Model comparison
DeepSeek V4 Pro vs Mixtral 8x7B
DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 27.1 on the Noometry Index.
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 Mixtral 8x7B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek V4 Pro leads 59.5 to 11.0.
- The biggest single-benchmark swing is GPQA Diamond: 91.7% for DeepSeek V4 Pro and 30.6% for Mixtral 8x7B.
- Mixtral 8x7B is cheaper at $0.70 / $0.70 per million input/output tokens, against $0.66 / $1.98 for DeepSeek V4 Pro.
- DeepSeek V4 Pro accepts more context: 1M tokens versus 32K.
Side by side
| DeepSeek V4 Pro | Mixtral 8x7B | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 54.3 | 27.1 |
| Released | 2026-04-24 | 2023-12-11 |
| Weights | Open | Open |
| Context window | 1M | 32K |
| Max output | 393K | 32K |
| Input $ / M tokens | $0.66 | $0.70 |
| Output $ / M tokens | $1.98 | $0.70 |
| Results tracked | 48 | 38 |
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Category by category
Coding DeepSeek V4 Pro leads
DeepSeek V4 Pro: 52.4 (#34), Mixtral 8x7B: 32.8 (#269)
| Benchmark | DeepSeek V4 Pro | Mixtral 8x7B |
|---|---|---|
| LMArena Coding | 1470 | 1126 |
| SWE-bench Verified | 77.6% | — |
| FrontierCode | 28.6% | — |
| LMArena WebDev | 1582 | — |
| SciCode | 51% | — |
| WeirdML | 66.2% | — |
| ALE-Bench | 1,403 | — |
| HumanEval+ | — | 39.6% |
| MBPP+ | — | 49.7% |
Agentic & Tool Use Not comparable
DeepSeek V4 Pro: 32.8 (#58), Mixtral 8x7B: —
| Benchmark | DeepSeek V4 Pro | Mixtral 8x7B |
|---|---|---|
| APEX-Agents | 47.3% | — |
| Vending-Bench 2 | 3,285 | — |
Reasoning DeepSeek V4 Pro leads
DeepSeek V4 Pro: 56.5 (#24), Mixtral 8x7B: 18.2 (#285)
| Benchmark | DeepSeek V4 Pro | Mixtral 8x7B |
|---|---|---|
| LMArena Hard Prompts | 1461 | 1115 |
| DTBench | 93.9% | 49.6% |
| Epoch Capabilities Index | 155.31 | 118.47 |
| ForecastBench | 56.1 | 56.3 |
| ARC-AGI-2 | 61.3% | — |
| Kagi LLM Benchmark | 53.5% | — |
| NYT Connections (extended) | 91.3% | — |
| ARC-AGI-1 | 90.5% | — |
| CritPt | 18% | — |
| Chess Puzzles | 47% | — |
| Mystery Game Puzzles | 43% | — |
| LMCA | 45.5% | — |
| Surface Evolver Bench | 40% | — |
| Adversarial NLI | — | 55.2% |
| HellaSwag | — | 86.7% |
| PIQA | — | 83.6% |
| WinoGrande | — | 77.2% |
Math DeepSeek V4 Pro leads
DeepSeek V4 Pro: 64.8 (#30), Mixtral 8x7B: 18.8 (#289)
| Benchmark | DeepSeek V4 Pro | Mixtral 8x7B |
|---|---|---|
| LMArena Math | 1455 | 1147 |
| FrontierMath (Tiers 1-3) | 64.6% | — |
| FrontierMath Tier 4 | 26.8% | — |
| MathArena Final-Answer Competitions | 76.6% | — |
| OTIS Mock AIME 2024-2025 | 98.6% | — |
| ProofBench | 50% | — |
| Omni-MATH | — | 10.5% |
| MATH Level 5 | — | 10% |
| GSM8K | — | 74.4% |
Knowledge DeepSeek V4 Pro leads
DeepSeek V4 Pro: 59.5 (#31), Mixtral 8x7B: 11.0 (#301)
| Benchmark | DeepSeek V4 Pro | Mixtral 8x7B |
|---|---|---|
| GPQA Diamond | 91.7% | 30.6% |
| LMArena Expert | 1464 | 1088 |
| SimpleQA Verified | 52.9% | — |
| MMLU-Pro | — | 33.5% |
| Vectara Hallucination Rate | 8.6% | — |
| GPQA (HELM) | — | 29.6% |
| ARC (AI2) Challenge | — | 87.3% |
| MMLU | — | 70.6% |
| OpenBookQA | — | 85.8% |
| TriviaQA | — | 82.2% |
Multilingual DeepSeek V4 Pro leads
DeepSeek V4 Pro: 54.4 (#45), Mixtral 8x7B: 29.6 (#266)
| Benchmark | DeepSeek V4 Pro | Mixtral 8x7B |
|---|---|---|
| LMArena Non-English | 1439 | 1077 |
| LMArena Chinese | 1486 | 1055 |
| LMArena French | 1472 | 1166 |
| LMArena German | 1458 | 1114 |
| LMArena Japanese | 1445 | 931 |
| LMArena Korean | 1447 | 968 |
| LMArena Russian | 1453 | 1090 |
| LMArena Spanish | 1458 | 1111 |
Instruction Following DeepSeek V4 Pro leads
DeepSeek V4 Pro: 76.1 (#47), Mixtral 8x7B: 51.0 (#297)
| Benchmark | DeepSeek V4 Pro | Mixtral 8x7B |
|---|---|---|
| LMArena Instruction Following | 1448 | 1109 |
| IFEval | — | 57.5% |
Long Context DeepSeek V4 Pro leads
DeepSeek V4 Pro: 45.0 (#51), Mixtral 8x7B: 33.4 (#260)
| Benchmark | DeepSeek V4 Pro | Mixtral 8x7B |
|---|---|---|
| LMArena Longer Query | 1458 | 1103 |
| CL-bench Life | 13.5% | — |
Writing & Preference DeepSeek V4 Pro leads
DeepSeek V4 Pro: 65.5 (#46), Mixtral 8x7B: 34.2 (#270)
| Benchmark | DeepSeek V4 Pro | Mixtral 8x7B |
|---|---|---|
| LMArena Text | 1451 | 1132 |
| LMArena Creative Writing | 1446 | 1109 |
| LMArena Multi-Turn | 1467 | 1115 |
| EQ-Bench Creative Writing | 1553 | — |
| WildBench | — | 67.3% |
| EQ-Bench 4 | 1166 | — |
Frequently asked questions
Is DeepSeek V4 Pro better than Mixtral 8x7B?
DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 27.1 on the Noometry Index.
Which is cheaper, DeepSeek V4 Pro or Mixtral 8x7B?
Mixtral 8x7B is cheaper. It lists at $0.70 per million input tokens and $0.70 per million output tokens; DeepSeek V4 Pro lists at $0.66 and $1.98.
Is DeepSeek V4 Pro or Mixtral 8x7B better for coding?
DeepSeek V4 Pro scores higher on coding benchmarks: 52.4 versus 32.8 in the Noometry coding category.
Which has the bigger context window?
DeepSeek V4 Pro does, with 1M tokens against 32K.
How many benchmarks do DeepSeek V4 Pro and Mixtral 8x7B share?
21 benchmarks have published results for both models. DeepSeek V4 Pro has 48 scored results on Noometry and Mixtral 8x7B has 38.