# DeepSeek V4 Pro vs Llama 2-7B

> DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 29.1 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v4-pro-vs-llama-2-7b
- Last updated: 2026-10-10
- Shared benchmarks: 17

## Summary

- They share 17 benchmarks with published results for both. DeepSeek V4 Pro scores higher in 8 categories and Llama 2-7B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4 Pro leads 56.5 to 15.7.
- The biggest single-benchmark swing is Chess Puzzles: 47% for DeepSeek V4 Pro and 0% for Llama 2-7B.

## Snapshot

| | DeepSeek V4 Pro | Llama 2-7B |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 54.3 | 29.1 |
| Rank | 31 | 317 |
| Context | 1M | — |
| Input $/M | $0.66 | — |
| Output $/M | $1.98 | — |
| Weights | Open | Open |

## Coding

- DeepSeek V4 Pro: 52.4 (#34)
- Llama 2-7B: 29.2 (#307)

| Benchmark | DeepSeek V4 Pro | Llama 2-7B |
|---|---|---|
| LMArena Coding | 1470 | 1002 |
| SWE-bench Verified | 77.6% | — |
| FrontierCode | 28.6% | — |
| LMArena WebDev | 1582 | — |
| SciCode | 51% | — |
| WeirdML | 66.2% | — |
| ALE-Bench | 1,403 | — |

## Agentic & Tool Use

- DeepSeek V4 Pro: 32.8 (#58)
- Llama 2-7B: —

| Benchmark | DeepSeek V4 Pro | Llama 2-7B |
|---|---|---|
| APEX-Agents | 47.3% | — |
| Vending-Bench 2 | 3,285 | — |

## Reasoning

- DeepSeek V4 Pro: 56.5 (#24)
- Llama 2-7B: 15.7 (#312)

| Benchmark | DeepSeek V4 Pro | Llama 2-7B |
|---|---|---|
| Chess Puzzles | 47% | 0% |
| LMArena Hard Prompts | 1461 | 1009 |
| Epoch Capabilities Index | 155.31 | 99.06 |
| ARC-AGI-2 | 61.3% | — |
| Kagi LLM Benchmark | 53.5% | — |
| NYT Connections (extended) | 91.3% | — |
| ARC-AGI-1 | 90.5% | — |
| CritPt | 18% | — |
| Mystery Game Puzzles | 43% | — |
| DTBench | 93.9% | — |
| LMCA | 45.5% | — |
| Surface Evolver Bench | 40% | — |
| BIG-Bench Hard | — | 39.2% |
| ForecastBench | 56.1 | — |
| HellaSwag | — | 77.2% |
| LAMBADA | — | 73.3% |
| PIQA | — | 78.8% |
| WinoGrande | — | 69.2% |

## Math

- DeepSeek V4 Pro: 64.8 (#30)
- Llama 2-7B: 30.7 (#233)

| Benchmark | DeepSeek V4 Pro | Llama 2-7B |
|---|---|---|
| LMArena Math | 1455 | 1042 |
| FrontierMath (Tiers 1-3) | 64.6% | — |
| FrontierMath Tier 4 | 26.8% | — |
| MathArena Final-Answer Competitions | 76.6% | — |
| OTIS Mock AIME 2024-2025 | 98.6% | — |
| ProofBench | 50% | — |
| GSM8K | — | 16.7% |

## Knowledge

- DeepSeek V4 Pro: 59.5 (#31)
- Llama 2-7B: 28.2 (#248)

| Benchmark | DeepSeek V4 Pro | Llama 2-7B |
|---|---|---|
| LMArena Expert | 1464 | 1036 |
| GPQA Diamond | 91.7% | — |
| SimpleQA Verified | 52.9% | — |
| Vectara Hallucination Rate | 8.6% | — |
| ARC (AI2) Challenge | — | 45.9% |
| BoolQ | — | 77.9% |
| MMLU | — | 45.8% |
| OpenBookQA | — | 58.6% |
| TriviaQA | — | 73.7% |

## Multimodal

- DeepSeek V4 Pro: —
- Llama 2-7B: —

| Benchmark | DeepSeek V4 Pro | Llama 2-7B |
|---|---|---|
| ScienceQA | — | 43.1% |

## Multilingual

- DeepSeek V4 Pro: 54.4 (#45)
- Llama 2-7B: 23.8 (#293)

| Benchmark | DeepSeek V4 Pro | Llama 2-7B |
|---|---|---|
| LMArena Non-English | 1439 | 973 |
| LMArena Chinese | 1486 | 973 |
| LMArena French | 1472 | 970 |
| LMArena German | 1458 | 978 |
| LMArena Russian | 1453 | 995 |
| LMArena Spanish | 1458 | 1007 |
| LMArena Japanese | 1445 | — |
| LMArena Korean | 1447 | — |

## Instruction Following

- DeepSeek V4 Pro: 76.1 (#47)
- Llama 2-7B: 50.8 (#298)

| Benchmark | DeepSeek V4 Pro | Llama 2-7B |
|---|---|---|
| LMArena Instruction Following | 1448 | 1006 |

## Long Context

- DeepSeek V4 Pro: 45.0 (#51)
- Llama 2-7B: 30.4 (#287)

| Benchmark | DeepSeek V4 Pro | Llama 2-7B |
|---|---|---|
| LMArena Longer Query | 1458 | 999 |
| CL-bench Life | 13.5% | — |

## Writing & Preference

- DeepSeek V4 Pro: 65.5 (#46)
- Llama 2-7B: 28.0 (#298)

| Benchmark | DeepSeek V4 Pro | Llama 2-7B |
|---|---|---|
| LMArena Text | 1451 | 1053 |
| LMArena Creative Writing | 1446 | 1033 |
| LMArena Multi-Turn | 1467 | 1029 |
| EQ-Bench Creative Writing | 1553 | — |
| EQ-Bench 4 | 1166 | — |

## FAQ

### Is DeepSeek V4 Pro better than Llama 2-7B?

DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 29.1 on the Noometry Index.

### Is DeepSeek V4 Pro or Llama 2-7B better for coding?

DeepSeek V4 Pro scores higher on coding benchmarks: 52.4 versus 29.2 in the Noometry coding category.

### How many benchmarks do DeepSeek V4 Pro and Llama 2-7B share?

17 benchmarks have published results for both models. DeepSeek V4 Pro has 48 scored results on Noometry and Llama 2-7B has 29.