Model comparison

Command R7B vs GLM-5.2

GLM-5.2 has enough public results to be ranked (#44); Command R7B does not yet, so treat this comparison as directional.

Last verified . 0 shared benchmarks.

Command R7B Cohere

32.5

Unranked Sparse

GLM-5.2 Z.ai (Zhipu)

51.1

Rank #44 Confirmed

Summary

  • The widest gap is in agentic & tool use, where GLM-5.2 leads 32.4 to 26.1.
  • Command R7B is cheaper at $0.0375 / $0.15 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
  • GLM-5.2 accepts more context: 1M tokens versus 128K.

Side by side

Command R7B and GLM-5.2 specifications
Command R7BGLM-5.2
ProviderCohereZ.ai (Zhipu)
Noometry Index32.551.1
Released2024-12-022026-06-13
WeightsOpenOpen
Context window128K1M
Max output4K131K
Input $ / M tokens$0.0375$1.40
Output $ / M tokens$0.15$4.40
Results tracked151

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Category by category

Coding Not comparable

Command R7B: —, GLM-5.2: 51.3 (#41)

Coding benchmarks
BenchmarkCommand R7BGLM-5.2
SWE-bench Verified—78.7%
DeepSWE—43.8%
FrontierCode—24.5%
LMArena WebDev—1603
SciCode—50.5%
WeirdML—70.1%
LMArena Coding—1485
ALE-Bench—1,047

Agentic & Tool Use GLM-5.2 leads

Command R7B: 26.1, GLM-5.2: 32.4 (#63)

Agentic & Tool Use benchmarks
BenchmarkCommand R7BGLM-5.2
APEX-Agents—45.2%
Berkeley Function Calling Leaderboard32.1%—
τ²-bench Banking—37.1%
PostTrainBench—31.7%
GBAEval—0%
Vending-Bench 2—8,314

Reasoning Not comparable

Command R7B: —, GLM-5.2: 42.3 (#52)

Reasoning benchmarks
BenchmarkCommand R7BGLM-5.2
ARC-AGI-2—22.8%
SimpleBench—58.8%
Kagi LLM Benchmark—62.6%
NYT Connections (extended)—74.3%
ARC-AGI-1—77%
CritPt—20.9%
Chess Puzzles—21%
EBR-Bench—9.5%
LMArena Hard Prompts—1480
Mystery Game Puzzles—19%
DTBench—93.6%
LMCA—45.8%
Surface Evolver Bench—55.6%
Epoch Capabilities Index—151.78

Math Not comparable

Command R7B: —, GLM-5.2: 55.7 (#43)

Math benchmarks
BenchmarkCommand R7BGLM-5.2
FrontierMath (Tiers 1-3)—59.2%
FrontierMath Tier 4—29.3%
MathArena Final-Answer Competitions—67.6%
OTIS Mock AIME 2024-2025—86.4%
ProofBench—35%
LMArena Math—1482

Knowledge Not comparable

Command R7B: —, GLM-5.2: 57.1 (#40)

Knowledge benchmarks
BenchmarkCommand R7BGLM-5.2
GPQA Diamond—91.9%
SimpleQA Verified—34.2%
LMArena Expert—1486

Multilingual Not comparable

Command R7B: —, GLM-5.2: 55.8 (#26)

Multilingual benchmarks
BenchmarkCommand R7BGLM-5.2
LMArena Non-English—1459
LMArena Chinese—1519
LMArena French—1479
LMArena German—1468
LMArena Japanese—1451
LMArena Korean—1445
LMArena Russian—1466
LMArena Spanish—1477

Instruction Following Not comparable

Command R7B: —, GLM-5.2: 76.9 (#34)

Instruction Following benchmarks
BenchmarkCommand R7BGLM-5.2
LMArena Instruction Following—1465

Long Context Not comparable

Command R7B: —, GLM-5.2: 45.3 (#43)

Long Context benchmarks
BenchmarkCommand R7BGLM-5.2
LMArena Longer Query—1479

Writing & Preference Not comparable

Command R7B: —, GLM-5.2: 70.4 (#21)

Writing & Preference benchmarks
BenchmarkCommand R7BGLM-5.2
LMArena Text—1470
LMArena Creative Writing—1462
EQ-Bench Creative Writing—1757
EQ-Bench 4—1222
LMArena Multi-Turn—1469

Frequently asked questions

Is Command R7B better than GLM-5.2?

GLM-5.2 has enough public results to be ranked (#44); Command R7B does not yet, so treat this comparison as directional.

Which is cheaper, Command R7B or GLM-5.2?

Command R7B is cheaper. It lists at $0.0375 per million input tokens and $0.15 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.

Which has the bigger context window?

GLM-5.2 does, with 1M tokens against 128K.

How many benchmarks do Command R7B and GLM-5.2 share?

0 benchmarks have published results for both models. Command R7B has 1 scored results on Noometry and GLM-5.2 has 51.

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