Command R vs GLM 4.7
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 4.7.
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.
GLM 4.7
GLM-4.7 is Z.ai’s latest flagship model, featuring upgrades in two key areas: enhanced programming capabilities and more stable multi-step reasoning/execution. It demonstrates significant improvements in executing complex agent tasks while...
Technical Specifications
| Specification | Command R | GLM 4.7 |
|---|---|---|
| Provider | Cohere | Zhipu AI |
| Context Window | 128,000 tokens | 202,752 tokens |
| Agent Suitability | 78/100 | N/A |
| Time to First Token (TTFT) | 200 ms | N/A |
| Deployment Model | managed api | managed api |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2024-03-11 | 2025-12-22 |
API Pricing Comparison
Input Price per Million Tokens
Command R
$0.15
GLM 4.7
$0.40
Output Price per Million Tokens
Command R
$0.60
GLM 4.7
$1.75
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Benchmark Performance Metrics
Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.
Command R Quirks & Gotchas
- ▸Cost-effective RAG model — strong multilingual search performance
- ▸Limited agentic capability — use Command R+ for complex multi-step tool use
GLM 4.7 Quirks & Gotchas
No developer gotchas reported.