Model comparison
Command R vs GPT-5.6 Luna
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 31.4 on the Noometry Index. Command R costs 1.7× less per token, which makes it the better buy when GPT-5.6 Luna's lead doesn't matter for your workload.
Last verified . 19 shared benchmarks.
CohereSummary
- They share 19 benchmarks with published results for both. Command R scores higher in 0 categories and GPT-5.6 Luna in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Luna leads 77.7 to 28.0.
- The biggest single-benchmark swing is DTBench: 46.4% for Command R and 89.1% for GPT-5.6 Luna.
- Command R is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.20 / $1.20 for GPT-5.6 Luna.
- GPT-5.6 Luna accepts more context: 1.05M tokens versus 128K.
- Command R has downloadable open weights; the other is API-only.
Side by side
| Command R | GPT-5.6 Luna | |
|---|---|---|
| Provider | Cohere | OpenAI |
| Noometry Index | 31.4 | 54.6 |
| Released | 2024-08-30 | 2026-07-09 |
| Weights | Open | Proprietary |
| Context window | 128K | 1.05M |
| Max output | 4K | 128K |
| Input $ / M tokens | $0.15 | $0.20 |
| Output $ / M tokens | $0.60 | $1.20 |
| Results tracked | 29 | 52 |
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Category by category
Coding GPT-5.6 Luna leads
Command R: 29.3 (#306), GPT-5.6 Luna: 54.5 (#28)
| Benchmark | Command R | GPT-5.6 Luna |
|---|---|---|
| LMArena Coding | 1169 | 1466 |
| DeepSWE | — | 67.2% |
| FrontierCode | — | 39.8% |
| CursorBench | — | 35.9% |
| LMArena WebDev | — | 1519 |
| SciCode | — | 53.6% |
| WeirdML | — | 60.9% |
| BigCodeBench Instruct | 37.1% | — |
| LiveBench Coding | 17.9% | — |
| BigCodeBench Complete | 45.2% | — |
| ALE-Bench | — | 1,667 |
Agentic & Tool Use Not comparable
Command R: —, GPT-5.6 Luna: 34.4 (#45)
| Benchmark | Command R | GPT-5.6 Luna |
|---|---|---|
| APEX-Agents | — | 43% |
| BALROG | — | 45.6% |
| GDP.pdf | — | 22.7% |
| Vending-Bench 2 | — | 4,095 |
Reasoning GPT-5.6 Luna leads
Command R: 13.8 (#331), GPT-5.6 Luna: 47.6 (#43)
| Benchmark | Command R | GPT-5.6 Luna |
|---|---|---|
| LMArena Hard Prompts | 1164 | 1451 |
| DTBench | 46.4% | 89.1% |
| LMCA | 9.2% | 48.5% |
| ARC-AGI-2 | — | 59.5% |
| SimpleBench | — | 46.8% |
| Kagi LLM Benchmark | — | 49.1% |
| NYT Connections (extended) | — | 69.4% |
| ARC-AGI-1 | — | 88% |
| CritPt | — | 20.6% |
| Chess Puzzles | — | 40% |
| LiveBench Reasoning | 21.9% | — |
| Mystery Game Puzzles | — | 21% |
| LiveBench Data Analysis | 33.3% | — |
| Surface Evolver Bench | — | 61.9% |
| Epoch Capabilities Index | — | 156.39 |
| LiveBench | 27.5% | — |
Math GPT-5.6 Luna leads
Command R: 28.0 (#246), GPT-5.6 Luna: 77.7 (#14)
| Benchmark | Command R | GPT-5.6 Luna |
|---|---|---|
| LMArena Math | 1155 | 1458 |
| FrontierMath (Tiers 1-3) | — | 82.1% |
| FrontierMath Tier 4 | — | 61% |
| OTIS Mock AIME 2024-2025 | — | 98.3% |
| ProofBench | — | 60% |
| LiveBench Math | 19.4% | — |
Knowledge GPT-5.6 Luna leads
Command R: 31.0 (#221), GPT-5.6 Luna: 58.5 (#34)
| Benchmark | Command R | GPT-5.6 Luna |
|---|---|---|
| LMArena Expert | 1138 | 1478 |
| GPQA Diamond | — | 91.6% |
| SimpleQA Verified | — | 41% |
| MMLU | 65.2% | — |
Multimodal Not comparable
Command R: —, GPT-5.6 Luna: 42.7 (#28)
| Benchmark | Command R | GPT-5.6 Luna |
|---|---|---|
| LMArena Vision | — | 1258 |
| Blueprint-Bench 2 | — | 22.6% |
| Furniture Assembly | — | 42.5% |
| LMArena Document | — | 1457 |
Multilingual GPT-5.6 Luna leads
Command R: 35.7 (#245), GPT-5.6 Luna: 52.8 (#78)
| Benchmark | Command R | GPT-5.6 Luna |
|---|---|---|
| LMArena Non-English | 1174 | 1417 |
| LMArena Chinese | 1182 | 1470 |
| LMArena French | 1162 | 1456 |
| LMArena German | 1176 | 1454 |
| LMArena Japanese | 1143 | 1411 |
| LMArena Korean | 1163 | 1415 |
| LMArena Russian | 1174 | 1428 |
| LMArena Spanish | 1151 | 1448 |
Instruction Following GPT-5.6 Luna leads
Command R: 58.1 (#261), GPT-5.6 Luna: 75.6 (#57)
| Benchmark | Command R | GPT-5.6 Luna |
|---|---|---|
| LMArena Instruction Following | 1167 | 1437 |
| LiveBench Instruction Following | 55.6% | — |
Long Context GPT-5.6 Luna leads
Command R: 36.3 (#231), GPT-5.6 Luna: 43.9 (#82)
| Benchmark | Command R | GPT-5.6 Luna |
|---|---|---|
| LMArena Longer Query | 1198 | 1436 |
Writing & Preference GPT-5.6 Luna leads
Command R: 38.2 (#254), GPT-5.6 Luna: 68.0 (#29)
| Benchmark | Command R | GPT-5.6 Luna |
|---|---|---|
| LMArena Text | 1187 | 1431 |
| LMArena Creative Writing | 1170 | 1396 |
| LMArena Multi-Turn | 1163 | 1434 |
| EQ-Bench Creative Writing | — | 1829 |
| EQ-Bench 4 | — | 1156 |
| LiveBench Language | 16.7% | — |
Frequently asked questions
Is Command R better than GPT-5.6 Luna?
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 31.4 on the Noometry Index. Command R costs 1.7× less per token, which makes it the better buy when GPT-5.6 Luna's lead doesn't matter for your workload.
Which is cheaper, Command R or GPT-5.6 Luna?
Command R is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GPT-5.6 Luna lists at $0.20 and $1.20.
Is Command R or GPT-5.6 Luna better for coding?
GPT-5.6 Luna scores higher on coding benchmarks: 54.5 versus 29.3 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Luna does, with 1.05M tokens against 128K.
How many benchmarks do Command R and GPT-5.6 Luna share?
19 benchmarks have published results for both models. Command R has 29 scored results on Noometry and GPT-5.6 Luna has 52.