Noometry

Model comparison

Command R vs GLM-4.6

GLM-4.6 is the stronger model overall, scoring 41.4 to 31.4 on the Noometry Index. Command R costs 3.8× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.

Last verified . 17 shared benchmarks.

Command R Cohere

31.4

Rank #272 Confirmed

GLM-4.6 Z.ai (Zhipu)

41.4

Rank #135 Confirmed

Summary

  • They share 17 benchmarks with published results for both. Command R scores higher in 0 categories and GLM-4.6 in 8 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where GLM-4.6 leads 61.1 to 38.2.
  • Command R is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
  • GLM-4.6 accepts more context: 205K tokens versus 128K.

Side by side

Command RGLM-4.6
ProviderCohereZ.ai (Zhipu)
Noometry Index31.441.4
Released2024-08-302025-09-30
WeightsOpenOpen
Context window128K205K
Max output4K131K
Input $ / M tokens$0.15$0.60
Output $ / M tokens$0.60$2.20
Results tracked2929

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Category by category

Coding GLM-4.6 leads

Command R: 29.3 (#306), GLM-4.6: 40.1 (#148)

BenchmarkCommand RGLM-4.6
LMArena Coding11691449
SWE-bench Verified (bash only)—55.4%
LMArena WebDev—1340
SciCode—38.4%
BigCodeBench Instruct37.1%—
LiveBench Coding17.9%—
BigCodeBench Complete45.2%—
ALE-Bench—340.82

Agentic & Tool Use Not comparable

Command R: —, GLM-4.6: 32.3 (#66)

BenchmarkCommand RGLM-4.6
Terminal-Bench—24.5%
Berkeley Function Calling Leaderboard—72.4%

Reasoning GLM-4.6 leads

Command R: 13.8 (#331), GLM-4.6: 23.7 (#172)

BenchmarkCommand RGLM-4.6
LMArena Hard Prompts11641440
Kagi LLM Benchmark—47.4%
CritPt—1.1%
LiveBench Reasoning21.9%—
DTBench46.4%—
LiveBench Data Analysis33.3%—
LMCA9.2%—
LiveBench27.5%—

Math GLM-4.6 leads

Command R: 28.0 (#246), GLM-4.6: 39.1 (#111)

BenchmarkCommand RGLM-4.6
LMArena Math11551432
LiveBench Math19.4%—
FrontierMath (Feb 2025 set)—3.8%
FrontierMath Tier 4 (v1)—2.1%

Knowledge GLM-4.6 leads

Command R: 31.0 (#221), GLM-4.6: 40.2 (#124)

BenchmarkCommand RGLM-4.6
LMArena Expert11381431
Vectara Hallucination Rate—9.5%
MMLU65.2%—

Multilingual GLM-4.6 leads

Command R: 35.7 (#245), GLM-4.6: 53.5 (#66)

BenchmarkCommand RGLM-4.6
LMArena Non-English11741426
LMArena Chinese11821499
LMArena French11621459
LMArena German11761447
LMArena Japanese11431393
LMArena Korean11631400
LMArena Russian11741419
LMArena Spanish11511436

Instruction Following GLM-4.6 leads

Command R: 58.1 (#261), GLM-4.6: 74.3 (#98)

BenchmarkCommand RGLM-4.6
LMArena Instruction Following11671410
LiveBench Instruction Following55.6%—

Long Context GLM-4.6 leads

Command R: 36.3 (#231), GLM-4.6: 43.4 (#94)

BenchmarkCommand RGLM-4.6
LMArena Longer Query11981422

Writing & Preference GLM-4.6 leads

Command R: 38.2 (#254), GLM-4.6: 61.1 (#90)

BenchmarkCommand RGLM-4.6
LMArena Text11871440
LMArena Creative Writing11701411
LMArena Multi-Turn11631427
EQ-Bench Creative Writing—1411
LiveBench Language16.7%—

Frequently asked questions

Is Command R better than GLM-4.6?

GLM-4.6 is the stronger model overall, scoring 41.4 to 31.4 on the Noometry Index. Command R costs 3.8× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.

Which is cheaper, Command R or GLM-4.6?

Command R is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GLM-4.6 lists at $0.60 and $2.20.

Is Command R or GLM-4.6 better for coding?

GLM-4.6 scores higher on coding benchmarks: 40.1 versus 29.3 in the Noometry coding category.

Which has the bigger context window?

GLM-4.6 does, with 205K tokens against 128K.

How many benchmarks do Command R and GLM-4.6 share?

17 benchmarks have published results for both models. Command R has 29 scored results on Noometry and GLM-4.6 has 29.

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