Noometry

Model comparison

Command R vs GLM-4.7-Flash

GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 31.4 on the Noometry Index.

Last verified . 16 shared benchmarks.

Command R Cohere

31.4

Rank #272 Confirmed

GLM-4.7-Flash Z.ai (Zhipu)

38.8

Rank #180 Confirmed

Summary

  • They share 16 benchmarks with published results for both. Command R scores higher in 0 categories and GLM-4.7-Flash in 8 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in instruction following, where GLM-4.7-Flash leads 70.1 to 58.1.
  • GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.15 / $0.60 for Command R.
  • GLM-4.7-Flash accepts more context: 200K tokens versus 128K.

Side by side

Command RGLM-4.7-Flash
ProviderCohereZ.ai (Zhipu)
Noometry Index31.438.8
Released2024-08-302026-01-19
WeightsOpenOpen
Context window128K200K
Max output4K131K
Input $ / M tokens$0.15$0.06
Output $ / M tokens$0.60$0.40
Results tracked2921

Sponsored placements are available on pages like this one. Advertise on Noometry

Category by category

Coding GLM-4.7-Flash leads

Command R: 29.3 (#306), GLM-4.7-Flash: 40.6 (#135)

BenchmarkCommand RGLM-4.7-Flash
LMArena Coding11691383
BigCodeBench Instruct37.1%—
LiveBench Coding17.9%—
BigCodeBench Complete45.2%—

Reasoning GLM-4.7-Flash leads

Command R: 13.8 (#331), GLM-4.7-Flash: 20.9 (#229)

BenchmarkCommand RGLM-4.7-Flash
LMArena Hard Prompts11641356
Chess Puzzles—0%
LiveBench Reasoning21.9%—
DTBench46.4%—
LiveBench Data Analysis33.3%—
LMCA9.2%—
LiveBench27.5%—

Math GLM-4.7-Flash leads

Command R: 28.0 (#246), GLM-4.7-Flash: 36.1 (#173)

BenchmarkCommand RGLM-4.7-Flash
LMArena Math11551355
OTIS Mock AIME 2024-2025—58.3%
LiveBench Math19.4%—

Knowledge GLM-4.7-Flash leads

Command R: 31.0 (#221), GLM-4.7-Flash: 35.5 (#184)

BenchmarkCommand RGLM-4.7-Flash
LMArena Expert11381357
GPQA Diamond—60.5%
Vectara Hallucination Rate—9.3%
MMLU65.2%—

Multilingual GLM-4.7-Flash leads

Command R: 35.7 (#245), GLM-4.7-Flash: 46.5 (#158)

BenchmarkCommand RGLM-4.7-Flash
LMArena Non-English11741330
LMArena Chinese11821403
LMArena French11621332
LMArena German11761337
LMArena Korean11631283
LMArena Russian11741332
LMArena Spanish11511350
LMArena Japanese1143—

Instruction Following GLM-4.7-Flash leads

Command R: 58.1 (#261), GLM-4.7-Flash: 70.1 (#167)

BenchmarkCommand RGLM-4.7-Flash
LMArena Instruction Following11671327
LiveBench Instruction Following55.6%—

Long Context GLM-4.7-Flash leads

Command R: 36.3 (#231), GLM-4.7-Flash: 40.9 (#148)

BenchmarkCommand RGLM-4.7-Flash
LMArena Longer Query11981345

Writing & Preference GLM-4.7-Flash leads

Command R: 38.2 (#254), GLM-4.7-Flash: 47.4 (#210)

BenchmarkCommand RGLM-4.7-Flash
LMArena Text11871351
LMArena Creative Writing11701297
LMArena Multi-Turn11631342
EQ-Bench Creative Writing—1125
LiveBench Language16.7%—

Frequently asked questions

Is Command R better than GLM-4.7-Flash?

GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 31.4 on the Noometry Index.

Which is cheaper, Command R or GLM-4.7-Flash?

GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; Command R lists at $0.15 and $0.60.

Is Command R or GLM-4.7-Flash better for coding?

GLM-4.7-Flash scores higher on coding benchmarks: 40.6 versus 29.3 in the Noometry coding category.

Which has the bigger context window?

GLM-4.7-Flash does, with 200K tokens against 128K.

How many benchmarks do Command R and GLM-4.7-Flash share?

16 benchmarks have published results for both models. Command R has 29 scored results on Noometry and GLM-4.7-Flash has 21.

Related comparisons

Go deeper