# DeepSeek V4 Pro vs Llama-3.3-70B-Instruct

> DeepSeek V4 Pro is the stronger model overall, scoring 54.2 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 3.5× less per token, which makes it the better buy when DeepSeek V4 Pro's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v4-pro-vs-llama-3-3-70b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 27

## Summary

- They share 27 benchmarks with published results for both. DeepSeek V4 Pro 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 Pro leads 64.6 to 14.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.6% for DeepSeek V4 Pro 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 $0.43 / $0.87 for DeepSeek V4 Pro.
- DeepSeek V4 Pro accepts more context: 1M tokens versus 128K.

## Snapshot

| | DeepSeek V4 Pro | Llama-3.3-70B-Instruct |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 54.2 | 30.3 |
| Rank | 28 | 283 |
| Context | 1M | 128K |
| Input $/M | $0.43 | $0.10 |
| Output $/M | $0.87 | $0.32 |
| Weights | Open | Open |

## Coding

- DeepSeek V4 Pro: 52.2 (#31)
- Llama-3.3-70B-Instruct: 30.6 (#284)

| Benchmark | DeepSeek V4 Pro | Llama-3.3-70B-Instruct |
|---|---|---|
| SciCode | 51% | 26% |
| WeirdML | 66.2% | 14.4% |
| LMArena Coding | 1470 | 1268 |
| SWE-bench Verified | 77.6% | — |
| FrontierCode | 28.6% | — |
| LMArena WebDev | 1582 | — |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |
| ALE-Bench | 1,403 | — |

## Agentic & Tool Use

- DeepSeek V4 Pro: 32.7 (#54)
- Llama-3.3-70B-Instruct: 25.6 (#100)

| Benchmark | DeepSeek V4 Pro | Llama-3.3-70B-Instruct |
|---|---|---|
| APEX-Agents | 47.3% | — |
| Berkeley Function Calling Leaderboard | — | 31.9% |
| BALROG | — | 23% |
| Vending-Bench 2 | 3,285 | — |

## Reasoning

- DeepSeek V4 Pro: 56.4 (#22)
- Llama-3.3-70B-Instruct: 13.7 (#325)

| Benchmark | DeepSeek V4 Pro | Llama-3.3-70B-Instruct |
|---|---|---|
| CritPt | 18% | 0% |
| LMArena Hard Prompts | 1461 | 1257 |
| DTBench | 93.9% | 59.5% |
| LMCA | 45.5% | 17.5% |
| Epoch Capabilities Index | 155.39 | 127.32 |
| ForecastBench | 56.1 | 58.6 |
| ARC-AGI-2 | 61.3% | — |
| SimpleBench | — | 19.9% |
| Kagi LLM Benchmark | 53.5% | — |
| NYT Connections (extended) | 91.3% | — |
| ARC-AGI-1 | 90.5% | — |
| Chess Puzzles | 47% | — |
| LiveBench Reasoning | — | 50.8% |
| Mystery Game Puzzles | 43% | — |
| LiveBench Data Analysis | — | 49.5% |
| Surface Evolver Bench | 40% | — |
| LiveBench | — | 50.2% |

## Math

- DeepSeek V4 Pro: 64.6 (#24)
- Llama-3.3-70B-Instruct: 14.3 (#292)

| Benchmark | DeepSeek V4 Pro | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.6% | 5.1% |
| LMArena Math | 1455 | 1268 |
| FrontierMath (Tiers 1-3) | 64.6% | — |
| FrontierMath Tier 4 | 26.8% | — |
| MathArena Final-Answer Competitions | 76.6% | — |
| ProofBench | 50% | — |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |

## Knowledge

- DeepSeek V4 Pro: 59.5 (#28)
- Llama-3.3-70B-Instruct: 30.5 (#220)

| Benchmark | DeepSeek V4 Pro | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 91.7% | 47.4% |
| Vectara Hallucination Rate | 8.6% | 4.1% |
| LMArena Expert | 1463 | 1225 |
| SimpleQA Verified | 52.9% | — |
| Confabulations | — | 22.8% |
| MMLU | — | 86.3% |

## Multilingual

- DeepSeek V4 Pro: 54.5 (#44)
- Llama-3.3-70B-Instruct: 40.1 (#215)

| Benchmark | DeepSeek V4 Pro | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1438 | 1236 |
| LMArena Chinese | 1486 | 1217 |
| LMArena French | 1473 | 1280 |
| LMArena German | 1458 | 1251 |
| LMArena Japanese | 1448 | 1149 |
| LMArena Korean | 1452 | 1142 |
| LMArena Russian | 1452 | 1252 |
| LMArena Spanish | 1457 | 1269 |

## Instruction Following

- DeepSeek V4 Pro: 76.3 (#44)
- Llama-3.3-70B-Instruct: 71.3 (#152)

| Benchmark | DeepSeek V4 Pro | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1448 | 1242 |
| LiveBench Instruction Following | — | 82.7% |

## Long Context

- DeepSeek V4 Pro: 45.0 (#49)
- Llama-3.3-70B-Instruct: 26.4 (#290)

| Benchmark | DeepSeek V4 Pro | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1459 | 1256 |
| Fiction.LiveBench | — | 33.3% |
| CL-bench Life | 13.5% | — |

## Writing & Preference

- DeepSeek V4 Pro: 65.6 (#45)
- Llama-3.3-70B-Instruct: 47.8 (#203)

| Benchmark | DeepSeek V4 Pro | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1451 | 1274 |
| LMArena Creative Writing | 1446 | 1250 |
| LMArena Multi-Turn | 1467 | 1279 |
| EQ-Bench Creative Writing | 1553 | — |
| EQ-Bench 4 | 1166 | — |
| LiveBench Language | — | 39.2% |

## FAQ

### Is DeepSeek V4 Pro better than Llama-3.3-70B-Instruct?

DeepSeek V4 Pro is the stronger model overall, scoring 54.2 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 3.5× less per token, which makes it the better buy when DeepSeek V4 Pro's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek V4 Pro 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 Pro lists at $0.43 and $0.87.

### Is DeepSeek V4 Pro or Llama-3.3-70B-Instruct better for coding?

DeepSeek V4 Pro scores higher on coding benchmarks: 52.2 versus 30.6 in the Noometry coding category.

### Which has the bigger context window?

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

### How many benchmarks do DeepSeek V4 Pro and Llama-3.3-70B-Instruct share?

27 benchmarks have published results for both models. DeepSeek V4 Pro has 48 scored results on Noometry and Llama-3.3-70B-Instruct has 43.