Claude Sonnet 4 vs Command R
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 Claude Sonnet 4 and Command R.
Claude Sonnet 4
Claude Sonnet 4 significantly enhances the capabilities of its predecessor, Sonnet 3.7, excelling in both coding and reasoning tasks with improved precision and controllability. Achieving state-of-the-art performance on SWE-bench (72.7%),...
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.
Technical Specifications
| Specification | Claude Sonnet 4 | Command R |
|---|---|---|
| Provider | Anthropic | Cohere |
| Context Window | 1,000,000 tokens | 128,000 tokens |
| Agent Suitability | N/A | 78/100 |
| Time to First Token (TTFT) | N/A | 200 ms |
| Deployment Model | managed api | managed api |
| Production Stability | beta | stable |
| API Available | Yes | Yes |
| Released Date | 2025-05-22 | 2024-03-11 |
API Pricing Comparison
Input Price per Million Tokens
Claude Sonnet 4
$3.00
Command R
$0.15
Output Price per Million Tokens
Claude Sonnet 4
$15.00
Command R
$0.60
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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.
Claude Sonnet 4 Quirks & Gotchas
No developer gotchas reported.
Command R Quirks & Gotchas
- ▸Cost-effective RAG model — strong multilingual search performance
- ▸Limited agentic capability — use Command R+ for complex multi-step tool use