Model comparison
Command R+ vs Llama 4 Maverick
Command R+ is the stronger model overall, scoring 32.4 to 30.9 on the Noometry Index. Llama 4 Maverick costs 14× less per token, which makes it the better buy when Command R+'s lead doesn't matter for your workload.
Last verified . 24 shared benchmarks.
Cohere
MetaSummary
- They share 24 benchmarks with published results for both. Command R+ scores higher in 5 categories and Llama 4 Maverick in 3 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in instruction following, where Llama 4 Maverick leads 71.7 to 60.0.
- The biggest single-benchmark swing is BigCodeBench Complete: 41.9% for Command R+ and 61.4% for Llama 4 Maverick.
- Llama 4 Maverick is cheaper at $0.19 / $0.65 per million input/output tokens, against $2.50 / $10 for Command R+.
Side by side
| Command R+ | Llama 4 Maverick | |
|---|---|---|
| Provider | Cohere | Meta |
| Noometry Index | 32.4 | 30.9 |
| Released | 2024-08-30 | 2025-04-05 |
| Weights | Open | Open |
| Context window | 128K | 128K |
| Max output | 4K | 4K |
| Input $ / M tokens | $2.50 | $0.19 |
| Output $ / M tokens | $10 | $0.65 |
| Results tracked | 34 | 54 |
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Category by category
Coding Command R+ leads
Command R+: 29.1 (#309), Llama 4 Maverick: 26.6 (#324)
| Benchmark | Command R+ | Llama 4 Maverick |
|---|---|---|
| BigCodeBench Instruct | 33.8% | 49.7% |
| LMArena Coding | 1187 | 1302 |
| BigCodeBench Complete | 41.9% | 61.4% |
| SWE-bench Verified (bash only) | — | 21% |
| Aider Polyglot | — | 15.6% |
| SciCode | — | 33.1% |
| WeirdML | — | 24.5% |
| LiveBench Coding | 19.1% | — |
| ALE-Bench | — | 172.97 |
| HumanEval+ | 56.7% | — |
| MBPP+ | 63.5% | — |
Agentic & Tool Use Not comparable
Command R+: —, Llama 4 Maverick: 28.2 (#91)
| Benchmark | Command R+ | Llama 4 Maverick |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 37.3% |
Reasoning Too close to call
Command R+: 9.2 (#344), Llama 4 Maverick: 10.1 (#342)
| Benchmark | Command R+ | Llama 4 Maverick |
|---|---|---|
| SimpleBench | 17.4% | 27.7% |
| LMArena Hard Prompts | 1186 | 1281 |
| DTBench | 54.9% | 61.9% |
| LMCA | 5% | 15.9% |
| Epoch Capabilities Index | 119.34 | 132.2 |
| ARC-AGI-2 | — | 0% |
| Kagi LLM Benchmark | — | 55.9% |
| NYT Connections (extended) | — | 8% |
| ARC-AGI-1 | — | 4.4% |
| CritPt | — | 0% |
| EnigmaEval | — | 0.6% |
| LiveBench Reasoning | 24.8% | — |
| LiveBench Data Analysis | 38.1% | — |
| ForecastBench | — | 57.5 |
| LiveBench | 31.8% | — |
Math Command R+ leads
Command R+: 28.9 (#242), Llama 4 Maverick: 26.0 (#262)
| Benchmark | Command R+ | Llama 4 Maverick |
|---|---|---|
| LMArena Math | 1188 | 1299 |
| OTIS Mock AIME 2024-2025 | — | 20.6% |
| Omni-MATH | — | 42.2% |
| LiveBench Math | 21.3% | — |
| MATH Level 5 | — | 73% |
| FrontierMath (Feb 2025 set) | — | 0.7% |
Knowledge Command R+ leads
Command R+: 36.4 (#169), Llama 4 Maverick: 33.4 (#204)
| Benchmark | Command R+ | Llama 4 Maverick |
|---|---|---|
| Vectara Hallucination Rate | 6.9% | 8.2% |
| LMArena Expert | 1174 | 1259 |
| GPQA Diamond | — | 67% |
| Humanity's Last Exam | — | 5.7% |
| MMLU-Pro | — | 81% |
| Confabulations | — | 22.6% |
| GPQA (HELM) | — | 65% |
| MMLU | 69.4% | — |
Multimodal Not comparable
Command R+: —, Llama 4 Maverick: 31.6 (#105)
| Benchmark | Command R+ | Llama 4 Maverick |
|---|---|---|
| LMArena Vision | — | 1142 |
| GeoBench | — | 52% |
| SpatialViz-Bench | — | 31.8% |
Multilingual Llama 4 Maverick leads
Command R+: 38.6 (#227), Llama 4 Maverick: 42.2 (#195)
| Benchmark | Command R+ | Llama 4 Maverick |
|---|---|---|
| LMArena Non-English | 1216 | 1269 |
| LMArena Chinese | 1226 | 1277 |
| LMArena French | 1209 | 1259 |
| LMArena German | 1216 | 1291 |
| LMArena Japanese | 1166 | 1207 |
| LMArena Korean | 1138 | 1203 |
| LMArena Russian | 1227 | 1286 |
| LMArena Spanish | 1189 | 1293 |
Instruction Following Llama 4 Maverick leads
Command R+: 60.0 (#254), Llama 4 Maverick: 71.7 (#146)
| Benchmark | Command R+ | Llama 4 Maverick |
|---|---|---|
| LMArena Instruction Following | 1197 | 1267 |
| LiveBench Instruction Following | 57.6% | — |
| IFEval | — | 90.8% |
Long Context Command R+ leads
Command R+: 37.3 (#219), Llama 4 Maverick: 31.4 (#279)
| Benchmark | Command R+ | Llama 4 Maverick |
|---|---|---|
| LMArena Longer Query | 1230 | 1280 |
| Fiction.LiveBench | — | 46.2% |
Writing & Preference Command R+ leads
Command R+: 43.5 (#228), Llama 4 Maverick: 38.8 (#252)
| Benchmark | Command R+ | Llama 4 Maverick |
|---|---|---|
| LMArena Text | 1229 | 1287 |
| LMArena Creative Writing | 1235 | 1267 |
| LMArena Multi-Turn | 1213 | 1289 |
| Short-Story Creative Writing | — | 62% |
| EQ-Bench Creative Writing | — | 860 |
| WildBench | — | 80% |
| LiveBench Language | 29.7% | — |
Frequently asked questions
Is Command R+ better than Llama 4 Maverick?
Command R+ is the stronger model overall, scoring 32.4 to 30.9 on the Noometry Index. Llama 4 Maverick costs 14× 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 Maverick?
Llama 4 Maverick is cheaper. It lists at $0.19 per million input tokens and $0.65 per million output tokens; Command R+ lists at $2.50 and $10.
Is Command R+ or Llama 4 Maverick better for coding?
Command R+ scores higher on coding benchmarks: 29.1 versus 26.6 in the Noometry coding category.
Which has the bigger context window?
Both accept 128K tokens.
How many benchmarks do Command R+ and Llama 4 Maverick share?
24 benchmarks have published results for both models. Command R+ has 34 scored results on Noometry and Llama 4 Maverick has 54.