Model comparison
Grok 4.3 vs Llama 4 Scout
Grok 4.3 is the stronger model overall, scoring 43.8 to 27.7 on the Noometry Index. Llama 4 Scout costs 10× less per token, which makes it the better buy when Grok 4.3's lead doesn't matter for your workload.
Last verified . 26 shared benchmarks.
MetaSummary
- They share 26 benchmarks with published results for both. Grok 4.3 scores higher in 9 categories and Llama 4 Scout in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.3 leads 35.9 to 9.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.3% for Grok 4.3 and 7.8% for Llama 4 Scout.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $1.25 / $2.50 for Grok 4.3.
- Grok 4.3 accepts more context: 1M tokens versus 128K.
- Llama 4 Scout has downloadable open weights; the other is API-only.
Side by side
| Grok 4.3 | Llama 4 Scout | |
|---|---|---|
| Provider | xAI | Meta |
| Noometry Index | 43.8 | 27.7 |
| Released | 2026-04-17 | 2025-04-05 |
| Weights | Proprietary | Open |
| Context window | 1M | 128K |
| Max output | 30K | 4K |
| Input $ / M tokens | $1.25 | $0.10 |
| Output $ / M tokens | $2.50 | $0.30 |
| Results tracked | 40 | 43 |
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Category by category
Coding Grok 4.3 leads
Grok 4.3: 41.6 (#121), Llama 4 Scout: 20.2 (#339)
| Benchmark | Grok 4.3 | Llama 4 Scout |
|---|---|---|
| SciCode | 47.3% | 17% |
| LMArena Coding | 1415 | 1286 |
| SWE-bench Verified (bash only) | — | 9.1% |
| LMArena WebDev | 1357 | — |
| WeirdML | 49.9% | — |
| BigCodeBench Complete | — | 43.1% |
| ALE-Bench | 944.17 | — |
Agentic & Tool Use Grok 4.3 leads
Grok 4.3: 27.7 (#99), Llama 4 Scout: 24.6 (#119)
| Benchmark | Grok 4.3 | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.1% |
| GDP.pdf | 8% | — |
| LMArena Search | 1165 | — |
| Vending-Bench 2 | 35.26 | — |
Reasoning Grok 4.3 leads
Grok 4.3: 35.9 (#68), Llama 4 Scout: 9.1 (#345)
| Benchmark | Grok 4.3 | Llama 4 Scout |
|---|---|---|
| CritPt | 8% | 0% |
| LMArena Hard Prompts | 1396 | 1266 |
| DTBench | 90.7% | 57.9% |
| LMCA | 38.3% | 12% |
| Epoch Capabilities Index | 149.16 | 129.64 |
| ForecastBench | 60.3 | 57.5 |
| ARC-AGI-2 | — | 0% |
| Kagi LLM Benchmark | — | 36.9% |
| NYT Connections (extended) | 55.2% | — |
| ARC-AGI-1 | — | 0.5% |
| Chess Puzzles | 25% | — |
Math Grok 4.3 leads
Grok 4.3: 46.0 (#74), Llama 4 Scout: 19.6 (#286)
| Benchmark | Grok 4.3 | Llama 4 Scout |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.3% | 7.8% |
| LMArena Math | 1388 | 1287 |
| FrontierMath (Tiers 1-3) | 42.8% | — |
| FrontierMath Tier 4 | 14.6% | — |
| ProofBench | 11% | — |
| Omni-MATH | — | 37.3% |
| MATH Level 5 | — | 62.3% |
| FrontierMath (Feb 2025 set) | — | 0% |
Knowledge Grok 4.3 leads
Grok 4.3: 52.5 (#62), Llama 4 Scout: 31.9 (#217)
| Benchmark | Grok 4.3 | Llama 4 Scout |
|---|---|---|
| GPQA Diamond | 88.8% | 51.8% |
| LMArena Expert | 1385 | 1235 |
| SimpleQA Verified | 33.2% | — |
| MMLU-Pro | — | 74.2% |
| Vectara Hallucination Rate | — | 7.7% |
| GPQA (HELM) | — | 50.7% |
Multimodal Too close to call
Grok 4.3: 31.6 (#104), Llama 4 Scout: 32.2 (#102)
| Benchmark | Grok 4.3 | Llama 4 Scout |
|---|---|---|
| LMArena Vision | 1229 | 1118 |
| Blueprint-Bench 2 | 0% | — |
| SpatialViz-Bench | — | 34.2% |
Multilingual Grok 4.3 leads
Grok 4.3: 50.5 (#120), Llama 4 Scout: 41.0 (#212)
| Benchmark | Grok 4.3 | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1385 | 1252 |
| LMArena Chinese | 1422 | 1255 |
| LMArena French | 1412 | 1282 |
| LMArena German | 1395 | 1272 |
| LMArena Japanese | 1379 | 1206 |
| LMArena Korean | 1356 | 1207 |
| LMArena Russian | 1399 | 1263 |
| LMArena Spanish | 1398 | 1278 |
Instruction Following Grok 4.3 leads
Grok 4.3: 72.1 (#140), Llama 4 Scout: 65.8 (#217)
| Benchmark | Grok 4.3 | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1366 | 1248 |
| IFEval | — | 81.8% |
Long Context Grok 4.3 leads
Grok 4.3: 42.5 (#123), Llama 4 Scout: 27.5 (#294)
| Benchmark | Grok 4.3 | Llama 4 Scout |
|---|---|---|
| LMArena Longer Query | 1393 | 1265 |
| Fiction.LiveBench | — | 36% |
Writing & Preference Grok 4.3 leads
Grok 4.3: 58.5 (#118), Llama 4 Scout: 37.0 (#261)
| Benchmark | Grok 4.3 | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1397 | 1279 |
| LMArena Creative Writing | 1380 | 1249 |
| LMArena Multi-Turn | 1406 | 1280 |
| EQ-Bench Creative Writing | — | 783 |
| WildBench | — | 78% |
| EQ-Bench 4 | 1075 | — |
Frequently asked questions
Is Grok 4.3 better than Llama 4 Scout?
Grok 4.3 is the stronger model overall, scoring 43.8 to 27.7 on the Noometry Index. Llama 4 Scout costs 10× less per token, which makes it the better buy when Grok 4.3's lead doesn't matter for your workload.
Which is cheaper, Grok 4.3 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 4.3 lists at $1.25 and $2.50.
Is Grok 4.3 or Llama 4 Scout better for coding?
Grok 4.3 scores higher on coding benchmarks: 41.6 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
Grok 4.3 does, with 1M tokens against 128K.
How many benchmarks do Grok 4.3 and Llama 4 Scout share?
26 benchmarks have published results for both models. Grok 4.3 has 40 scored results on Noometry and Llama 4 Scout has 43.