Model comparison
DeepSeek-V3.2-Speciale vs Llama 2-7B
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 29.1 on the Noometry Index.
Last verified . 0 shared benchmarks.
DeepSeek
MetaSummary
- The widest gap is in writing & preference, where DeepSeek-V3.2-Speciale leads 46.0 to 28.0.
Side by side
| DeepSeek-V3.2-Speciale | Llama 2-7B | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 39.7 | 29.1 |
| Released | 2025-12-01 | 2023-07-18 |
| Weights | Open | Open |
| Context window | 128K | — |
| Max output | 128K | — |
| Input $ / M tokens | $0.58 | — |
| Output $ / M tokens | $1.68 | — |
| Results tracked | 3 | 29 |
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Category by category
Coding DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 40.4 (#140), Llama 2-7B: 29.2 (#307)
| Benchmark | DeepSeek-V3.2-Speciale | Llama 2-7B |
|---|---|---|
| WeirdML | 46.7% | — |
| LMArena Coding | — | 1002 |
Reasoning DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 32.9 (#73), Llama 2-7B: 15.7 (#312)
| Benchmark | DeepSeek-V3.2-Speciale | Llama 2-7B |
|---|---|---|
| SimpleBench | 52.6% | — |
| Chess Puzzles | — | 0% |
| LMArena Hard Prompts | — | 1009 |
| BIG-Bench Hard | — | 39.2% |
| Epoch Capabilities Index | — | 99.06 |
| HellaSwag | — | 77.2% |
| LAMBADA | — | 73.3% |
| PIQA | — | 78.8% |
| WinoGrande | — | 69.2% |
Math Not comparable
DeepSeek-V3.2-Speciale: —, Llama 2-7B: 30.7 (#233)
| Benchmark | DeepSeek-V3.2-Speciale | Llama 2-7B |
|---|---|---|
| LMArena Math | — | 1042 |
| GSM8K | — | 16.7% |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, Llama 2-7B: 28.2 (#248)
| Benchmark | DeepSeek-V3.2-Speciale | Llama 2-7B |
|---|---|---|
| LMArena Expert | — | 1036 |
| ARC (AI2) Challenge | — | 45.9% |
| BoolQ | — | 77.9% |
| MMLU | — | 45.8% |
| OpenBookQA | — | 58.6% |
| TriviaQA | — | 73.7% |
Multimodal Not comparable
DeepSeek-V3.2-Speciale: —, Llama 2-7B: —
| Benchmark | DeepSeek-V3.2-Speciale | Llama 2-7B |
|---|---|---|
| ScienceQA | — | 43.1% |
Multilingual Not comparable
DeepSeek-V3.2-Speciale: —, Llama 2-7B: 23.8 (#293)
| Benchmark | DeepSeek-V3.2-Speciale | 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-V3.2-Speciale: —, Llama 2-7B: 50.8 (#298)
| Benchmark | DeepSeek-V3.2-Speciale | Llama 2-7B |
|---|---|---|
| LMArena Instruction Following | — | 1006 |
Long Context Not comparable
DeepSeek-V3.2-Speciale: —, Llama 2-7B: 30.4 (#287)
| Benchmark | DeepSeek-V3.2-Speciale | Llama 2-7B |
|---|---|---|
| LMArena Longer Query | — | 999 |
Writing & Preference DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 46.0 (#222), Llama 2-7B: 28.0 (#298)
| Benchmark | DeepSeek-V3.2-Speciale | Llama 2-7B |
|---|---|---|
| LMArena Text | — | 1053 |
| LMArena Creative Writing | — | 1033 |
| EQ-Bench Creative Writing | 1276 | — |
| LMArena Multi-Turn | — | 1029 |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than Llama 2-7B?
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 29.1 on the Noometry Index.
Is DeepSeek-V3.2-Speciale or Llama 2-7B better for coding?
DeepSeek-V3.2-Speciale scores higher on coding benchmarks: 40.4 versus 29.2 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.2-Speciale and Llama 2-7B share?
0 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Llama 2-7B has 29.