# DeepSeek V4 Flash vs Llama 3.1-70B

> DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 29.6 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v4-flash-vs-llama-3-1-70b
- Last updated: 2026-10-12
- Shared benchmarks: 24

## Summary

- They share 24 benchmarks with published results for both. DeepSeek V4 Flash scores higher in 8 categories and Llama 3.1-70B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek V4 Flash leads 60.3 to 13.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 94.4% for DeepSeek V4 Flash and 3.6% for Llama 3.1-70B.
- DeepSeek V4 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 Flash accepts more context: 1M tokens versus 128K.

## Snapshot

| | DeepSeek V4 Flash | Llama 3.1-70B |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 53.6 | 29.6 |
| Rank | 35 | 308 |
| Context | 1M | 128K |
| Input $/M | $0.15 | $0.40 |
| Output $/M | $0.60 | $0.40 |
| Weights | Open | Open |

## Coding

- DeepSeek V4 Flash: 47.9 (#59)
- Llama 3.1-70B: 30.3 (#296)

| Benchmark | DeepSeek V4 Flash | Llama 3.1-70B |
|---|---|---|
| WeirdML | 63% | 9% |
| LMArena Coding | 1457 | 1260 |
| FrontierCode | 18.8% | — |
| LMArena WebDev | 1582 | — |
| SciCode | 49.9% | — |
| BigCodeBench Instruct | — | 46.1% |
| BigCodeBench Complete | — | 54.8% |
| ALE-Bench | 1,306 | — |

## Agentic & Tool Use

- DeepSeek V4 Flash: —
- Llama 3.1-70B: 25.1 (#112)

| Benchmark | DeepSeek V4 Flash | Llama 3.1-70B |
|---|---|---|
| TheAgentCompany | — | 6.9% |
| BALROG | — | 27.9% |

## Reasoning

- DeepSeek V4 Flash: 53.7 (#30)
- Llama 3.1-70B: 21.6 (#220)

| Benchmark | DeepSeek V4 Flash | Llama 3.1-70B |
|---|---|---|
| LMArena Hard Prompts | 1444 | 1241 |
| DTBench | 90.9% | 60% |
| LMCA | 41.7% | 14.8% |
| Epoch Capabilities Index | 154.49 | 125.92 |
| ARC-AGI-2 | 61.4% | — |
| SimpleBench | 61.1% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 89.6% | — |
| ARC-AGI-1 | 89% | — |
| CritPt | 16.6% | — |
| Chess Puzzles | 33% | — |
| Mystery Game Puzzles | 34% | — |

## Math

- DeepSeek V4 Flash: 60.3 (#37)
- Llama 3.1-70B: 13.5 (#304)

| Benchmark | DeepSeek V4 Flash | Llama 3.1-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 94.4% | 3.6% |
| LMArena Math | 1427 | 1252 |
| FrontierMath (Tiers 1-3) | 57.5% | — |
| FrontierMath Tier 4 | 24.4% | — |
| MathArena Final-Answer Competitions | 76.5% | — |
| ProofBench | 56% | — |
| Omni-MATH | — | 21% |
| MATH Level 5 | — | 36.7% |

## Knowledge

- DeepSeek V4 Flash: 55.4 (#48)
- Llama 3.1-70B: 24.2 (#269)

| Benchmark | DeepSeek V4 Flash | Llama 3.1-70B |
|---|---|---|
| GPQA Diamond | 91% | 44.2% |
| LMArena Expert | 1441 | 1209 |
| SimpleQA Verified | 33.6% | — |
| MMLU-Pro | — | 65.3% |
| GPQA (HELM) | — | 42.6% |
| MMLU | — | 80.1% |

## Multilingual

- DeepSeek V4 Flash: 53.0 (#72)
- Llama 3.1-70B: 38.8 (#225)

| Benchmark | DeepSeek V4 Flash | Llama 3.1-70B |
|---|---|---|
| LMArena Non-English | 1420 | 1219 |
| LMArena Chinese | 1468 | 1215 |
| LMArena French | 1439 | 1261 |
| LMArena German | 1418 | 1222 |
| LMArena Japanese | 1406 | 1132 |
| LMArena Korean | 1384 | 1140 |
| LMArena Russian | 1428 | 1234 |
| LMArena Spanish | 1436 | 1253 |

## Instruction Following

- DeepSeek V4 Flash: 74.9 (#81)
- Llama 3.1-70B: 65.3 (#223)

| Benchmark | DeepSeek V4 Flash | Llama 3.1-70B |
|---|---|---|
| LMArena Instruction Following | 1421 | 1231 |
| IFEval | — | 82.1% |

## Long Context

- DeepSeek V4 Flash: 43.8 (#85)
- Llama 3.1-70B: 37.6 (#214)

| Benchmark | DeepSeek V4 Flash | Llama 3.1-70B |
|---|---|---|
| LMArena Longer Query | 1434 | 1241 |

## Writing & Preference

- DeepSeek V4 Flash: 63.8 (#61)
- Llama 3.1-70B: 35.4 (#267)

| Benchmark | DeepSeek V4 Flash | Llama 3.1-70B |
|---|---|---|
| LMArena Text | 1432 | 1261 |
| LMArena Creative Writing | 1403 | 1232 |
| EQ-Bench Creative Writing | 1559 | 784 |
| LMArena Multi-Turn | 1449 | 1256 |
| WildBench | — | 75.8% |

## FAQ

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

DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 29.6 on the Noometry Index.

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

DeepSeek V4 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 Flash or Llama 3.1-70B better for coding?

DeepSeek V4 Flash scores higher on coding benchmarks: 47.9 versus 30.3 in the Noometry coding category.

### Which has the bigger context window?

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

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

24 benchmarks have published results for both models. DeepSeek V4 Flash has 41 scored results on Noometry and Llama 3.1-70B has 35.