# DeepSeek V4.1 Flash vs Llama-3.3-70B-Instruct

> DeepSeek V4.1 Flash is the stronger model overall, scoring 52.8 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 1.7× less per token, which makes it the better buy when DeepSeek V4.1 Flash's lead doesn't matter for your workload.

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

## Summary

- They share 24 benchmarks with published results for both. DeepSeek V4.1 Flash scores higher in 9 categories and Llama-3.3-70B-Instruct 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 15.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.3% for DeepSeek V4.1 Flash and 5.1% for Llama-3.3-70B-Instruct.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $0.15 / $0.60 for DeepSeek V4.1 Flash.
- DeepSeek V4.1 Flash accepts more context: 1M tokens versus 128K.

## Snapshot

| | DeepSeek V4.1 Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 52.8 | 30.6 |
| Rank | 38 | 291 |
| Context | 1M | 128K |
| Input $/M | $0.15 | $0.10 |
| Output $/M | $0.60 | $0.32 |
| Weights | Open | Open |

## Coding

- DeepSeek V4.1 Flash: 52.9 (#32)
- Llama-3.3-70B-Instruct: 31.0 (#290)

| Benchmark | DeepSeek V4.1 Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| SciCode | 51.9% | 26% |
| LMArena Coding | 1506 | 1268 |
| LMArena WebDev | 1619 | — |
| WeirdML | — | 14.4% |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |
| ALE-Bench | 1,092 | — |

## Agentic & Tool Use

- DeepSeek V4.1 Flash: 31.2 (#69)
- Llama-3.3-70B-Instruct: 25.8 (#105)

| Benchmark | DeepSeek V4.1 Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| APEX-Agents | 39.5% | — |
| Berkeley Function Calling Leaderboard | — | 31.9% |
| BALROG | — | 23% |
| GDP.pdf | 19.8% | — |

## Reasoning

- DeepSeek V4.1 Flash: 50.2 (#36)
- Llama-3.3-70B-Instruct: 14.1 (#327)

| Benchmark | DeepSeek V4.1 Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| CritPt | 14.3% | 0% |
| LMArena Hard Prompts | 1483 | 1257 |
| DTBench | 89.9% | 59.5% |
| LMCA | 47% | 17.5% |
| Epoch Capabilities Index | 154.9 | 127.33 |
| SimpleBench | — | 19.9% |
| NYT Connections (extended) | 89.6% | — |
| LiveBench Reasoning | — | 50.8% |
| Mystery Game Puzzles | 43% | — |
| LiveBench Data Analysis | — | 49.5% |
| Surface Evolver Bench | 46.3% | — |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |

## Math

- DeepSeek V4.1 Flash: 66.7 (#25)
- Llama-3.3-70B-Instruct: 15.3 (#298)

| Benchmark | DeepSeek V4.1 Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.3% | 5.1% |
| LMArena Math | 1477 | 1267 |
| FrontierMath (Tiers 1-3) | 67.4% | — |
| FrontierMath Tier 4 | 26.8% | — |
| ProofBench | 54% | — |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |

## Knowledge

- DeepSeek V4.1 Flash: 57.9 (#38)
- Llama-3.3-70B-Instruct: 30.6 (#226)

| Benchmark | DeepSeek V4.1 Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 89.8% | 47.4% |
| LMArena Expert | 1506 | 1225 |
| Confabulations | — | 22.8% |
| Vectara Hallucination Rate | — | 4.1% |
| MMLU | — | 86.3% |

## Multimodal

- DeepSeek V4.1 Flash: 39.1 (#61)
- Llama-3.3-70B-Instruct: —

| Benchmark | DeepSeek V4.1 Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Vision | 1277 | — |
| Furniture Assembly | 34.2% | — |

## Multilingual

- DeepSeek V4.1 Flash: 55.0 (#35)
- Llama-3.3-70B-Instruct: 39.9 (#220)

| Benchmark | DeepSeek V4.1 Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1448 | 1236 |
| LMArena Chinese | 1497 | 1217 |
| LMArena French | 1452 | 1281 |
| LMArena German | 1484 | 1251 |
| LMArena Japanese | 1412 | 1150 |
| LMArena Korean | 1452 | 1143 |
| LMArena Russian | 1471 | 1252 |
| LMArena Spanish | 1459 | 1270 |

## Instruction Following

- DeepSeek V4.1 Flash: 77.3 (#26)
- Llama-3.3-70B-Instruct: 71.1 (#157)

| Benchmark | DeepSeek V4.1 Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1474 | 1242 |
| LiveBench Instruction Following | — | 82.7% |

## Long Context

- DeepSeek V4.1 Flash: 45.2 (#47)
- Llama-3.3-70B-Instruct: 26.4 (#295)

| Benchmark | DeepSeek V4.1 Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1475 | 1256 |
| Fiction.LiveBench | — | 33.3% |

## Writing & Preference

- DeepSeek V4.1 Flash: 65.4 (#48)
- Llama-3.3-70B-Instruct: 47.6 (#207)

| Benchmark | DeepSeek V4.1 Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1462 | 1274 |
| LMArena Creative Writing | 1435 | 1250 |
| LMArena Multi-Turn | 1457 | 1280 |
| EQ-Bench Creative Writing | 1540 | — |
| LiveBench Language | — | 39.2% |

## FAQ

### Is DeepSeek V4.1 Flash better than Llama-3.3-70B-Instruct?

DeepSeek V4.1 Flash is the stronger model overall, scoring 52.8 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 1.7× less per token, which makes it the better buy when DeepSeek V4.1 Flash's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek V4.1 Flash or Llama-3.3-70B-Instruct?

Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; DeepSeek V4.1 Flash lists at $0.15 and $0.60.

### Is DeepSeek V4.1 Flash or Llama-3.3-70B-Instruct better for coding?

DeepSeek V4.1 Flash scores higher on coding benchmarks: 52.9 versus 31.0 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-3.3-70B-Instruct share?

24 benchmarks have published results for both models. DeepSeek V4.1 Flash has 37 scored results on Noometry and Llama-3.3-70B-Instruct has 43.