Model comparison
DeepSeek-V3.2-Speciale vs Llama 2-13B
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.1 to 29.1 on the Noometry Index.
Last verified . 0 shared benchmarks.
DeepSeek
MetaSummary
- The widest gap is in reasoning, where DeepSeek-V3.2-Speciale leads 32.1 to 12.3.
Side by side
| DeepSeek-V3.2-Speciale | Llama 2-13B | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 39.1 | 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 | 32 |
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Category by category
Coding DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 39.9 (#136), Llama 2-13B: 30.4 (#287)
| Benchmark | DeepSeek-V3.2-Speciale | Llama 2-13B |
|---|---|---|
| WeirdML | 46.7% | — |
| LMArena Coding | — | 1062 |
Reasoning DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 32.1 (#65), Llama 2-13B: 12.3 (#333)
| Benchmark | DeepSeek-V3.2-Speciale | Llama 2-13B |
|---|---|---|
| SimpleBench | 52.6% | — |
| Chess Puzzles | — | 0% |
| LMArena Hard Prompts | — | 1051 |
| DTBench | — | 42.2% |
| BIG-Bench Hard | — | 58.2% |
| Epoch Capabilities Index | — | 105.8 |
| HellaSwag | — | 80.7% |
| LAMBADA | — | 76.5% |
| PIQA | — | 80.8% |
| WinoGrande | — | 72.8% |
Math Not comparable
DeepSeek-V3.2-Speciale: —, Llama 2-13B: 28.7 (#226)
| Benchmark | DeepSeek-V3.2-Speciale | Llama 2-13B |
|---|---|---|
| LMArena Math | — | 1065 |
| GSM8K | — | 36.9% |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, Llama 2-13B: 27.5 (#244)
| Benchmark | DeepSeek-V3.2-Speciale | Llama 2-13B |
|---|---|---|
| LMArena Expert | — | 1030 |
| ARC (AI2) Challenge | — | 60.3% |
| BoolQ | — | 82.4% |
| MMLU | — | 55.6% |
| OpenBookQA | — | 57% |
| TriviaQA | — | 79.6% |
Multimodal Not comparable
DeepSeek-V3.2-Speciale: —, Llama 2-13B: —
| Benchmark | DeepSeek-V3.2-Speciale | Llama 2-13B |
|---|---|---|
| ScienceQA | — | 55.8% |
Multilingual Not comparable
DeepSeek-V3.2-Speciale: —, Llama 2-13B: 26.7 (#274)
| Benchmark | DeepSeek-V3.2-Speciale | Llama 2-13B |
|---|---|---|
| LMArena Non-English | — | 1024 |
| LMArena Chinese | — | 1001 |
| LMArena French | — | 1043 |
| LMArena German | — | 1009 |
| LMArena Japanese | — | 894 |
| LMArena Korean | — | 953 |
| LMArena Russian | — | 1055 |
| LMArena Spanish | — | 1086 |
Instruction Following Not comparable
DeepSeek-V3.2-Speciale: —, Llama 2-13B: 53.5 (#282)
| Benchmark | DeepSeek-V3.2-Speciale | Llama 2-13B |
|---|---|---|
| LMArena Instruction Following | — | 1046 |
Long Context Not comparable
DeepSeek-V3.2-Speciale: —, Llama 2-13B: 32.3 (#264)
| Benchmark | DeepSeek-V3.2-Speciale | Llama 2-13B |
|---|---|---|
| LMArena Longer Query | — | 1064 |
Writing & Preference DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 46.1 (#217), Llama 2-13B: 30.0 (#284)
| Benchmark | DeepSeek-V3.2-Speciale | Llama 2-13B |
|---|---|---|
| LMArena Text | — | 1085 |
| LMArena Creative Writing | — | 1047 |
| EQ-Bench Creative Writing | 1276 | — |
| LMArena Multi-Turn | — | 1050 |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than Llama 2-13B?
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.1 to 29.1 on the Noometry Index.
Is DeepSeek-V3.2-Speciale or Llama 2-13B better for coding?
DeepSeek-V3.2-Speciale scores higher on coding benchmarks: 39.9 versus 30.4 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.2-Speciale and Llama 2-13B share?
0 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Llama 2-13B has 32.