Noometry

Model comparison

Command R7B vs GPT-5.6 Luna

GPT-5.6 Luna has enough public results to be ranked (#30); Command R7B does not yet, so treat this comparison as directional.

Last verified . 0 shared benchmarks.

Command R7B Cohere

32.5

Unranked Sparse

GPT-5.6 Luna OpenAI

54.6

Rank #30 Confirmed

Summary

  • The widest gap is in agentic & tool use, where GPT-5.6 Luna leads 34.4 to 26.1.
  • Command R7B is cheaper at $0.0375 / $0.15 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 R7B has downloadable open weights; the other is API-only.

Side by side

Command R7BGPT-5.6 Luna
ProviderCohereOpenAI
Noometry Index32.554.6
Released2024-12-022026-07-09
WeightsOpenProprietary
Context window128K1.05M
Max output4K128K
Input $ / M tokens$0.0375$0.20
Output $ / M tokens$0.15$1.20
Results tracked152

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Category by category

Coding Not comparable

Command R7B: —, GPT-5.6 Luna: 54.5 (#28)

BenchmarkCommand R7BGPT-5.6 Luna
DeepSWE—67.2%
FrontierCode—39.8%
CursorBench—35.9%
LMArena WebDev—1519
SciCode—53.6%
WeirdML—60.9%
LMArena Coding—1466
ALE-Bench—1,667

Agentic & Tool Use GPT-5.6 Luna leads

Command R7B: 26.1, GPT-5.6 Luna: 34.4 (#45)

BenchmarkCommand R7BGPT-5.6 Luna
APEX-Agents—43%
Berkeley Function Calling Leaderboard32.1%—
BALROG—45.6%
GDP.pdf—22.7%
Vending-Bench 2—4,095

Reasoning Not comparable

Command R7B: —, GPT-5.6 Luna: 47.6 (#43)

BenchmarkCommand R7BGPT-5.6 Luna
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%
LMArena Hard Prompts—1451
Mystery Game Puzzles—21%
DTBench—89.1%
LMCA—48.5%
Surface Evolver Bench—61.9%
Epoch Capabilities Index—156.39

Math Not comparable

Command R7B: —, GPT-5.6 Luna: 77.7 (#14)

BenchmarkCommand R7BGPT-5.6 Luna
FrontierMath (Tiers 1-3)—82.1%
FrontierMath Tier 4—61%
OTIS Mock AIME 2024-2025—98.3%
ProofBench—60%
LMArena Math—1458

Knowledge Not comparable

Command R7B: —, GPT-5.6 Luna: 58.5 (#34)

BenchmarkCommand R7BGPT-5.6 Luna
GPQA Diamond—91.6%
SimpleQA Verified—41%
LMArena Expert—1478

Multimodal Not comparable

Command R7B: —, GPT-5.6 Luna: 42.7 (#28)

BenchmarkCommand R7BGPT-5.6 Luna
LMArena Vision—1258
Blueprint-Bench 2—22.6%
Furniture Assembly—42.5%
LMArena Document—1457

Multilingual Not comparable

Command R7B: —, GPT-5.6 Luna: 52.8 (#78)

BenchmarkCommand R7BGPT-5.6 Luna
LMArena Non-English—1417
LMArena Chinese—1470
LMArena French—1456
LMArena German—1454
LMArena Japanese—1411
LMArena Korean—1415
LMArena Russian—1428
LMArena Spanish—1448

Instruction Following Not comparable

Command R7B: —, GPT-5.6 Luna: 75.6 (#57)

BenchmarkCommand R7BGPT-5.6 Luna
LMArena Instruction Following—1437

Long Context Not comparable

Command R7B: —, GPT-5.6 Luna: 43.9 (#82)

BenchmarkCommand R7BGPT-5.6 Luna
LMArena Longer Query—1436

Writing & Preference Not comparable

Command R7B: —, GPT-5.6 Luna: 68.0 (#29)

BenchmarkCommand R7BGPT-5.6 Luna
LMArena Text—1431
LMArena Creative Writing—1396
EQ-Bench Creative Writing—1829
EQ-Bench 4—1156
LMArena Multi-Turn—1434

Frequently asked questions

Is Command R7B better than GPT-5.6 Luna?

GPT-5.6 Luna has enough public results to be ranked (#30); Command R7B does not yet, so treat this comparison as directional.

Which is cheaper, Command R7B or GPT-5.6 Luna?

Command R7B is cheaper. It lists at $0.0375 per million input tokens and $0.15 per million output tokens; GPT-5.6 Luna lists at $0.20 and $1.20.

Which has the bigger context window?

GPT-5.6 Luna does, with 1.05M tokens against 128K.

How many benchmarks do Command R7B and GPT-5.6 Luna share?

0 benchmarks have published results for both models. Command R7B has 1 scored results on Noometry and GPT-5.6 Luna has 52.

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