Model comparison

Command A vs GLM-4.6V

GLM-4.6V is the stronger model overall, scoring 41.3 to 36.5 on the Noometry Index.

Last verified . 11 shared benchmarks.

Command A Cohere

36.5

Rank #215 Confirmed

GLM-4.6V Z.ai (Zhipu)

41.3

Rank #137 Confirmed

Summary

  • They share 11 benchmarks with published results for both. Command A scores higher in 0 categories and GLM-4.6V in 7 categories; 5 gaps are clear of the uncertainty.
  • The widest gap is in coding, where GLM-4.6V leads 40.9 to 27.2.
  • GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $2.50 / $10 for Command A.
  • Command A accepts more context: 256K tokens versus 128K.

Side by side

Command A and GLM-4.6V specifications
Command AGLM-4.6V
ProviderCohereZ.ai (Zhipu)
Noometry Index36.541.3
Released2025-03-132025-12-08
WeightsOpenOpen
Context window256K128K
Max output8K33K
Input $ / M tokens$2.50$0.30
Output $ / M tokens$10$0.90
Results tracked2412

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Category by category

Coding GLM-4.6V leads

Command A: 27.2 (#322), GLM-4.6V: 40.9 (#128)

Coding benchmarks
BenchmarkCommand AGLM-4.6V
LMArena Coding13301390
Aider Polyglot12%—

Agentic & Tool Use Not comparable

Command A: 35.9 (#40), GLM-4.6V: —

Agentic & Tool Use benchmarks
BenchmarkCommand AGLM-4.6V
Berkeley Function Calling Leaderboard57.1%—

Reasoning GLM-4.6V leads

Command A: 18.3 (#283), GLM-4.6V: 27.6 (#115)

Reasoning benchmarks
BenchmarkCommand AGLM-4.6V
LMArena Hard Prompts13261368
Kagi LLM Benchmark28.8%—
DTBench61.3%—
LMCA10.3%—

Math Not comparable

Command A: 36.2 (#171), GLM-4.6V: —

Math benchmarks
BenchmarkCommand AGLM-4.6V
LMArena Math1300—

Knowledge Too close to call

Command A: 37.1 (#159), GLM-4.6V: 38.0 (#149)

Knowledge benchmarks
BenchmarkCommand AGLM-4.6V
LMArena Expert12951371
Vectara Hallucination Rate9.3%—

Multimodal Not comparable

Command A: —, GLM-4.6V: 34.8 (#90)

Multimodal benchmarks
BenchmarkCommand AGLM-4.6V
LMArena Vision—1164

Multilingual GLM-4.6V leads

Command A: 45.3 (#170), GLM-4.6V: 48.6 (#141)

Multilingual benchmarks
BenchmarkCommand AGLM-4.6V
LMArena Non-English13131359
LMArena Chinese13271425
LMArena Russian13141340
LMArena French1351—
LMArena German1341—
LMArena Japanese1285—
LMArena Korean1285—
LMArena Spanish1347—

Instruction Following GLM-4.6V leads

Command A: 69.1 (#177), GLM-4.6V: 71.4 (#151)

Instruction Following benchmarks
BenchmarkCommand AGLM-4.6V
LMArena Instruction Following13091352

Long Context Too close to call

Command A: 40.6 (#151), GLM-4.6V: 41.3 (#143)

Long Context benchmarks
BenchmarkCommand AGLM-4.6V
LMArena Longer Query13341358

Writing & Preference GLM-4.6V leads

Command A: 47.6 (#208), GLM-4.6V: 56.6 (#137)

Writing & Preference benchmarks
BenchmarkCommand AGLM-4.6V
LMArena Text13311377
LMArena Creative Writing13191347
LMArena Multi-Turn13391360
EQ-Bench Creative Writing1145—

Frequently asked questions

Is Command A better than GLM-4.6V?

GLM-4.6V is the stronger model overall, scoring 41.3 to 36.5 on the Noometry Index.

Which is cheaper, Command A or GLM-4.6V?

GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Command A lists at $2.50 and $10.

Is Command A or GLM-4.6V better for coding?

GLM-4.6V scores higher on coding benchmarks: 40.9 versus 27.2 in the Noometry coding category.

Which has the bigger context window?

Command A does, with 256K tokens against 128K.

How many benchmarks do Command A and GLM-4.6V share?

11 benchmarks have published results for both models. Command A has 24 scored results on Noometry and GLM-4.6V has 12.

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