# Grok 4.6 vs Llama 4 Scout

> Grok 4.6 is the stronger model overall, scoring 56.8 to 27.4 on the Noometry Index. Llama 4 Scout costs 20× less per token, which makes it the better buy when Grok 4.6's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/grok-4-6-vs-llama-4-scout
- Last updated: 2026-10-10
- Shared benchmarks: 27

## Summary

- They share 27 benchmarks with published results for both. Grok 4.6 scores higher in 10 categories and Llama 4 Scout in 0 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.6 leads 61.3 to 9.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 99.2% for Grok 4.6 and 7.8% for Llama 4 Scout.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $2 / $6 for Grok 4.6.
- Grok 4.6 accepts more context: 500K tokens versus 128K.
- Llama 4 Scout has downloadable open weights; the other is API-only.

## Snapshot

| | Grok 4.6 | Llama 4 Scout |
|---|---|---|
| Provider | xAI | Meta |
| Noometry Index | 56.8 | 27.4 |
| Rank | 19 | 323 |
| Context | 500K | 128K |
| Input $/M | $2 | $0.10 |
| Output $/M | $6 | $0.30 |
| Weights | Proprietary | Open |

## Coding

- Grok 4.6: 58.0 (#14)
- Llama 4 Scout: 19.9 (#334)

| Benchmark | Grok 4.6 | Llama 4 Scout |
|---|---|---|
| SciCode | 56.5% | 17% |
| LMArena Coding | 1464 | 1286 |
| DeepSWE | 67.5% | — |
| FrontierCode | 48% | — |
| SWE-bench Verified (bash only) | — | 9.1% |
| CursorBench | 41.4% | — |
| LMArena WebDev | 1620 | — |
| FrontierSWE | 25.3% | — |
| WeirdML | 67.3% | — |
| BigCodeBench Complete | — | 43.1% |
| ALE-Bench | 1,508 | — |

## Agentic & Tool Use

- Grok 4.6: 39.4 (#25)
- Llama 4 Scout: 24.4 (#109)

| Benchmark | Grok 4.6 | Llama 4 Scout |
|---|---|---|
| APEX-Agents | 65.3% | — |
| Berkeley Function Calling Leaderboard | — | 28.1% |
| GDP.pdf | 17.2% | — |
| Vending-Bench 2 | 9,047 | — |

## Reasoning

- Grok 4.6: 61.3 (#18)
- Llama 4 Scout: 9.2 (#339)

| Benchmark | Grok 4.6 | Llama 4 Scout |
|---|---|---|
| ARC-AGI-2 | 67.1% | 0% |
| ARC-AGI-1 | 87.5% | 0.5% |
| CritPt | 19.7% | 0% |
| LMArena Hard Prompts | 1445 | 1266 |
| DTBench | 97.3% | 57.9% |
| LMCA | 48.5% | 12% |
| Epoch Capabilities Index | 156.48 | 129.64 |
| SimpleBench | 75.9% | — |
| Kagi LLM Benchmark | — | 36.9% |
| NYT Connections (extended) | 80% | — |
| Chess Puzzles | 40% | — |
| EBR-Bench | 30.5% | — |
| Mystery Game Puzzles | 34% | — |
| ForecastBench | — | 57.5 |

## Math

- Grok 4.6: 66.8 (#19)
- Llama 4 Scout: 18.4 (#280)

| Benchmark | Grok 4.6 | Llama 4 Scout |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 99.2% | 7.8% |
| LMArena Math | 1416 | 1286 |
| FrontierMath (Tiers 1-3) | 66% | — |
| FrontierMath Tier 4 | 31.7% | — |
| ProofBench | 51% | — |
| Omni-MATH | — | 37.3% |
| MATH Level 5 | — | 62.3% |
| FrontierMath (Feb 2025 set) | — | 0% |

## Knowledge

- Grok 4.6: 63.3 (#18)
- Llama 4 Scout: 31.8 (#208)

| Benchmark | Grok 4.6 | Llama 4 Scout |
|---|---|---|
| GPQA Diamond | 94% | 51.8% |
| LMArena Expert | 1463 | 1236 |
| SimpleQA Verified | 49.3% | — |
| MMLU-Pro | — | 74.2% |
| Vectara Hallucination Rate | — | 7.7% |
| GPQA (HELM) | — | 50.7% |

## Multimodal

- Grok 4.6: 43.9 (#18)
- Llama 4 Scout: 31.6 (#98)

| Benchmark | Grok 4.6 | Llama 4 Scout |
|---|---|---|
| LMArena Vision | 1265 | 1118 |
| Blueprint-Bench 2 | 33.2% | — |
| Furniture Assembly | 40% | — |
| LMArena Document | 1452 | — |
| SpatialViz-Bench | — | 34.2% |

## Multilingual

- Grok 4.6: 53.3 (#67)
- Llama 4 Scout: 41.3 (#207)

| Benchmark | Grok 4.6 | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1422 | 1253 |
| LMArena Chinese | 1479 | 1256 |
| LMArena French | 1458 | 1283 |
| LMArena German | 1425 | 1274 |
| LMArena Japanese | 1383 | 1209 |
| LMArena Korean | 1395 | 1207 |
| LMArena Russian | 1427 | 1263 |
| LMArena Spanish | 1408 | 1277 |

## Instruction Following

- Grok 4.6: 75.6 (#59)
- Llama 4 Scout: 66.0 (#212)

| Benchmark | Grok 4.6 | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1431 | 1248 |
| IFEval | — | 81.8% |

## Long Context

- Grok 4.6: 44.4 (#62)
- Llama 4 Scout: 27.5 (#289)

| Benchmark | Grok 4.6 | Llama 4 Scout |
|---|---|---|
| LMArena Longer Query | 1453 | 1265 |
| Fiction.LiveBench | — | 36% |

## Writing & Preference

- Grok 4.6: 62.5 (#77)
- Llama 4 Scout: 37.1 (#256)

| Benchmark | Grok 4.6 | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1427 | 1279 |
| LMArena Creative Writing | 1430 | 1249 |
| LMArena Multi-Turn | 1422 | 1281 |
| EQ-Bench Creative Writing | — | 783 |
| WildBench | — | 78% |

## FAQ

### Is Grok 4.6 better than Llama 4 Scout?

Grok 4.6 is the stronger model overall, scoring 56.8 to 27.4 on the Noometry Index. Llama 4 Scout costs 20× less per token, which makes it the better buy when Grok 4.6's lead doesn't matter for your workload.

### Which is cheaper, Grok 4.6 or Llama 4 Scout?

Llama 4 Scout is cheaper. It lists at $0.10 per million input tokens and $0.30 per million output tokens; Grok 4.6 lists at $2 and $6.

### Is Grok 4.6 or Llama 4 Scout better for coding?

Grok 4.6 scores higher on coding benchmarks: 58.0 versus 19.9 in the Noometry coding category.

### Which has the bigger context window?

Grok 4.6 does, with 500K tokens against 128K.

### How many benchmarks do Grok 4.6 and Llama 4 Scout share?

27 benchmarks have published results for both models. Grok 4.6 has 49 scored results on Noometry and Llama 4 Scout has 43.