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Command R vs GLM 5.2

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Command R and GLM 5.2.

Cohere

Command R

Command R is Cohere's lightweight, cost-efficient model engineered for high-speed enterprise integrations, productivity automation, and retrieval-augmented generation (RAG) pipelines. Optimized for low-latency API tool use and structured JSON output, it is particularly effective in enterprise search and question-answering systems where fast, reliable responses are critical. With a 128,000-token context window and a price of $0.15/MTok for input, Command R provides strong RAG performance and multilingual support at a fraction of the cost of Command R+, making it the preferred choice for teams scaling intelligent document retrieval at high request volumes.

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Zhipu AI

GLM 5.2

GLM 5.2 is a large-scale reasoning model from Z.ai. It supports text input and output with a 1M-token context window, and is suited for long-horizon agent workflows, project-level software engineering,...

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Technical Specifications

SpecificationCommand RGLM 5.2
ProviderCohereZhipu AI
Context Window128,000 tokens1,048,576 tokens
Agent Suitability78/100N/A
Time to First Token (TTFT)200 msN/A
Deployment Modelmanaged apimanaged api
Production Stabilitystablebeta
API AvailableYesYes
Released Date2024-03-112026-06-16

API Pricing Comparison

Input Price per Million Tokens

Command R

$0.15

GLM 5.2

$0.93

Output Price per Million Tokens

Command R

$0.60

GLM 5.2

$3.00

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
7100.0%vs8950.0%
Command R
GLM 5.2
HumanEvalPython coding & logic synthesis
7300.0%vs9120.0%
Command R
GLM 5.2
MATHComplex mathematical problem solving
5400.0%vs8050.0%
Command R
GLM 5.2
GPQAGraduate-level expert reasoning
3500.0%vs5350.0%
Command R
GLM 5.2
HellaSwagCommonsense reasoning and inference
7800.0%vs8980.0%
Command R
GLM 5.2
MT-BenchMulti-turn conversation flow quality
750.0%vs930.0%
Command R
GLM 5.2

Command R Quirks & Gotchas

  • โ–ธCost-effective RAG model โ€” strong multilingual search performance
  • โ–ธLimited agentic capability โ€” use Command R+ for complex multi-step tool use

GLM 5.2 Quirks & Gotchas

No developer gotchas reported.