Model comparison
Grok-2 (Dec 2024) vs Llama 4 Scout
Grok-2 (Dec 2024) is the stronger model overall, scoring 33.7 to 27.7 on the Noometry Index.
Last verified . 23 shared benchmarks.
MetaSummary
- They share 23 benchmarks with published results for both. Grok-2 (Dec 2024) scores higher in 7 categories and Llama 4 Scout in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in coding, where Grok-2 (Dec 2024) leads 33.3 to 20.2.
- The biggest single-benchmark swing is DTBench: 65.2% for Grok-2 (Dec 2024) and 57.9% for Llama 4 Scout.
- Llama 4 Scout has downloadable open weights; the other is API-only.
Side by side
| Grok-2 (Dec 2024) | Llama 4 Scout | |
|---|---|---|
| Provider | xAI | Meta |
| Noometry Index | 33.7 | 27.7 |
| Released | 2024-08-13 | 2025-04-05 |
| Weights | Proprietary | Open |
| Context window | — | 128K |
| Max output | — | 4K |
| Input $ / M tokens | — | $0.10 |
| Output $ / M tokens | — | $0.30 |
| Results tracked | 34 | 43 |
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Category by category
Coding Grok-2 (Dec 2024) leads
Grok-2 (Dec 2024): 33.3 (#258), Llama 4 Scout: 20.2 (#339)
| Benchmark | Grok-2 (Dec 2024) | Llama 4 Scout |
|---|---|---|
| LMArena Coding | 1287 | 1286 |
| SWE-bench Verified (bash only) | — | 9.1% |
| SciCode | — | 17% |
| WeirdML | 22.2% | — |
| LiveBench Coding | 46.4% | — |
| BigCodeBench Complete | — | 43.1% |
Agentic & Tool Use Not comparable
Grok-2 (Dec 2024): —, Llama 4 Scout: 24.6 (#119)
| Benchmark | Grok-2 (Dec 2024) | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.1% |
Reasoning Grok-2 (Dec 2024) leads
Grok-2 (Dec 2024): 16.9 (#299), Llama 4 Scout: 9.1 (#345)
| Benchmark | Grok-2 (Dec 2024) | Llama 4 Scout |
|---|---|---|
| LMArena Hard Prompts | 1272 | 1266 |
| DTBench | 65.2% | 57.9% |
| Epoch Capabilities Index | 130.48 | 129.64 |
| ARC-AGI-2 | — | 0% |
| SimpleBench | 22.7% | — |
| Kagi LLM Benchmark | — | 36.9% |
| ARC-AGI-1 | — | 0.5% |
| CritPt | — | 0% |
| LiveBench Reasoning | 54.8% | — |
| LiveBench Data Analysis | 54.5% | — |
| LMCA | — | 12% |
| ForecastBench | — | 57.5 |
| LiveBench | 54.3% | — |
Math Grok-2 (Dec 2024) leads
Grok-2 (Dec 2024): 20.8 (#284), Llama 4 Scout: 19.6 (#286)
| Benchmark | Grok-2 (Dec 2024) | Llama 4 Scout |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 11.5% | 7.8% |
| LMArena Math | 1283 | 1287 |
| MATH Level 5 | 63.5% | 62.3% |
| FrontierMath (Feb 2025 set) | 0.7% | 0% |
| Omni-MATH | — | 37.3% |
| LiveBench Math | 54.9% | — |
Knowledge Llama 4 Scout leads
Grok-2 (Dec 2024): 29.8 (#233), Llama 4 Scout: 31.9 (#217)
| Benchmark | Grok-2 (Dec 2024) | Llama 4 Scout |
|---|---|---|
| GPQA Diamond | 53.8% | 51.8% |
| LMArena Expert | 1254 | 1235 |
| MMLU-Pro | — | 74.2% |
| Confabulations | 20.1% | — |
| Vectara Hallucination Rate | — | 7.7% |
| GPQA (HELM) | — | 50.7% |
Multimodal Not comparable
Grok-2 (Dec 2024): —, Llama 4 Scout: 32.2 (#102)
| Benchmark | Grok-2 (Dec 2024) | Llama 4 Scout |
|---|---|---|
| LMArena Vision | — | 1118 |
| SpatialViz-Bench | — | 34.2% |
Multilingual Grok-2 (Dec 2024) leads
Grok-2 (Dec 2024): 43.1 (#188), Llama 4 Scout: 41.0 (#212)
| Benchmark | Grok-2 (Dec 2024) | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1282 | 1252 |
| LMArena Chinese | 1289 | 1255 |
| LMArena French | 1318 | 1282 |
| LMArena German | 1287 | 1272 |
| LMArena Japanese | 1244 | 1206 |
| LMArena Korean | 1237 | 1207 |
| LMArena Russian | 1286 | 1263 |
| LMArena Spanish | 1281 | 1278 |
Instruction Following Grok-2 (Dec 2024) leads
Grok-2 (Dec 2024): 66.9 (#202), Llama 4 Scout: 65.8 (#217)
| Benchmark | Grok-2 (Dec 2024) | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1270 | 1248 |
| LiveBench Instruction Following | 69.6% | — |
| IFEval | — | 81.8% |
Long Context Grok-2 (Dec 2024) leads
Grok-2 (Dec 2024): 38.8 (#190), Llama 4 Scout: 27.5 (#294)
| Benchmark | Grok-2 (Dec 2024) | Llama 4 Scout |
|---|---|---|
| LMArena Longer Query | 1276 | 1265 |
| Fiction.LiveBench | — | 36% |
Writing & Preference Grok-2 (Dec 2024) leads
Grok-2 (Dec 2024): 48.6 (#198), Llama 4 Scout: 37.0 (#261)
| Benchmark | Grok-2 (Dec 2024) | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1305 | 1279 |
| LMArena Creative Writing | 1284 | 1249 |
| LMArena Multi-Turn | 1290 | 1280 |
| Short-Story Creative Writing | 63.6% | — |
| EQ-Bench Creative Writing | — | 783 |
| WildBench | — | 78% |
| LiveBench Language | 45.6% | — |
Frequently asked questions
Is Grok-2 (Dec 2024) better than Llama 4 Scout?
Grok-2 (Dec 2024) is the stronger model overall, scoring 33.7 to 27.7 on the Noometry Index.
Is Grok-2 (Dec 2024) or Llama 4 Scout better for coding?
Grok-2 (Dec 2024) scores higher on coding benchmarks: 33.3 versus 20.2 in the Noometry coding category.
How many benchmarks do Grok-2 (Dec 2024) and Llama 4 Scout share?
23 benchmarks have published results for both models. Grok-2 (Dec 2024) has 34 scored results on Noometry and Llama 4 Scout has 43.