# DeepSeek V4.1 Flash vs Llama 3.1-70B

> DeepSeek V4.1 Flash is the stronger model overall, scoring 52.8 to 29.6 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v4-1-flash-vs-llama-3-1-70b
- Last updated: 2026-10-12
- Shared benchmarks: 23

## Summary

- They share 23 benchmarks with published results for both. DeepSeek V4.1 Flash scores higher in 9 categories and Llama 3.1-70B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek V4.1 Flash leads 66.7 to 13.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.3% for DeepSeek V4.1 Flash and 3.6% for Llama 3.1-70B.
- DeepSeek V4.1 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.40 / $0.40 for Llama 3.1-70B.
- DeepSeek V4.1 Flash accepts more context: 1M tokens versus 128K.

## Snapshot

| | DeepSeek V4.1 Flash | Llama 3.1-70B |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 52.8 | 29.6 |
| Rank | 38 | 308 |
| Context | 1M | 128K |
| Input $/M | $0.15 | $0.40 |
| Output $/M | $0.60 | $0.40 |
| Weights | Open | Open |

## Coding

- DeepSeek V4.1 Flash: 52.9 (#32)
- Llama 3.1-70B: 30.3 (#296)

| Benchmark | DeepSeek V4.1 Flash | Llama 3.1-70B |
|---|---|---|
| LMArena Coding | 1506 | 1260 |
| LMArena WebDev | 1619 | — |
| SciCode | 51.9% | — |
| WeirdML | — | 9% |
| BigCodeBench Instruct | — | 46.1% |
| BigCodeBench Complete | — | 54.8% |
| ALE-Bench | 1,092 | — |

## Agentic & Tool Use

- DeepSeek V4.1 Flash: 31.2 (#69)
- Llama 3.1-70B: 25.1 (#112)

| Benchmark | DeepSeek V4.1 Flash | Llama 3.1-70B |
|---|---|---|
| APEX-Agents | 39.5% | — |
| TheAgentCompany | — | 6.9% |
| BALROG | — | 27.9% |
| GDP.pdf | 19.8% | — |

## Reasoning

- DeepSeek V4.1 Flash: 50.2 (#36)
- Llama 3.1-70B: 21.6 (#220)

| Benchmark | DeepSeek V4.1 Flash | Llama 3.1-70B |
|---|---|---|
| LMArena Hard Prompts | 1483 | 1241 |
| DTBench | 89.9% | 60% |
| LMCA | 47% | 14.8% |
| Epoch Capabilities Index | 154.9 | 125.92 |
| NYT Connections (extended) | 89.6% | — |
| CritPt | 14.3% | — |
| Mystery Game Puzzles | 43% | — |
| Surface Evolver Bench | 46.3% | — |

## Math

- DeepSeek V4.1 Flash: 66.7 (#25)
- Llama 3.1-70B: 13.5 (#304)

| Benchmark | DeepSeek V4.1 Flash | Llama 3.1-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.3% | 3.6% |
| LMArena Math | 1477 | 1252 |
| FrontierMath (Tiers 1-3) | 67.4% | — |
| FrontierMath Tier 4 | 26.8% | — |
| ProofBench | 54% | — |
| Omni-MATH | — | 21% |
| MATH Level 5 | — | 36.7% |

## Knowledge

- DeepSeek V4.1 Flash: 57.9 (#38)
- Llama 3.1-70B: 24.2 (#269)

| Benchmark | DeepSeek V4.1 Flash | Llama 3.1-70B |
|---|---|---|
| GPQA Diamond | 89.8% | 44.2% |
| LMArena Expert | 1506 | 1209 |
| MMLU-Pro | — | 65.3% |
| GPQA (HELM) | — | 42.6% |
| MMLU | — | 80.1% |

## Multimodal

- DeepSeek V4.1 Flash: 39.1 (#61)
- Llama 3.1-70B: —

| Benchmark | DeepSeek V4.1 Flash | Llama 3.1-70B |
|---|---|---|
| LMArena Vision | 1277 | — |
| Furniture Assembly | 34.2% | — |

## Multilingual

- DeepSeek V4.1 Flash: 55.0 (#35)
- Llama 3.1-70B: 38.8 (#225)

| Benchmark | DeepSeek V4.1 Flash | Llama 3.1-70B |
|---|---|---|
| LMArena Non-English | 1448 | 1219 |
| LMArena Chinese | 1497 | 1215 |
| LMArena French | 1452 | 1261 |
| LMArena German | 1484 | 1222 |
| LMArena Japanese | 1412 | 1132 |
| LMArena Korean | 1452 | 1140 |
| LMArena Russian | 1471 | 1234 |
| LMArena Spanish | 1459 | 1253 |

## Instruction Following

- DeepSeek V4.1 Flash: 77.3 (#26)
- Llama 3.1-70B: 65.3 (#223)

| Benchmark | DeepSeek V4.1 Flash | Llama 3.1-70B |
|---|---|---|
| LMArena Instruction Following | 1474 | 1231 |
| IFEval | — | 82.1% |

## Long Context

- DeepSeek V4.1 Flash: 45.2 (#47)
- Llama 3.1-70B: 37.6 (#214)

| Benchmark | DeepSeek V4.1 Flash | Llama 3.1-70B |
|---|---|---|
| LMArena Longer Query | 1475 | 1241 |

## Writing & Preference

- DeepSeek V4.1 Flash: 65.4 (#48)
- Llama 3.1-70B: 35.4 (#267)

| Benchmark | DeepSeek V4.1 Flash | Llama 3.1-70B |
|---|---|---|
| LMArena Text | 1462 | 1261 |
| LMArena Creative Writing | 1435 | 1232 |
| EQ-Bench Creative Writing | 1540 | 784 |
| LMArena Multi-Turn | 1457 | 1256 |
| WildBench | — | 75.8% |

## FAQ

### Is DeepSeek V4.1 Flash better than Llama 3.1-70B?

DeepSeek V4.1 Flash is the stronger model overall, scoring 52.8 to 29.6 on the Noometry Index.

### Which is cheaper, DeepSeek V4.1 Flash or Llama 3.1-70B?

DeepSeek V4.1 Flash is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; Llama 3.1-70B lists at $0.40 and $0.40.

### Is DeepSeek V4.1 Flash or Llama 3.1-70B better for coding?

DeepSeek V4.1 Flash scores higher on coding benchmarks: 52.9 versus 30.3 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek V4.1 Flash does, with 1M tokens against 128K.

### How many benchmarks do DeepSeek V4.1 Flash and Llama 3.1-70B share?

23 benchmarks have published results for both models. DeepSeek V4.1 Flash has 37 scored results on Noometry and Llama 3.1-70B has 35.