Model comparison
Command R vs Llama 4 Scout
Command R is the stronger model overall, scoring 31.4 to 27.7 on the Noometry Index. Llama 4 Scout costs 1.7× less per token, which makes it the better buy when Command R's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
Cohere
MetaSummary
- They share 20 benchmarks with published results for both. Command R scores higher in 5 categories and Llama 4 Scout in 3 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in coding, where Command R leads 29.3 to 20.2.
- The biggest single-benchmark swing is DTBench: 46.4% for Command R and 57.9% for Llama 4 Scout.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $0.15 / $0.60 for Command R.
Side by side
| Command R | Llama 4 Scout | |
|---|---|---|
| Provider | Cohere | Meta |
| Noometry Index | 31.4 | 27.7 |
| Released | 2024-08-30 | 2025-04-05 |
| Weights | Open | Open |
| Context window | 128K | 128K |
| Max output | 4K | 4K |
| Input $ / M tokens | $0.15 | $0.10 |
| Output $ / M tokens | $0.60 | $0.30 |
| Results tracked | 29 | 43 |
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Category by category
Coding Command R leads
Command R: 29.3 (#306), Llama 4 Scout: 20.2 (#339)
| Benchmark | Command R | Llama 4 Scout |
|---|---|---|
| LMArena Coding | 1169 | 1286 |
| BigCodeBench Complete | 45.2% | 43.1% |
| SWE-bench Verified (bash only) | — | 9.1% |
| SciCode | — | 17% |
| BigCodeBench Instruct | 37.1% | — |
| LiveBench Coding | 17.9% | — |
Agentic & Tool Use Not comparable
Command R: —, Llama 4 Scout: 24.6 (#119)
| Benchmark | Command R | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.1% |
Reasoning Command R leads
Command R: 13.8 (#331), Llama 4 Scout: 9.1 (#345)
| Benchmark | Command R | Llama 4 Scout |
|---|---|---|
| LMArena Hard Prompts | 1164 | 1266 |
| DTBench | 46.4% | 57.9% |
| LMCA | 9.2% | 12% |
| ARC-AGI-2 | — | 0% |
| Kagi LLM Benchmark | — | 36.9% |
| ARC-AGI-1 | — | 0.5% |
| CritPt | — | 0% |
| LiveBench Reasoning | 21.9% | — |
| LiveBench Data Analysis | 33.3% | — |
| Epoch Capabilities Index | — | 129.64 |
| ForecastBench | — | 57.5 |
| LiveBench | 27.5% | — |
Math Command R leads
Command R: 28.0 (#246), Llama 4 Scout: 19.6 (#286)
| Benchmark | Command R | Llama 4 Scout |
|---|---|---|
| LMArena Math | 1155 | 1287 |
| OTIS Mock AIME 2024-2025 | — | 7.8% |
| Omni-MATH | — | 37.3% |
| LiveBench Math | 19.4% | — |
| MATH Level 5 | — | 62.3% |
| FrontierMath (Feb 2025 set) | — | 0% |
Knowledge Too close to call
Command R: 31.0 (#221), Llama 4 Scout: 31.9 (#217)
| Benchmark | Command R | Llama 4 Scout |
|---|---|---|
| LMArena Expert | 1138 | 1235 |
| GPQA Diamond | — | 51.8% |
| MMLU-Pro | — | 74.2% |
| Vectara Hallucination Rate | — | 7.7% |
| GPQA (HELM) | — | 50.7% |
| MMLU | 65.2% | — |
Multimodal Not comparable
Command R: —, Llama 4 Scout: 32.2 (#102)
| Benchmark | Command R | Llama 4 Scout |
|---|---|---|
| LMArena Vision | — | 1118 |
| SpatialViz-Bench | — | 34.2% |
Multilingual Llama 4 Scout leads
Command R: 35.7 (#245), Llama 4 Scout: 41.0 (#212)
| Benchmark | Command R | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1174 | 1252 |
| LMArena Chinese | 1182 | 1255 |
| LMArena French | 1162 | 1282 |
| LMArena German | 1176 | 1272 |
| LMArena Japanese | 1143 | 1206 |
| LMArena Korean | 1163 | 1207 |
| LMArena Russian | 1174 | 1263 |
| LMArena Spanish | 1151 | 1278 |
Instruction Following Llama 4 Scout leads
Command R: 58.1 (#261), Llama 4 Scout: 65.8 (#217)
| Benchmark | Command R | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1167 | 1248 |
| LiveBench Instruction Following | 55.6% | — |
| IFEval | — | 81.8% |
Long Context Command R leads
Command R: 36.3 (#231), Llama 4 Scout: 27.5 (#294)
| Benchmark | Command R | Llama 4 Scout |
|---|---|---|
| LMArena Longer Query | 1198 | 1265 |
| Fiction.LiveBench | — | 36% |
Writing & Preference Command R leads
Command R: 38.2 (#254), Llama 4 Scout: 37.0 (#261)
| Benchmark | Command R | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1187 | 1279 |
| LMArena Creative Writing | 1170 | 1249 |
| LMArena Multi-Turn | 1163 | 1280 |
| EQ-Bench Creative Writing | — | 783 |
| WildBench | — | 78% |
| LiveBench Language | 16.7% | — |
Frequently asked questions
Is Command R better than Llama 4 Scout?
Command R is the stronger model overall, scoring 31.4 to 27.7 on the Noometry Index. Llama 4 Scout costs 1.7× less per token, which makes it the better buy when Command R's lead doesn't matter for your workload.
Which is cheaper, Command R 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; Command R lists at $0.15 and $0.60.
Is Command R or Llama 4 Scout better for coding?
Command R scores higher on coding benchmarks: 29.3 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
Both accept 128K tokens.
How many benchmarks do Command R and Llama 4 Scout share?
20 benchmarks have published results for both models. Command R has 29 scored results on Noometry and Llama 4 Scout has 43.