Model comparison

Command A vs GLM-4.5V

GLM-4.5V is the stronger model overall, scoring 39.8 to 36.5 on the Noometry Index.

Last verified October 10, 2026. 14 shared benchmarks.

Command A

Cohere

36.5

Rank #215 Confirmed

GLM-4.5V Z.ai (Zhipu)

39.8

Rank #158 Confirmed

Summary

They share 14 benchmarks with published results for both. Command A scores higher in 2 categories and GLM-4.5V in 6 categories; 4 gaps are clear of the uncertainty.
The widest gap is in coding, where GLM-4.5V leads 39.5 to 27.2.
The biggest single-benchmark swing is Kagi LLM Benchmark: 28.8% for Command A and 59.8% for GLM-4.5V.
GLM-4.5V is cheaper at $0.60 / $1.80 per million input/output tokens, against $2.50 / $10 for Command A.
Command A accepts more context: 256K tokens versus 64K.

Side by side

Command A and GLM-4.5V specifications
	Command A	GLM-4.5V
Provider	Cohere	Z.ai (Zhipu)
Noometry Index	36.5	39.8
Released	2025-03-13	2025-08-11
Weights	Open	Open
Context window	256K	64K
Max output	8K	16K
Input $ / M tokens	$2.50	$0.60
Output $ / M tokens	$10	$1.80
Results tracked	24	15

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Category by category

Coding GLM-4.5V leads

Command A: 27.2 (#322), GLM-4.5V: 39.5 (#155)

Coding benchmarks
Benchmark	Command A	GLM-4.5V
LMArena Coding	1330	1347
Aider Polyglot	12%	—

Agentic & Tool Use Not comparable

Command A: 35.9 (#40), GLM-4.5V: —

Agentic & Tool Use benchmarks
Benchmark	Command A	GLM-4.5V
Berkeley Function Calling Leaderboard	57.1%	—

Reasoning GLM-4.5V leads

Command A: 18.3 (#283), GLM-4.5V: 27.4 (#119)

Reasoning benchmarks
Benchmark	Command A	GLM-4.5V
Kagi LLM Benchmark	28.8%	59.8%
LMArena Hard Prompts	1326	1334
DTBench	61.3%	—
LMCA	10.3%	—

Math GLM-4.5V leads

Command A: 36.2 (#171), GLM-4.5V: 37.4 (#159)

Math benchmarks
Benchmark	Command A	GLM-4.5V
LMArena Math	1300	1354

Knowledge Too close to call

Command A: 37.1 (#159), GLM-4.5V: 37.5 (#156)

Knowledge benchmarks
Benchmark	Command A	GLM-4.5V
LMArena Expert	1295	1353
Vectara Hallucination Rate	9.3%	—

Multimodal Not comparable

Command A: —, GLM-4.5V: 34.3 (#92)

Multimodal benchmarks
Benchmark	Command A	GLM-4.5V
LMArena Vision	—	1154

Multilingual Too close to call

Command A: 45.3 (#170), GLM-4.5V: 44.6 (#177)

Multilingual benchmarks
Benchmark	Command A	GLM-4.5V
LMArena Non-English	1313	1303
LMArena Chinese	1327	1337
LMArena Russian	1314	1298
LMArena Spanish	1347	1336
LMArena French	1351	—
LMArena German	1341	—
LMArena Japanese	1285	—
LMArena Korean	1285	—

Instruction Following Too close to call

Command A: 69.1 (#177), GLM-4.5V: 69.2 (#175)

Instruction Following benchmarks
Benchmark	Command A	GLM-4.5V
LMArena Instruction Following	1309	1311

Long Context Too close to call

Command A: 40.6 (#151), GLM-4.5V: 39.6 (#171)

Long Context benchmarks
Benchmark	Command A	GLM-4.5V
LMArena Longer Query	1334	1304

Writing & Preference GLM-4.5V leads

Command A: 47.6 (#208), GLM-4.5V: 52.5 (#170)

Writing & Preference benchmarks
Benchmark	Command A	GLM-4.5V
LMArena Text	1331	1333
LMArena Creative Writing	1319	1295
LMArena Multi-Turn	1339	1332
EQ-Bench Creative Writing	1145	—

Frequently asked questions

Is Command A better than GLM-4.5V?

GLM-4.5V is the stronger model overall, scoring 39.8 to 36.5 on the Noometry Index.

Which is cheaper, Command A or GLM-4.5V?

GLM-4.5V is cheaper. It lists at $0.60 per million input tokens and $1.80 per million output tokens; Command A lists at $2.50 and $10.

Is Command A or GLM-4.5V better for coding?

GLM-4.5V scores higher on coding benchmarks: 39.5 versus 27.2 in the Noometry coding category.

Which has the bigger context window?

Command A does, with 256K tokens against 64K.

How many benchmarks do Command A and GLM-4.5V share?

14 benchmarks have published results for both models. Command A has 24 scored results on Noometry and GLM-4.5V has 15.