# DeepSeek-V3.1 vs Llama-3.3-70B-Instruct

> DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 2.7× less per token, which makes it the better buy when DeepSeek-V3.1's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-vs-llama-3-3-70b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 25

## Summary

- They share 25 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 8 categories and Llama-3.3-70B-Instruct in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek-V3.1 leads 36.1 to 14.3.
- The biggest single-benchmark swing is WeirdML: 38.4% for DeepSeek-V3.1 and 14.4% for Llama-3.3-70B-Instruct.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $0.25 / $0.95 for DeepSeek-V3.1.
- DeepSeek-V3.1 accepts more context: 164K tokens versus 128K.

## Snapshot

| | DeepSeek-V3.1 | Llama-3.3-70B-Instruct |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 42.2 | 30.3 |
| Rank | 106 | 283 |
| Context | 164K | 128K |
| Input $/M | $0.25 | $0.10 |
| Output $/M | $0.95 | $0.32 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1: 39.8 (#140)
- Llama-3.3-70B-Instruct: 30.6 (#284)

| Benchmark | DeepSeek-V3.1 | Llama-3.3-70B-Instruct |
|---|---|---|
| WeirdML | 38.4% | 14.4% |
| LMArena Coding | 1417 | 1268 |
| SciCode | — | 26% |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |

## Agentic & Tool Use

- DeepSeek-V3.1: —
- Llama-3.3-70B-Instruct: 25.6 (#100)

| Benchmark | DeepSeek-V3.1 | Llama-3.3-70B-Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 31.9% |
| BALROG | — | 23% |

## Reasoning

- DeepSeek-V3.1: 27.3 (#109)
- Llama-3.3-70B-Instruct: 13.7 (#325)

| Benchmark | DeepSeek-V3.1 | Llama-3.3-70B-Instruct |
|---|---|---|
| SimpleBench | 40% | 19.9% |
| LMArena Hard Prompts | 1417 | 1257 |
| DTBench | 82.7% | 59.5% |
| LMCA | 24.3% | 17.5% |
| Epoch Capabilities Index | 139.92 | 127.32 |
| ForecastBench | 58 | 58.6 |
| Kagi LLM Benchmark | 53.2% | — |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 50.8% |
| LiveBench Data Analysis | — | 49.5% |
| LiveBench | — | 50.2% |

## Math

- DeepSeek-V3.1: 36.1 (#125)
- Llama-3.3-70B-Instruct: 14.3 (#292)

| Benchmark | DeepSeek-V3.1 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Math | 1420 | 1268 |
| OTIS Mock AIME 2024-2025 | — | 5.1% |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |

## Knowledge

- DeepSeek-V3.1: 43.0 (#86)
- Llama-3.3-70B-Instruct: 30.5 (#220)

| Benchmark | DeepSeek-V3.1 | Llama-3.3-70B-Instruct |
|---|---|---|
| Vectara Hallucination Rate | 5.5% | 4.1% |
| LMArena Expert | 1403 | 1225 |
| GPQA Diamond | — | 47.4% |
| Confabulations | — | 22.8% |
| MMLU | — | 86.3% |

## Multilingual

- DeepSeek-V3.1: 51.8 (#100)
- Llama-3.3-70B-Instruct: 40.1 (#215)

| Benchmark | DeepSeek-V3.1 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1400 | 1236 |
| LMArena Chinese | 1470 | 1217 |
| LMArena French | 1447 | 1280 |
| LMArena German | 1412 | 1251 |
| LMArena Japanese | 1374 | 1149 |
| LMArena Korean | 1337 | 1142 |
| LMArena Russian | 1405 | 1252 |
| LMArena Spanish | 1429 | 1269 |

## Instruction Following

- DeepSeek-V3.1: 74.1 (#105)
- Llama-3.3-70B-Instruct: 71.3 (#152)

| Benchmark | DeepSeek-V3.1 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1400 | 1242 |
| LiveBench Instruction Following | — | 82.7% |

## Long Context

- DeepSeek-V3.1: 36.3 (#227)
- Llama-3.3-70B-Instruct: 26.4 (#290)

| Benchmark | DeepSeek-V3.1 | Llama-3.3-70B-Instruct |
|---|---|---|
| Fiction.LiveBench | 52.8% | 33.3% |
| LMArena Longer Query | 1422 | 1256 |

## Writing & Preference

- DeepSeek-V3.1: 60.5 (#93)
- Llama-3.3-70B-Instruct: 47.8 (#203)

| Benchmark | DeepSeek-V3.1 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1420 | 1274 |
| LMArena Creative Writing | 1401 | 1250 |
| LMArena Multi-Turn | 1408 | 1279 |
| EQ-Bench Creative Writing | 1436 | — |
| LiveBench Language | — | 39.2% |

## FAQ

### Is DeepSeek-V3.1 better than Llama-3.3-70B-Instruct?

DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 2.7× less per token, which makes it the better buy when DeepSeek-V3.1's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-V3.1 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-V3.1 lists at $0.25 and $0.95.

### Is DeepSeek-V3.1 or Llama-3.3-70B-Instruct better for coding?

DeepSeek-V3.1 scores higher on coding benchmarks: 39.8 versus 30.6 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek-V3.1 does, with 164K tokens against 128K.

### How many benchmarks do DeepSeek-V3.1 and Llama-3.3-70B-Instruct share?

25 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Llama-3.3-70B-Instruct has 43.