Model comparison
Grok 4.6 vs Llama 4 Scout
Grok 4.6 is the stronger model overall, scoring 56.9 to 27.7 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.
Last verified . 27 shared benchmarks.
MetaSummary
- 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 61.4 to 9.1.
- 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.
Side by side
| Grok 4.6 | Llama 4 Scout | |
|---|---|---|
| Provider | xAI | Meta |
| Noometry Index | 56.9 | 27.7 |
| Released | 2026-08-12 | 2025-04-05 |
| Weights | Proprietary | Open |
| Context window | 500K | 128K |
| Max output | 500K | 4K |
| Input $ / M tokens | $2 | $0.10 |
| Output $ / M tokens | $6 | $0.30 |
| Results tracked | 49 | 43 |
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Category by category
Coding Grok 4.6 leads
Grok 4.6: 58.5 (#16), Llama 4 Scout: 20.2 (#339)
| Benchmark | Grok 4.6 | Llama 4 Scout |
|---|---|---|
| SciCode | 56.5% | 17% |
| LMArena Coding | 1465 | 1286 |
| DeepSWE | 67.5% | — |
| FrontierCode | 48% | — |
| SWE-bench Verified (bash only) | — | 9.1% |
| CursorBench | 41.4% | — |
| LMArena WebDev | 1617 | — |
| FrontierSWE | 25.3% | — |
| WeirdML | 67.3% | — |
| BigCodeBench Complete | — | 43.1% |
| ALE-Bench | 1,508 | — |
Agentic & Tool Use Grok 4.6 leads
Grok 4.6: 39.4 (#27), Llama 4 Scout: 24.6 (#119)
| 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 leads
Grok 4.6: 61.4 (#20), Llama 4 Scout: 9.1 (#345)
| 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 | 1447 | 1266 |
| DTBench | 97.3% | 57.9% |
| LMCA | 48.5% | 12% |
| Epoch Capabilities Index | 156.44 | 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 leads
Grok 4.6: 67.0 (#24), Llama 4 Scout: 19.6 (#286)
| Benchmark | Grok 4.6 | Llama 4 Scout |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 99.2% | 7.8% |
| LMArena Math | 1423 | 1287 |
| 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 leads
Grok 4.6: 63.3 (#20), Llama 4 Scout: 31.9 (#217)
| Benchmark | Grok 4.6 | Llama 4 Scout |
|---|---|---|
| GPQA Diamond | 94% | 51.8% |
| LMArena Expert | 1467 | 1235 |
| SimpleQA Verified | 49.3% | — |
| MMLU-Pro | — | 74.2% |
| Vectara Hallucination Rate | — | 7.7% |
| GPQA (HELM) | — | 50.7% |
Multimodal Grok 4.6 leads
Grok 4.6: 43.6 (#23), Llama 4 Scout: 32.2 (#102)
| Benchmark | Grok 4.6 | Llama 4 Scout |
|---|---|---|
| LMArena Vision | 1263 | 1118 |
| Blueprint-Bench 2 | 33.2% | — |
| Furniture Assembly | 40% | — |
| LMArena Document | 1452 | — |
| SpatialViz-Bench | — | 34.2% |
Multilingual Grok 4.6 leads
Grok 4.6: 53.0 (#74), Llama 4 Scout: 41.0 (#212)
| Benchmark | Grok 4.6 | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1420 | 1252 |
| LMArena Chinese | 1480 | 1255 |
| LMArena French | 1461 | 1282 |
| LMArena German | 1431 | 1272 |
| LMArena Japanese | 1376 | 1206 |
| LMArena Korean | 1397 | 1207 |
| LMArena Russian | 1422 | 1263 |
| LMArena Spanish | 1404 | 1278 |
Instruction Following Grok 4.6 leads
Grok 4.6: 75.4 (#63), Llama 4 Scout: 65.8 (#217)
| Benchmark | Grok 4.6 | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1431 | 1248 |
| IFEval | — | 81.8% |
Long Context Grok 4.6 leads
Grok 4.6: 44.5 (#66), Llama 4 Scout: 27.5 (#294)
| Benchmark | Grok 4.6 | Llama 4 Scout |
|---|---|---|
| LMArena Longer Query | 1454 | 1265 |
| Fiction.LiveBench | — | 36% |
Writing & Preference Grok 4.6 leads
Grok 4.6: 62.3 (#80), Llama 4 Scout: 37.0 (#261)
| Benchmark | Grok 4.6 | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1428 | 1279 |
| LMArena Creative Writing | 1428 | 1249 |
| LMArena Multi-Turn | 1425 | 1280 |
| EQ-Bench Creative Writing | — | 783 |
| WildBench | — | 78% |
Frequently asked questions
Is Grok 4.6 better than Llama 4 Scout?
Grok 4.6 is the stronger model overall, scoring 56.9 to 27.7 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: 58.5 versus 20.2 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.