Model comparison
DeepSeek-V2 (MoE-236B, May 2024) vs Mistral Large
Mistral Large has enough public results to be ranked (#263); DeepSeek-V2 (MoE-236B, May 2024) does not yet, so treat this comparison as directional.
Last verified . 4 shared benchmarks.
DeepSeek
Mistral AISummary
- They share 4 benchmarks with published results for both. DeepSeek-V2 (MoE-236B, May 2024) scores higher in 1 category and Mistral Large in 0 categories; one gap is clear of the uncertainty.
- The widest gap is in coding, where DeepSeek-V2 (MoE-236B, May 2024) leads 40.4 to 34.3.
- The biggest single-benchmark swing is BigCodeBench Complete: 59.4% for DeepSeek-V2 (MoE-236B, May 2024) and 38.3% for Mistral Large.
Side by side
| DeepSeek-V2 (MoE-236B, May 2024) | Mistral Large | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 40.3 | 31.9 |
| Released | 2024-05-07 | 2024-02-26 |
| Weights | Open | Open |
| Context window | — | 131K |
| Max output | — | 16K |
| Input $ / M tokens | — | $2 |
| Output $ / M tokens | — | $6 |
| Results tracked | 10 | 51 |
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Category by category
Coding DeepSeek-V2 (MoE-236B, May 2024) leads
DeepSeek-V2 (MoE-236B, May 2024): 40.4 (#139), Mistral Large: 34.3 (#240)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Mistral Large |
|---|---|---|
| BigCodeBench Instruct | 48.9% | 30% |
| BigCodeBench Complete | 59.4% | 38.3% |
| SciCode | — | 36.2% |
| LiveBench Coding | — | 47.1% |
| LMArena Coding | — | 1277 |
| ALE-Bench | — | 264.7 |
| HumanEval+ | — | 62.2% |
| MBPP+ | — | 59.5% |
Agentic & Tool Use Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Mistral Large: 28.6 (#89)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Mistral Large |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 38.4% |
Reasoning Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Mistral Large: 15.8 (#310)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Mistral Large |
|---|---|---|
| Epoch Capabilities Index | 124.77 | 128.52 |
| SimpleBench | — | 22.5% |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 43.5% |
| LMArena Hard Prompts | — | 1257 |
| DTBench | — | 65.1% |
| LiveBench Data Analysis | — | 50.1% |
| LMCA | — | 16.7% |
| BIG-Bench Hard | 78.8% | — |
| ForecastBench | — | 57.1 |
| HellaSwag | 87.1% | — |
| LiveBench | — | 48.4% |
| PIQA | 83.9% | — |
| WinoGrande | 86.3% | — |
Math Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Mistral Large: 18.2 (#291)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Mistral Large |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 8.5% |
| Omni-MATH | — | 28.1% |
| LiveBench Math | — | 42.5% |
| LMArena Math | — | 1262 |
| MATH Level 5 | — | 50.3% |
| FrontierMath (Feb 2025 set) | — | 0.3% |
Knowledge Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Mistral Large: 30.1 (#230)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Mistral Large |
|---|---|---|
| MMLU | 78.4% | 80% |
| GPQA Diamond | — | 51.3% |
| MMLU-Pro | — | 59.9% |
| Confabulations | — | 21.4% |
| Vectara Hallucination Rate | — | 4.5% |
| GPQA (HELM) | — | 43.5% |
| LMArena Expert | — | 1232 |
| ARC (AI2) Challenge | 92.2% | — |
| TriviaQA | 80% | — |
Multilingual Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Mistral Large: 40.0 (#219)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Mistral Large |
|---|---|---|
| LMArena Non-English | — | 1237 |
| LMArena Chinese | — | 1240 |
| LMArena French | — | 1325 |
| LMArena German | — | 1254 |
| LMArena Japanese | — | 1188 |
| LMArena Korean | — | 1202 |
| LMArena Russian | — | 1257 |
| LMArena Spanish | — | 1268 |
Instruction Following Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Mistral Large: 67.9 (#191)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Mistral Large |
|---|---|---|
| LiveBench Instruction Following | — | 67.9% |
| IFEval | — | 87.7% |
| LMArena Instruction Following | — | 1249 |
Long Context Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Mistral Large: 38.3 (#199)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Mistral Large |
|---|---|---|
| LMArena Longer Query | — | 1261 |
Writing & Preference Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Mistral Large: 40.7 (#242)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Mistral Large |
|---|---|---|
| LMArena Text | — | 1266 |
| LMArena Creative Writing | — | 1243 |
| Short-Story Creative Writing | — | 69% |
| EQ-Bench Creative Writing | — | 985 |
| WildBench | — | 80.1% |
| LMArena Multi-Turn | — | 1260 |
| LiveBench Language | — | 39.4% |
Frequently asked questions
Is DeepSeek-V2 (MoE-236B, May 2024) better than Mistral Large?
Mistral Large has enough public results to be ranked (#263); DeepSeek-V2 (MoE-236B, May 2024) does not yet, so treat this comparison as directional.
Is DeepSeek-V2 (MoE-236B, May 2024) or Mistral Large better for coding?
DeepSeek-V2 (MoE-236B, May 2024) scores higher on coding benchmarks: 40.4 versus 34.3 in the Noometry coding category.
How many benchmarks do DeepSeek-V2 (MoE-236B, May 2024) and Mistral Large share?
4 benchmarks have published results for both models. DeepSeek-V2 (MoE-236B, May 2024) has 10 scored results on Noometry and Mistral Large has 51.