — | 36% |
| MBPP+ | — | 42.1% |
Agentic & Tool Use Not comparable
DeepSeek V4 Pro: 32.8 (#58), Mistral 7B: —
| Benchmark | DeepSeek V4 Pro | Mistral 7B |
|---|---|---|
| APEX-Agents | 47.3% | — |
| Vending-Bench 2 | 3,285 | — |
Reasoning DeepSeek V4 Pro leads
DeepSeek V4 Pro: 56.5 (#24), Mistral 7B: 13.1 (#336)
| Benchmark | DeepSeek V4 Pro | Mistral 7B |
|---|---|---|
| Chess Puzzles | 47% | 0% |
| LMArena Hard Prompts | 1461 | 1067 |
| DTBench | 93.9% | 42.5% |
| Epoch Capabilities Index | 155.31 | 112.21 |
| ARC-AGI-2 | 61.3% | — |
| Kagi LLM Benchmark | 53.5% | — |
| NYT Connections (extended) | 91.3% | — |
| ARC-AGI-1 | 90.5% | — |
| CritPt | 18% | — |
| Mystery Game Puzzles | 43% | — |
| LMCA | 45.5% | — |
| Surface Evolver Bench | 40% | — |
| Adversarial NLI | — | 47.1% |
| BIG-Bench Hard | — | 56.1% |
| ForecastBench | 56.1 | — |
| HellaSwag | — | 81% |
| PIQA | — | 83% |
| WinoGrande | — | 75.3% |
Math DeepSeek V4 Pro leads
DeepSeek V4 Pro: 64.8 (#30), Mistral 7B: 8.1 (#325)
| Benchmark | DeepSeek V4 Pro | Mistral 7B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.6% | 0.3% |
| LMArena Math | 1455 | 1085 |
| FrontierMath (Tiers 1-3) | 64.6% | — |
| FrontierMath Tier 4 | 26.8% | — |
| MathArena Final-Answer Competitions | 76.6% | — |
| ProofBench | 50% | — |
| MATH Level 5 | — | 3.7% |
| GSM8K | — | 54.4% |
Knowledge DeepSeek V4 Pro leads
DeepSeek V4 Pro: 59.5 (#31), Mistral 7B: 7.4 (#311)
| Benchmark | DeepSeek V4 Pro | Mistral 7B |
|---|---|---|
| GPQA Diamond | 91.7% | 15.2% |
| LMArena Expert | 1464 | 1036 |
| SimpleQA Verified | 52.9% | — |
| Vectara Hallucination Rate | 8.6% | — |
| ARC (AI2) Challenge | — | 78.6% |
| BoolQ | — | 87.4% |
| MMLU | — | 62.5% |
| OpenBookQA | — | 79.8% |
| TriviaQA | — | 75.2% |
Multilingual DeepSeek V4 Pro leads
DeepSeek V4 Pro: 54.4 (#45), Mistral 7B: 25.8 (#283)
| Benchmark | DeepSeek V4 Pro | Mistral 7B |
|---|---|---|
| LMArena Non-English | 1439 | 1012 |
| LMArena Chinese | 1486 | 1009 |
| LMArena French | 1472 | 1037 |
| LMArena German | 1458 | 987 |
| LMArena Japanese | 1445 | 878 |
| LMArena Russian | 1453 | 1018 |
| LMArena Spanish | 1458 | 1026 |
| LMArena Korean | 1447 | — |
Instruction Following DeepSeek V4 Pro leads
DeepSeek V4 Pro: 76.1 (#47), Mistral 7B: 54.2 (#280)
| Benchmark | DeepSeek V4 Pro | Mistral 7B |
|---|---|---|
| LMArena Instruction Following | 1448 | 1060 |
Long Context DeepSeek V4 Pro leads
DeepSeek V4 Pro: 45.0 (#51), Mistral 7B: 32.2 (#271)
| Benchmark | DeepSeek V4 Pro | Mistral 7B |
|---|---|---|
| LMArena Longer Query | 1458 | 1060 |
| CL-bench Life | 13.5% | — |
Writing & Preference DeepSeek V4 Pro leads
DeepSeek V4 Pro: 65.5 (#46), Mistral 7B: 30.7 (#286)
| Benchmark | DeepSeek V4 Pro | Mistral 7B |
|---|---|---|
| LMArena Text | 1451 | 1090 |
| LMArena Creative Writing | 1446 | 1068 |
| LMArena Multi-Turn | 1467 | 1062 |
| EQ-Bench Creative Writing | 1553 | — |
| EQ-Bench 4 | 1166 | — |
Frequently asked questions
Is DeepSeek V4 Pro better than Mistral 7B?
DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 23.0 on the Noometry Index. Mistral 7B costs 4.0× less per token, which makes it the better buy when DeepSeek V4 Pro's lead doesn't matter for your workload.
Which is cheaper, DeepSeek V4 Pro or Mistral 7B?
Mistral 7B is cheaper. It lists at $0.25 per million input tokens and $0.25 per million output tokens; DeepSeek V4 Pro lists at $0.66 and $1.98.
Is DeepSeek V4 Pro or Mistral 7B better for coding?
DeepSeek V4 Pro scores higher on coding benchmarks: 52.4 versus 26.4 in the Noometry coding category.
Which has the bigger context window?
DeepSeek V4 Pro does, with 1M tokens against 8K.
How many benchmarks do DeepSeek V4 Pro and Mistral 7B share?
21 benchmarks have published results for both models. DeepSeek V4 Pro has 48 scored results on Noometry and Mistral 7B has 37.