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Command R vs GLM 4.6

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 4.6.

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 4.6

Compared with GLM-4.5, this generation brings several key improvements: Longer context window: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex...

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Technical Specifications

SpecificationCommand RGLM 4.6
ProviderCohereZhipu AI
Context Window128,000 tokens202,752 tokens
Agent Suitability78/100N/A
Time to First Token (TTFT)200 msN/A
Deployment Modelmanaged apimanaged api
Production Stabilitystablestable
API AvailableYesYes
Released Date2024-03-112025-09-30

API Pricing Comparison

Input Price per Million Tokens

Command R

$0.15

GLM 4.6

$0.43

Output Price per Million Tokens

Command R

$0.60

GLM 4.6

$1.74

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%vsN/A
Command R
GLM 4.6
HumanEvalPython coding & logic synthesis
7300.0%vsN/A
Command R
GLM 4.6
MATHComplex mathematical problem solving
5400.0%vsN/A
Command R
GLM 4.6
GPQAGraduate-level expert reasoning
3500.0%vsN/A
Command R
GLM 4.6
HellaSwagCommonsense reasoning and inference
7800.0%vsN/A
Command R
GLM 4.6
MT-BenchMulti-turn conversation flow quality
750.0%vsN/A
Command R
GLM 4.6

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 4.6 Quirks & Gotchas

No developer gotchas reported.