# Grok 3 vs Llama 2-70B

> Grok 3 is the stronger model overall, scoring 39.9 to 24.4 on the Noometry Index.

- Canonical page: https://noometry.com/compare/grok-3-vs-llama-2-70b
- Last updated: 2026-10-11
- Shared benchmarks: 21

## Summary

- They share 21 benchmarks with published results for both. Grok 3 scores higher in 7 categories and Llama 2-70B in 1 category; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Grok 3 leads 46.2 to 7.4.
- The biggest single-benchmark swing is MATH Level 5: 88.7% for Grok 3 and 3.3% for Llama 2-70B.
- Llama 2-70B has downloadable open weights; the other is API-only.

## Snapshot

| | Grok 3 | Llama 2-70B |
|---|---|---|
| Provider | xAI | Meta |
| Noometry Index | 39.9 | 24.4 |
| Rank | 157 | 349 |
| Context | — | — |
| Input $/M | — | — |
| Output $/M | — | — |
| Weights | Proprietary | Open |

## Coding

- Grok 3: 41.9 (#115)
- Llama 2-70B: 31.4 (#286)

| Benchmark | Grok 3 | Llama 2-70B |
|---|---|---|
| LMArena Coding | 1432 | 1079 |
| Aider Polyglot | 53.3% | — |
| WeirdML | 37.2% | — |

## Agentic & Tool Use

- Grok 3: 30.5 (#76)
- Llama 2-70B: —

| Benchmark | Grok 3 | Llama 2-70B |
|---|---|---|
| BALROG | 29.5% | — |

## Reasoning

- Grok 3: 13.7 (#333)
- Llama 2-70B: 14.4 (#325)

| Benchmark | Grok 3 | Llama 2-70B |
|---|---|---|
| LMArena Hard Prompts | 1434 | 1073 |
| Epoch Capabilities Index | 138.33 | 113.79 |
| ARC-AGI-2 | 0% | — |
| SimpleBench | 36.1% | — |
| Kagi LLM Benchmark | 61.3% | — |
| ARC-AGI-1 | 5.5% | — |
| DTBench | — | 41.6% |
| BIG-Bench Hard | — | 64.9% |
| CommonsenseQA 2.0 | — | 50% |
| ForecastBench | — | 51.4 |
| HellaSwag | — | 85.3% |
| LAMBADA | — | 78.9% |
| PIQA | — | 82.8% |
| WinoGrande | — | 80.2% |

## Math

- Grok 3: 38.0 (#145)
- Llama 2-70B: 8.1 (#326)

| Benchmark | Grok 3 | Llama 2-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 55.6% | 0% |
| LMArena Math | 1391 | 1091 |
| MATH Level 5 | 88.7% | 3.3% |
| Omni-MATH | 46.4% | — |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
| GSM8K | — | 69.6% |

## Knowledge

- Grok 3: 46.2 (#82)
- Llama 2-70B: 7.4 (#310)

| Benchmark | Grok 3 | Llama 2-70B |
|---|---|---|
| GPQA Diamond | 75.8% | 26.3% |
| LMArena Expert | 1421 | 1039 |
| MMLU-Pro | 78.8% | — |
| Confabulations | 14.2% | — |
| Vectara Hallucination Rate | 5.8% | — |
| GPQA (HELM) | 65% | — |
| ARC (AI2) Challenge | — | 78.3% |
| BoolQ | — | 88.6% |
| MMLU | — | 69.9% |
| OpenBookQA | — | 60.2% |
| TriviaQA | — | 87.6% |

## Multilingual

- Grok 3: 52.3 (#87)
- Llama 2-70B: 27.7 (#274)

| Benchmark | Grok 3 | Llama 2-70B |
|---|---|---|
| LMArena Non-English | 1410 | 1045 |
| LMArena Chinese | 1448 | 995 |
| LMArena French | 1460 | 1090 |
| LMArena German | 1431 | 1041 |
| LMArena Japanese | 1387 | 927 |
| LMArena Korean | 1373 | 964 |
| LMArena Russian | 1416 | 1083 |
| LMArena Spanish | 1417 | 1143 |

## Instruction Following

- Grok 3: 75.0 (#73)
- Llama 2-70B: 54.9 (#278)

| Benchmark | Grok 3 | Llama 2-70B |
|---|---|---|
| LMArena Instruction Following | 1409 | 1071 |
| IFEval | 88.4% | — |

## Long Context

- Grok 3: 38.7 (#192)
- Llama 2-70B: 32.3 (#270)

| Benchmark | Grok 3 | Llama 2-70B |
|---|---|---|
| LMArena Longer Query | 1439 | 1062 |
| Fiction.LiveBench | 58.3% | — |

## Writing & Preference

- Grok 3: 55.8 (#141)
- Llama 2-70B: 32.3 (#279)

| Benchmark | Grok 3 | Llama 2-70B |
|---|---|---|
| LMArena Text | 1426 | 1115 |
| LMArena Creative Writing | 1414 | 1075 |
| LMArena Multi-Turn | 1425 | 1088 |
| Short-Story Creative Writing | 76.4% | — |
| EQ-Bench Creative Writing | 1186 | — |
| WildBench | 84.9% | — |

## FAQ

### Is Grok 3 better than Llama 2-70B?

Grok 3 is the stronger model overall, scoring 39.9 to 24.4 on the Noometry Index.

### Is Grok 3 or Llama 2-70B better for coding?

Grok 3 scores higher on coding benchmarks: 41.9 versus 31.4 in the Noometry coding category.

### How many benchmarks do Grok 3 and Llama 2-70B share?

21 benchmarks have published results for both models. Grok 3 has 40 scored results on Noometry and Llama 2-70B has 35.
