Model comparison
DeepSeek-V2 (MoE-236B, May 2024) vs Llama 2-7B
Llama 2-7B has enough public results to be ranked (#317); DeepSeek-V2 (MoE-236B, May 2024) does not yet, so treat this comparison as directional.
Last verified . 8 shared benchmarks.
Summary
- They share 8 benchmarks with published results for both. DeepSeek-V2 (MoE-236B, May 2024) scores higher in 1 category and Llama 2-7B 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 29.2.
Side by side
| DeepSeek-V2 (MoE-236B, May 2024) | Llama 2-7B | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 40.3 | 29.1 |
| Released | 2024-05-07 | 2023-07-18 |
| Weights | Open | Open |
| Context window | — | — |
| Max output | — | — |
| Input $ / M tokens | — | — |
| Output $ / M tokens | — | — |
| Results tracked | 10 | 29 |
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Category by category
Coding DeepSeek-V2 (MoE-236B, May 2024) leads
DeepSeek-V2 (MoE-236B, May 2024): 40.4 (#139), Llama 2-7B: 29.2 (#307)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 2-7B |
|---|---|---|
| BigCodeBench Instruct | 48.9% | — |
| LMArena Coding | — | 1002 |
| BigCodeBench Complete | 59.4% | — |
Reasoning Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Llama 2-7B: 15.7 (#312)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 2-7B |
|---|---|---|
| BIG-Bench Hard | 78.8% | 39.2% |
| Epoch Capabilities Index | 124.77 | 99.06 |
| HellaSwag | 87.1% | 77.2% |
| PIQA | 83.9% | 78.8% |
| WinoGrande | 86.3% | 69.2% |
| Chess Puzzles | — | 0% |
| LMArena Hard Prompts | — | 1009 |
| LAMBADA | — | 73.3% |
Math Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Llama 2-7B: 30.7 (#233)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 2-7B |
|---|---|---|
| LMArena Math | — | 1042 |
| GSM8K | — | 16.7% |
Knowledge Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Llama 2-7B: 28.2 (#248)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 2-7B |
|---|---|---|
| ARC (AI2) Challenge | 92.2% | 45.9% |
| MMLU | 78.4% | 45.8% |
| TriviaQA | 80% | 73.7% |
| LMArena Expert | — | 1036 |
| BoolQ | — | 77.9% |
| OpenBookQA | — | 58.6% |
Multimodal Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Llama 2-7B: —
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 2-7B |
|---|---|---|
| ScienceQA | — | 43.1% |
Multilingual Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Llama 2-7B: 23.8 (#293)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 2-7B |
|---|---|---|
| LMArena Non-English | — | 973 |
| LMArena Chinese | — | 973 |
| LMArena French | — | 970 |
| LMArena German | — | 978 |
| LMArena Russian | — | 995 |
| LMArena Spanish | — | 1007 |
Instruction Following Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Llama 2-7B: 50.8 (#298)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 2-7B |
|---|---|---|
| LMArena Instruction Following | — | 1006 |
Long Context Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Llama 2-7B: 30.4 (#287)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 2-7B |
|---|---|---|
| LMArena Longer Query | — | 999 |
Writing & Preference Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Llama 2-7B: 28.0 (#298)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 2-7B |
|---|---|---|
| LMArena Text | — | 1053 |
| LMArena Creative Writing | — | 1033 |
| LMArena Multi-Turn | — | 1029 |
Frequently asked questions
Is DeepSeek-V2 (MoE-236B, May 2024) better than Llama 2-7B?
Llama 2-7B has enough public results to be ranked (#317); DeepSeek-V2 (MoE-236B, May 2024) does not yet, so treat this comparison as directional.
Is DeepSeek-V2 (MoE-236B, May 2024) or Llama 2-7B better for coding?
DeepSeek-V2 (MoE-236B, May 2024) scores higher on coding benchmarks: 40.4 versus 29.2 in the Noometry coding category.
How many benchmarks do DeepSeek-V2 (MoE-236B, May 2024) and Llama 2-7B share?
8 benchmarks have published results for both models. DeepSeek-V2 (MoE-236B, May 2024) has 10 scored results on Noometry and Llama 2-7B has 29.