# DeepSeek V4 Pro vs Llama 3-8B

> DeepSeek V4 Pro is the stronger model overall, scoring 54.2 to 25.3 on the Noometry Index. Llama 3-8B costs 3.9× less per token, which makes it the better buy when DeepSeek V4 Pro's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v4-pro-vs-llama-3-8b
- Last updated: 2026-10-10
- Shared benchmarks: 23

## Summary

- They share 23 benchmarks with published results for both. DeepSeek V4 Pro scores higher in 8 categories and Llama 3-8B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek V4 Pro leads 64.6 to 8.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.6% for DeepSeek V4 Pro and 1.9% for Llama 3-8B.
- Llama 3-8B is cheaper at $0.14 / $0.14 per million input/output tokens, against $0.43 / $0.87 for DeepSeek V4 Pro.
- DeepSeek V4 Pro accepts more context: 1M tokens versus 8K.

## Snapshot

| | DeepSeek V4 Pro | Llama 3-8B |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 54.2 | 25.3 |
| Rank | 28 | 338 |
| Context | 1M | 8K |
| Input $/M | $0.43 | $0.14 |
| Output $/M | $0.87 | $0.14 |
| Weights | Open | Open |

## Coding

- DeepSeek V4 Pro: 52.2 (#31)
- Llama 3-8B: 30.5 (#285)

| Benchmark | DeepSeek V4 Pro | Llama 3-8B |
|---|---|---|
| LMArena Coding | 1470 | 1152 |
| SWE-bench Verified | 77.6% | — |
| FrontierCode | 28.6% | — |
| LMArena WebDev | 1582 | — |
| SciCode | 51% | — |
| WeirdML | 66.2% | — |
| BigCodeBench Instruct | — | 31.9% |
| BigCodeBench Complete | — | 36.9% |
| ALE-Bench | 1,403 | — |
| HumanEval+ | — | 56.7% |
| MBPP+ | — | 54.8% |

## Agentic & Tool Use

- DeepSeek V4 Pro: 32.7 (#54)
- Llama 3-8B: —

| Benchmark | DeepSeek V4 Pro | Llama 3-8B |
|---|---|---|
| APEX-Agents | 47.3% | — |
| Vending-Bench 2 | 3,285 | — |

## Reasoning

- DeepSeek V4 Pro: 56.4 (#22)
- Llama 3-8B: 13.8 (#321)

| Benchmark | DeepSeek V4 Pro | Llama 3-8B |
|---|---|---|
| Chess Puzzles | 47% | 0% |
| LMArena Hard Prompts | 1461 | 1133 |
| DTBench | 93.9% | 43.9% |
| Epoch Capabilities Index | 155.39 | 116.33 |
| ForecastBench | 56.1 | 58.6 |
| 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% | — |
| LMCA | 45.5% | — |
| Surface Evolver Bench | 40% | — |
| Adversarial NLI | — | 57.3% |
| WinoGrande | — | 75.7% |

## Math

- DeepSeek V4 Pro: 64.6 (#24)
- Llama 3-8B: 8.3 (#317)

| Benchmark | DeepSeek V4 Pro | Llama 3-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.6% | 1.9% |
| LMArena Math | 1455 | 1151 |
| FrontierMath (Tiers 1-3) | 64.6% | — |
| FrontierMath Tier 4 | 26.8% | — |
| MathArena Final-Answer Competitions | 76.6% | — |
| ProofBench | 50% | — |
| MATH Level 5 | — | 6.1% |

## Knowledge

- DeepSeek V4 Pro: 59.5 (#28)
- Llama 3-8B: 7.8 (#303)

| Benchmark | DeepSeek V4 Pro | Llama 3-8B |
|---|---|---|
| GPQA Diamond | 91.7% | 26.1% |
| LMArena Expert | 1463 | 1113 |
| SimpleQA Verified | 52.9% | — |
| Vectara Hallucination Rate | 8.6% | — |
| ARC (AI2) Challenge | — | 82.8% |
| MMLU | — | 68.8% |
| OpenBookQA | — | 82.6% |
| TriviaQA | — | 67.7% |

## Multilingual

- DeepSeek V4 Pro: 54.5 (#44)
- Llama 3-8B: 31.0 (#256)

| Benchmark | DeepSeek V4 Pro | Llama 3-8B |
|---|---|---|
| LMArena Non-English | 1438 | 1098 |
| LMArena Chinese | 1486 | 1076 |
| LMArena French | 1473 | 1158 |
| LMArena German | 1458 | 1104 |
| LMArena Japanese | 1448 | 967 |
| LMArena Korean | 1452 | 1003 |
| LMArena Russian | 1452 | 1109 |
| LMArena Spanish | 1457 | 1172 |

## Instruction Following

- DeepSeek V4 Pro: 76.3 (#44)
- Llama 3-8B: 58.7 (#255)

| Benchmark | DeepSeek V4 Pro | Llama 3-8B |
|---|---|---|
| LMArena Instruction Following | 1448 | 1127 |

## Long Context

- DeepSeek V4 Pro: 45.0 (#49)
- Llama 3-8B: 34.2 (#246)

| Benchmark | DeepSeek V4 Pro | Llama 3-8B |
|---|---|---|
| LMArena Longer Query | 1459 | 1128 |
| CL-bench Life | 13.5% | — |

## Writing & Preference

- DeepSeek V4 Pro: 65.6 (#45)
- Llama 3-8B: 37.7 (#251)

| Benchmark | DeepSeek V4 Pro | Llama 3-8B |
|---|---|---|
| LMArena Text | 1451 | 1166 |
| LMArena Creative Writing | 1446 | 1150 |
| LMArena Multi-Turn | 1467 | 1152 |
| EQ-Bench Creative Writing | 1553 | — |
| EQ-Bench 4 | 1166 | — |

## FAQ

### Is DeepSeek V4 Pro better than Llama 3-8B?

DeepSeek V4 Pro is the stronger model overall, scoring 54.2 to 25.3 on the Noometry Index. Llama 3-8B costs 3.9× less per token, which makes it the better buy when DeepSeek V4 Pro's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek V4 Pro or Llama 3-8B?

Llama 3-8B is cheaper. It lists at $0.14 per million input tokens and $0.14 per million output tokens; DeepSeek V4 Pro lists at $0.43 and $0.87.

### Is DeepSeek V4 Pro or Llama 3-8B better for coding?

DeepSeek V4 Pro scores higher on coding benchmarks: 52.2 versus 30.5 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek V4 Pro does, with 1M tokens against 8K.

### How many benchmarks do DeepSeek V4 Pro and Llama 3-8B share?

23 benchmarks have published results for both models. DeepSeek V4 Pro has 48 scored results on Noometry and Llama 3-8B has 34.