Model comparison
DeepSeek-V3.1-Terminus vs Llama 13b
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 42.6 to 23.9 on the Noometry Index.
Last verified . 8 shared benchmarks.
DeepSeek
MetaSummary
- They share 8 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 6 categories and Llama 13b in 0 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1-Terminus leads 61.3 to 13.9.
Side by side
| DeepSeek-V3.1-Terminus | Llama 13b | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 42.6 | 23.9 |
| Released | 2025-09-22 | 2023-02-24 |
| Weights | Open | Open |
| Context window | 164K | — |
| Max output | 66K | — |
| Input $ / M tokens | $0.30 | — |
| Output $ / M tokens | $1 | — |
| Results tracked | 16 | 21 |
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Category by category
Coding DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 41.5 (#114), Llama 13b: 20.9 (#332)
| Benchmark | DeepSeek-V3.1-Terminus | Llama 13b |
|---|---|---|
| LMArena Coding | 1426 | 683 |
| SciCode | 40.6% | — |
| ALE-Bench | 745.17 | — |
Reasoning DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 25.7 (#126), Llama 13b: 13.4 (#327)
| Benchmark | DeepSeek-V3.1-Terminus | Llama 13b |
|---|---|---|
| LMArena Hard Prompts | 1427 | 728 |
| Kagi LLM Benchmark | 57.4% | — |
| CritPt | 1.7% | — |
| DTBench | 81.3% | — |
| LMCA | 28.6% | — |
| BIG-Bench Hard | — | 37.9% |
| Epoch Capabilities Index | — | 100.09 |
| HellaSwag | — | 79.2% |
| LAMBADA | — | 75.2% |
| PIQA | — | 80.1% |
| WinoGrande | — | 73% |
Math DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 35.7 (#138), Llama 13b: 24.4 (#259)
| Benchmark | DeepSeek-V3.1-Terminus | Llama 13b |
|---|---|---|
| LMArena Math | 1402 | 838 |
| GSM8K | — | 20.6% |
Knowledge Not comparable
DeepSeek-V3.1-Terminus: —, Llama 13b: —
| Benchmark | DeepSeek-V3.1-Terminus | Llama 13b |
|---|---|---|
| ARC (AI2) Challenge | — | 52.7% |
| BoolQ | — | 78.7% |
| MMLU | — | 47.7% |
| OpenBookQA | — | 56.4% |
| TriviaQA | — | 77.9% |
Multimodal Not comparable
DeepSeek-V3.1-Terminus: —, Llama 13b: —
| Benchmark | DeepSeek-V3.1-Terminus | Llama 13b |
|---|---|---|
| ScienceQA | — | 43.3% |
Multilingual DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 52.3 (#87), Llama 13b: 16.8 (#292)
| Benchmark | DeepSeek-V3.1-Terminus | Llama 13b |
|---|---|---|
| LMArena Non-English | 1407 | 819 |
| LMArena Russian | 1434 | — |
Instruction Following DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 74.4 (#96), Llama 13b: 36.9 (#300)
| Benchmark | DeepSeek-V3.1-Terminus | Llama 13b |
|---|---|---|
| LMArena Instruction Following | 1406 | 781 |
Long Context Not comparable
DeepSeek-V3.1-Terminus: 43.5 (#88), Llama 13b: —
| Benchmark | DeepSeek-V3.1-Terminus | Llama 13b |
|---|---|---|
| LMArena Longer Query | 1426 | — |
Writing & Preference DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 61.3 (#87), Llama 13b: 13.9 (#307)
| Benchmark | DeepSeek-V3.1-Terminus | Llama 13b |
|---|---|---|
| LMArena Text | 1420 | 834 |
| LMArena Creative Writing | 1404 | 794 |
| LMArena Multi-Turn | 1411 | 753 |
Frequently asked questions
Is DeepSeek-V3.1-Terminus better than Llama 13b?
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 42.6 to 23.9 on the Noometry Index.
Is DeepSeek-V3.1-Terminus or Llama 13b better for coding?
DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 41.5 versus 20.9 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.1-Terminus and Llama 13b share?
8 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Llama 13b has 21.