Noometry

Model comparison

Command A vs GLM-4.7

GLM-4.7 is the stronger model overall, scoring 42.0 to 36.5 on the Noometry Index.

Last verified . 19 shared benchmarks.

Command A Cohere

36.5

Rank #215 Confirmed

GLM-4.7 Z.ai (Zhipu)

42.0

Rank #124 Confirmed

Summary

  • They share 19 benchmarks with published results for both. Command A scores higher in 1 category and GLM-4.7 in 8 categories; 9 gaps are clear of the uncertainty.
  • The widest gap is in coding, where GLM-4.7 leads 44.0 to 27.2.
  • GLM-4.7 is cheaper at $0.60 / $2.20 per million input/output tokens, against $2.50 / $10 for Command A.
  • Command A accepts more context: 256K tokens versus 205K.

Side by side

Command AGLM-4.7
ProviderCohereZ.ai (Zhipu)
Noometry Index36.542.0
Released2025-03-132025-12-22
WeightsOpenOpen
Context window256K205K
Max output8K131K
Input $ / M tokens$2.50$0.60
Output $ / M tokens$10$2.20
Results tracked2436

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Category by category

Coding GLM-4.7 leads

Command A: 27.2 (#322), GLM-4.7: 44.0 (#79)

BenchmarkCommand AGLM-4.7
LMArena Coding13301454
Aider Polyglot12%—
LMArena WebDev—1435
SciCode—45.1%
ALE-Bench—399.48

Agentic & Tool Use Command A leads

Command A: 35.9 (#40), GLM-4.7: 26.5 (#103)

BenchmarkCommand AGLM-4.7
Terminal-Bench—33.4%
Berkeley Function Calling Leaderboard57.1%—
Vending-Bench 2—2,377

Reasoning GLM-4.7 leads

Command A: 18.3 (#283), GLM-4.7: 24.3 (#164)

BenchmarkCommand AGLM-4.7
LMArena Hard Prompts13261443
SimpleBench—47.7%
Kagi LLM Benchmark28.8%—
CritPt—1.7%
Chess Puzzles—6%
DTBench61.3%—
LMCA10.3%—
Epoch Capabilities Index—143.51

Math GLM-4.7 leads

Command A: 36.2 (#171), GLM-4.7: 38.6 (#135)

BenchmarkCommand AGLM-4.7
LMArena Math13001423
OTIS Mock AIME 2024-2025—83.3%
ProofBench—6%
FrontierMath (Feb 2025 set)—2.4%
FrontierMath Tier 4 (v1)—0%

Knowledge GLM-4.7 leads

Command A: 37.1 (#159), GLM-4.7: 47.0 (#80)

BenchmarkCommand AGLM-4.7
Vectara Hallucination Rate9.3%11.7%
LMArena Expert12951424
GPQA Diamond—83.3%
SimpleQA Verified—32.2%

Multilingual GLM-4.7 leads

Command A: 45.3 (#170), GLM-4.7: 52.8 (#79)

BenchmarkCommand AGLM-4.7
LMArena Non-English13131417
LMArena Chinese13271495
LMArena French13511432
LMArena German13411424
LMArena Japanese12851439
LMArena Korean12851399
LMArena Russian13141423
LMArena Spanish13471434

Instruction Following GLM-4.7 leads

Command A: 69.1 (#177), GLM-4.7: 74.4 (#95)

BenchmarkCommand AGLM-4.7
LMArena Instruction Following13091411

Long Context GLM-4.7 leads

Command A: 40.6 (#151), GLM-4.7: 42.8 (#116)

BenchmarkCommand AGLM-4.7
LMArena Longer Query13341432
CL-bench—15.9%
CL-bench Life—10.9%

Writing & Preference GLM-4.7 leads

Command A: 47.6 (#208), GLM-4.7: 60.9 (#93)

BenchmarkCommand AGLM-4.7
LMArena Text13311435
LMArena Creative Writing13191401
EQ-Bench Creative Writing11451413
LMArena Multi-Turn13391446

Frequently asked questions

Is Command A better than GLM-4.7?

GLM-4.7 is the stronger model overall, scoring 42.0 to 36.5 on the Noometry Index.

Which is cheaper, Command A or GLM-4.7?

GLM-4.7 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Command A lists at $2.50 and $10.

Is Command A or GLM-4.7 better for coding?

GLM-4.7 scores higher on coding benchmarks: 44.0 versus 27.2 in the Noometry coding category.

Which has the bigger context window?

Command A does, with 256K tokens against 205K.

How many benchmarks do Command A and GLM-4.7 share?

19 benchmarks have published results for both models. Command A has 24 scored results on Noometry and GLM-4.7 has 36.

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