# DeepSeek V4.1 Flash vs Llama 3-8B

> DeepSeek V4.1 Flash is the stronger model overall, scoring 52.8 to 25.5 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v4-1-flash-vs-llama-3-8b
- Last updated: 2026-10-12
- Shared benchmarks: 21

## Summary

- They share 21 benchmarks with published results for both. DeepSeek V4.1 Flash 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 V4.1 Flash leads 66.7 to 8.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.3% for DeepSeek V4.1 Flash and 1.9% for Llama 3-8B.

## Snapshot

| | DeepSeek V4.1 Flash | Llama 3-8B |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 52.8 | 25.5 |
| Rank | 38 | 344 |
| Context | 1M | — |
| Input $/M | $0.15 | — |
| Output $/M | $0.60 | — |
| Weights | Open | Open |

## Coding

- DeepSeek V4.1 Flash: 52.9 (#32)
- Llama 3-8B: 31.0 (#289)

| Benchmark | DeepSeek V4.1 Flash | Llama 3-8B |
|---|---|---|
| LMArena Coding | 1506 | 1152 |
| LMArena WebDev | 1619 | — |
| SciCode | 51.9% | — |
| BigCodeBench Instruct | — | 31.9% |
| BigCodeBench Complete | — | 36.9% |
| ALE-Bench | 1,092 | — |
| HumanEval+ | — | 56.7% |
| MBPP+ | — | 54.8% |

## Agentic & Tool Use

- DeepSeek V4.1 Flash: 31.2 (#69)
- Llama 3-8B: —

| Benchmark | DeepSeek V4.1 Flash | Llama 3-8B |
|---|---|---|
| APEX-Agents | 39.5% | — |
| GDP.pdf | 19.8% | — |

## Reasoning

- DeepSeek V4.1 Flash: 50.2 (#36)
- Llama 3-8B: 14.3 (#326)

| Benchmark | DeepSeek V4.1 Flash | Llama 3-8B |
|---|---|---|
| LMArena Hard Prompts | 1483 | 1133 |
| DTBench | 89.9% | 43.9% |
| Epoch Capabilities Index | 154.9 | 116.45 |
| NYT Connections (extended) | 89.6% | — |
| CritPt | 14.3% | — |
| Chess Puzzles | — | 0% |
| Mystery Game Puzzles | 43% | — |
| LMCA | 47% | — |
| Surface Evolver Bench | 46.3% | — |
| Adversarial NLI | — | 57.3% |
| ForecastBench | — | 58.6 |
| WinoGrande | — | 75.7% |

## Math

- DeepSeek V4.1 Flash: 66.7 (#25)
- Llama 3-8B: 8.8 (#323)

| Benchmark | DeepSeek V4.1 Flash | Llama 3-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.3% | 1.9% |
| LMArena Math | 1477 | 1151 |
| FrontierMath (Tiers 1-3) | 67.4% | — |
| FrontierMath Tier 4 | 26.8% | — |
| ProofBench | 54% | — |
| MATH Level 5 | — | 6.1% |

## Knowledge

- DeepSeek V4.1 Flash: 57.9 (#38)
- Llama 3-8B: 7.8 (#308)

| Benchmark | DeepSeek V4.1 Flash | Llama 3-8B |
|---|---|---|
| GPQA Diamond | 89.8% | 26.1% |
| LMArena Expert | 1506 | 1113 |
| ARC (AI2) Challenge | — | 82.8% |
| MMLU | — | 68.8% |
| OpenBookQA | — | 82.6% |
| TriviaQA | — | 67.7% |

## Multimodal

- DeepSeek V4.1 Flash: 39.1 (#61)
- Llama 3-8B: —

| Benchmark | DeepSeek V4.1 Flash | Llama 3-8B |
|---|---|---|
| LMArena Vision | 1277 | — |
| Furniture Assembly | 34.2% | — |

## Multilingual

- DeepSeek V4.1 Flash: 55.0 (#35)
- Llama 3-8B: 30.8 (#261)

| Benchmark | DeepSeek V4.1 Flash | Llama 3-8B |
|---|---|---|
| LMArena Non-English | 1448 | 1098 |
| LMArena Chinese | 1497 | 1076 |
| LMArena French | 1452 | 1159 |
| LMArena German | 1484 | 1104 |
| LMArena Japanese | 1412 | 967 |
| LMArena Korean | 1452 | 1004 |
| LMArena Russian | 1471 | 1109 |
| LMArena Spanish | 1459 | 1173 |

## Instruction Following

- DeepSeek V4.1 Flash: 77.3 (#26)
- Llama 3-8B: 58.4 (#260)

| Benchmark | DeepSeek V4.1 Flash | Llama 3-8B |
|---|---|---|
| LMArena Instruction Following | 1474 | 1127 |

## Long Context

- DeepSeek V4.1 Flash: 45.2 (#47)
- Llama 3-8B: 34.2 (#251)

| Benchmark | DeepSeek V4.1 Flash | Llama 3-8B |
|---|---|---|
| LMArena Longer Query | 1475 | 1128 |

## Writing & Preference

- DeepSeek V4.1 Flash: 65.4 (#48)
- Llama 3-8B: 37.5 (#256)

| Benchmark | DeepSeek V4.1 Flash | Llama 3-8B |
|---|---|---|
| LMArena Text | 1462 | 1166 |
| LMArena Creative Writing | 1435 | 1150 |
| LMArena Multi-Turn | 1457 | 1152 |
| EQ-Bench Creative Writing | 1540 | — |

## FAQ

### Is DeepSeek V4.1 Flash better than Llama 3-8B?

DeepSeek V4.1 Flash is the stronger model overall, scoring 52.8 to 25.5 on the Noometry Index.

### Is DeepSeek V4.1 Flash or Llama 3-8B better for coding?

DeepSeek V4.1 Flash scores higher on coding benchmarks: 52.9 versus 31.0 in the Noometry coding category.

### How many benchmarks do DeepSeek V4.1 Flash and Llama 3-8B share?

21 benchmarks have published results for both models. DeepSeek V4.1 Flash has 37 scored results on Noometry and Llama 3-8B has 34.