# DeepSeek-V3.1-Terminus vs Llama 2-7B

> DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 29.1 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-terminus-vs-llama-2-7b
- Last updated: 2026-10-12
- Shared benchmarks: 10

## Summary

- They share 10 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 7 categories and Llama 2-7B in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1-Terminus leads 61.0 to 28.0.

## Snapshot

| | DeepSeek-V3.1-Terminus | Llama 2-7B |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 43.1 | 29.1 |
| Rank | 97 | 317 |
| Context | 164K | — |
| Input $/M | $0.27 | — |
| Output $/M | $1 | — |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1-Terminus: 42.0 (#113)
- Llama 2-7B: 29.2 (#307)

| Benchmark | DeepSeek-V3.1-Terminus | Llama 2-7B |
|---|---|---|
| LMArena Coding | 1426 | 1002 |
| SciCode | 40.6% | — |
| ALE-Bench | 745.17 | — |

## Reasoning

- DeepSeek-V3.1-Terminus: 26.4 (#133)
- Llama 2-7B: 15.7 (#312)

| Benchmark | DeepSeek-V3.1-Terminus | Llama 2-7B |
|---|---|---|
| LMArena Hard Prompts | 1426 | 1009 |
| Kagi LLM Benchmark | 57.4% | — |
| CritPt | 1.7% | — |
| Chess Puzzles | — | 0% |
| DTBench | 81.3% | — |
| LMCA | 28.6% | — |
| BIG-Bench Hard | — | 39.2% |
| Epoch Capabilities Index | — | 99.06 |
| HellaSwag | — | 77.2% |
| LAMBADA | — | 73.3% |
| PIQA | — | 78.8% |
| WinoGrande | — | 69.2% |

## Math

- DeepSeek-V3.1-Terminus: 38.5 (#137)
- Llama 2-7B: 30.7 (#233)

| Benchmark | DeepSeek-V3.1-Terminus | Llama 2-7B |
|---|---|---|
| LMArena Math | 1402 | 1042 |
| GSM8K | — | 16.7% |

## Knowledge

- DeepSeek-V3.1-Terminus: —
- Llama 2-7B: 28.2 (#248)

| Benchmark | DeepSeek-V3.1-Terminus | Llama 2-7B |
|---|---|---|
| LMArena Expert | — | 1036 |
| ARC (AI2) Challenge | — | 45.9% |
| BoolQ | — | 77.9% |
| MMLU | — | 45.8% |
| OpenBookQA | — | 58.6% |
| TriviaQA | — | 73.7% |

## Multimodal

- DeepSeek-V3.1-Terminus: —
- Llama 2-7B: —

| Benchmark | DeepSeek-V3.1-Terminus | Llama 2-7B |
|---|---|---|
| ScienceQA | — | 43.1% |

## Multilingual

- DeepSeek-V3.1-Terminus: 52.1 (#92)
- Llama 2-7B: 23.8 (#293)

| Benchmark | DeepSeek-V3.1-Terminus | Llama 2-7B |
|---|---|---|
| LMArena Non-English | 1407 | 973 |
| LMArena Russian | 1436 | 995 |
| LMArena Chinese | — | 973 |
| LMArena French | — | 970 |
| LMArena German | — | 978 |
| LMArena Spanish | — | 1007 |

## Instruction Following

- DeepSeek-V3.1-Terminus: 74.0 (#106)
- Llama 2-7B: 50.8 (#298)

| Benchmark | DeepSeek-V3.1-Terminus | Llama 2-7B |
|---|---|---|
| LMArena Instruction Following | 1404 | 1006 |

## Long Context

- DeepSeek-V3.1-Terminus: 43.4 (#97)
- Llama 2-7B: 30.4 (#287)

| Benchmark | DeepSeek-V3.1-Terminus | Llama 2-7B |
|---|---|---|
| LMArena Longer Query | 1421 | 999 |

## Writing & Preference

- DeepSeek-V3.1-Terminus: 61.0 (#92)
- Llama 2-7B: 28.0 (#298)

| Benchmark | DeepSeek-V3.1-Terminus | Llama 2-7B |
|---|---|---|
| LMArena Text | 1419 | 1053 |
| LMArena Creative Writing | 1403 | 1033 |
| LMArena Multi-Turn | 1411 | 1029 |

## FAQ

### Is DeepSeek-V3.1-Terminus better than Llama 2-7B?

DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 29.1 on the Noometry Index.

### Is DeepSeek-V3.1-Terminus or Llama 2-7B better for coding?

DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 42.0 versus 29.2 in the Noometry coding category.

### How many benchmarks do DeepSeek-V3.1-Terminus and Llama 2-7B share?

10 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Llama 2-7B has 29.