# Grok 4.20 Multi-Agent vs Llama 3.1-405B

> Grok 4.20 Multi-Agent is the stronger model overall, scoring 46.2 to 30.7 on the Noometry Index.

- Canonical page: https://noometry.com/compare/grok-4-20-multi-agent-vs-llama-3-1-405b
- Last updated: 2026-10-12
- Shared benchmarks: 17

## Summary

- They share 17 benchmarks with published results for both. Grok 4.20 Multi-Agent scores higher in 8 categories and Llama 3.1-405B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.20 Multi-Agent leads 43.9 to 16.8.
- Llama 3.1-405B has downloadable open weights; the other is API-only.

## Snapshot

| | Grok 4.20 Multi-Agent | Llama 3.1-405B |
|---|---|---|
| Provider | xAI | Meta |
| Noometry Index | 46.2 | 30.7 |
| Rank | 65 | 288 |
| Context | 1M | — |
| Input $/M | $1.25 | — |
| Output $/M | $2.50 | — |
| Weights | Proprietary | Open |

## Coding

- Grok 4.20 Multi-Agent: 43.0 (#92)
- Llama 3.1-405B: 33.1 (#262)

| Benchmark | Grok 4.20 Multi-Agent | Llama 3.1-405B |
|---|---|---|
| LMArena Coding | 1457 | 1291 |
| WeirdML | — | 21.4% |

## Agentic & Tool Use

- Grok 4.20 Multi-Agent: —
- Llama 3.1-405B: 21.0 (#140)

| Benchmark | Grok 4.20 Multi-Agent | Llama 3.1-405B |
|---|---|---|
| TheAgentCompany | — | 7.4% |
| Cybench | — | 7.5% |
| LMArena Search | 1204 | — |

## Reasoning

- Grok 4.20 Multi-Agent: 43.9 (#48)
- Llama 3.1-405B: 16.8 (#300)

| Benchmark | Grok 4.20 Multi-Agent | Llama 3.1-405B |
|---|---|---|
| LMArena Hard Prompts | 1448 | 1269 |
| SimpleBench | — | 23% |
| Kagi LLM Benchmark | — | 45% |
| NYT Connections (extended) | 89.6% | — |
| DTBench | — | 61.4% |
| BIG-Bench Hard | — | 82.9% |
| Epoch Capabilities Index | — | 128.75 |
| ForecastBench | — | 59.9 |
| HellaSwag | — | 89.2% |
| PIQA | — | 85.9% |
| WinoGrande | — | 89.2% |

## Math

- Grok 4.20 Multi-Agent: 39.4 (#104)
- Llama 3.1-405B: 18.4 (#290)

| Benchmark | Grok 4.20 Multi-Agent | Llama 3.1-405B |
|---|---|---|
| LMArena Math | 1442 | 1281 |
| OTIS Mock AIME 2024-2025 | — | 9.7% |
| Omni-MATH | — | 24.9% |
| MATH Level 5 | — | 49.8% |

## Knowledge

- Grok 4.20 Multi-Agent: 40.4 (#119)
- Llama 3.1-405B: 30.4 (#227)

| Benchmark | Grok 4.20 Multi-Agent | Llama 3.1-405B |
|---|---|---|
| LMArena Expert | 1445 | 1243 |
| GPQA Diamond | — | 50.9% |
| MMLU-Pro | — | 72.3% |
| Confabulations | — | 17.6% |
| GPQA (HELM) | — | 52.2% |
| ARC (AI2) Challenge | — | 95.3% |
| MMLU | — | 84.5% |
| TriviaQA | — | 82.7% |

## Multimodal

- Grok 4.20 Multi-Agent: 40.5 (#48)
- Llama 3.1-405B: —

| Benchmark | Grok 4.20 Multi-Agent | Llama 3.1-405B |
|---|---|---|
| LMArena Vision | 1259 | — |

## Multilingual

- Grok 4.20 Multi-Agent: 54.4 (#43)
- Llama 3.1-405B: 40.7 (#214)

| Benchmark | Grok 4.20 Multi-Agent | Llama 3.1-405B |
|---|---|---|
| LMArena Non-English | 1440 | 1248 |
| LMArena Chinese | 1475 | 1242 |
| LMArena French | 1466 | 1279 |
| LMArena German | 1456 | 1252 |
| LMArena Japanese | 1405 | 1208 |
| LMArena Korean | 1416 | 1184 |
| LMArena Russian | 1457 | 1265 |
| LMArena Spanish | 1447 | 1260 |

## Instruction Following

- Grok 4.20 Multi-Agent: 74.8 (#84)
- Llama 3.1-405B: 65.9 (#214)

| Benchmark | Grok 4.20 Multi-Agent | Llama 3.1-405B |
|---|---|---|
| LMArena Instruction Following | 1420 | 1259 |
| IFEval | — | 81.1% |

## Long Context

- Grok 4.20 Multi-Agent: 43.7 (#88)
- Llama 3.1-405B: 38.4 (#197)

| Benchmark | Grok 4.20 Multi-Agent | Llama 3.1-405B |
|---|---|---|
| LMArena Longer Query | 1431 | 1266 |

## Writing & Preference

- Grok 4.20 Multi-Agent: 64.0 (#59)
- Llama 3.1-405B: 38.9 (#251)

| Benchmark | Grok 4.20 Multi-Agent | Llama 3.1-405B |
|---|---|---|
| LMArena Text | 1450 | 1284 |
| LMArena Creative Writing | 1436 | 1262 |
| LMArena Multi-Turn | 1452 | 1297 |
| EQ-Bench Creative Writing | — | 870 |
| WildBench | — | 78.3% |

## FAQ

### Is Grok 4.20 Multi-Agent better than Llama 3.1-405B?

Grok 4.20 Multi-Agent is the stronger model overall, scoring 46.2 to 30.7 on the Noometry Index.

### Is Grok 4.20 Multi-Agent or Llama 3.1-405B better for coding?

Grok 4.20 Multi-Agent scores higher on coding benchmarks: 43.0 versus 33.1 in the Noometry coding category.

### How many benchmarks do Grok 4.20 Multi-Agent and Llama 3.1-405B share?

17 benchmarks have published results for both models. Grok 4.20 Multi-Agent has 20 scored results on Noometry and Llama 3.1-405B has 42.