Model comparison
Grok 4.20 Multi-Agent vs Llama 4 Scout
Grok 4.20 Multi-Agent is the stronger model overall, scoring 46.2 to 27.7 on the Noometry Index. Llama 4 Scout costs 10× less per token, which makes it the better buy when Grok 4.20 Multi-Agent's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
MetaSummary
- They share 18 benchmarks with published results for both. Grok 4.20 Multi-Agent scores higher in 9 categories and Llama 4 Scout in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.20 Multi-Agent leads 43.9 to 9.1.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $1.25 / $2.50 for Grok 4.20 Multi-Agent.
- Grok 4.20 Multi-Agent accepts more context: 1M tokens versus 128K.
- Llama 4 Scout has downloadable open weights; the other is API-only.
Side by side
| Grok 4.20 Multi-Agent | Llama 4 Scout | |
|---|---|---|
| Provider | xAI | Meta |
| Noometry Index | 46.2 | 27.7 |
| Released | 2026-03-09 | 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 | 20 | 43 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Grok 4.20 Multi-Agent leads
Grok 4.20 Multi-Agent: 43.0 (#92), Llama 4 Scout: 20.2 (#339)
| Benchmark | Grok 4.20 Multi-Agent | Llama 4 Scout |
|---|---|---|
| LMArena Coding | 1457 | 1286 |
| SWE-bench Verified (bash only) | — | 9.1% |
| SciCode | — | 17% |
| BigCodeBench Complete | — | 43.1% |
Agentic & Tool Use Not comparable
Grok 4.20 Multi-Agent: —, Llama 4 Scout: 24.6 (#119)
| Benchmark | Grok 4.20 Multi-Agent | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.1% |
| LMArena Search | 1204 | — |
Reasoning Grok 4.20 Multi-Agent leads
Grok 4.20 Multi-Agent: 43.9 (#48), Llama 4 Scout: 9.1 (#345)
| Benchmark | Grok 4.20 Multi-Agent | Llama 4 Scout |
|---|---|---|
| LMArena Hard Prompts | 1448 | 1266 |
| ARC-AGI-2 | — | 0% |
| Kagi LLM Benchmark | — | 36.9% |
| NYT Connections (extended) | 89.6% | — |
| ARC-AGI-1 | — | 0.5% |
| CritPt | — | 0% |
| DTBench | — | 57.9% |
| LMCA | — | 12% |
| Epoch Capabilities Index | — | 129.64 |
| ForecastBench | — | 57.5 |
Math Grok 4.20 Multi-Agent leads
Grok 4.20 Multi-Agent: 39.4 (#104), Llama 4 Scout: 19.6 (#286)
| Benchmark | Grok 4.20 Multi-Agent | Llama 4 Scout |
|---|---|---|
| LMArena Math | 1442 | 1287 |
| OTIS Mock AIME 2024-2025 | — | 7.8% |
| Omni-MATH | — | 37.3% |
| MATH Level 5 | — | 62.3% |
| FrontierMath (Feb 2025 set) | — | 0% |
Knowledge Grok 4.20 Multi-Agent leads
Grok 4.20 Multi-Agent: 40.4 (#119), Llama 4 Scout: 31.9 (#217)
| Benchmark | Grok 4.20 Multi-Agent | Llama 4 Scout |
|---|---|---|
| LMArena Expert | 1445 | 1235 |
| GPQA Diamond | — | 51.8% |
| MMLU-Pro | — | 74.2% |
| Vectara Hallucination Rate | — | 7.7% |
| GPQA (HELM) | — | 50.7% |
Multimodal Grok 4.20 Multi-Agent leads
Grok 4.20 Multi-Agent: 40.5 (#48), Llama 4 Scout: 32.2 (#102)
| Benchmark | Grok 4.20 Multi-Agent | Llama 4 Scout |
|---|---|---|
| LMArena Vision | 1259 | 1118 |
| SpatialViz-Bench | — | 34.2% |
Multilingual Grok 4.20 Multi-Agent leads
Grok 4.20 Multi-Agent: 54.4 (#43), Llama 4 Scout: 41.0 (#212)
| Benchmark | Grok 4.20 Multi-Agent | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1440 | 1252 |
| LMArena Chinese | 1475 | 1255 |
| LMArena French | 1466 | 1282 |
| LMArena German | 1456 | 1272 |
| LMArena Japanese | 1405 | 1206 |
| LMArena Korean | 1416 | 1207 |
| LMArena Russian | 1457 | 1263 |
| LMArena Spanish | 1447 | 1278 |
Instruction Following Grok 4.20 Multi-Agent leads
Grok 4.20 Multi-Agent: 74.8 (#84), Llama 4 Scout: 65.8 (#217)
| Benchmark | Grok 4.20 Multi-Agent | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1420 | 1248 |
| IFEval | — | 81.8% |
Long Context Grok 4.20 Multi-Agent leads
Grok 4.20 Multi-Agent: 43.7 (#88), Llama 4 Scout: 27.5 (#294)
| Benchmark | Grok 4.20 Multi-Agent | Llama 4 Scout |
|---|---|---|
| LMArena Longer Query | 1431 | 1265 |
| Fiction.LiveBench | — | 36% |
Writing & Preference Grok 4.20 Multi-Agent leads
Grok 4.20 Multi-Agent: 64.0 (#59), Llama 4 Scout: 37.0 (#261)
| Benchmark | Grok 4.20 Multi-Agent | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1450 | 1279 |
| LMArena Creative Writing | 1436 | 1249 |
| LMArena Multi-Turn | 1452 | 1280 |
| EQ-Bench Creative Writing | — | 783 |
| WildBench | — | 78% |
Frequently asked questions
Is Grok 4.20 Multi-Agent better than Llama 4 Scout?
Grok 4.20 Multi-Agent is the stronger model overall, scoring 46.2 to 27.7 on the Noometry Index. Llama 4 Scout costs 10× less per token, which makes it the better buy when Grok 4.20 Multi-Agent's lead doesn't matter for your workload.
Which is cheaper, Grok 4.20 Multi-Agent 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 4.20 Multi-Agent lists at $1.25 and $2.50.
Is Grok 4.20 Multi-Agent or Llama 4 Scout better for coding?
Grok 4.20 Multi-Agent scores higher on coding benchmarks: 43.0 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
Grok 4.20 Multi-Agent does, with 1M tokens against 128K.
How many benchmarks do Grok 4.20 Multi-Agent and Llama 4 Scout share?
18 benchmarks have published results for both models. Grok 4.20 Multi-Agent has 20 scored results on Noometry and Llama 4 Scout has 43.