Claude Fable Latest 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 Fable Latest and Command R.
Claude Fable Latest
This model always redirects to the latest model in the Claude Fable family.
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 Fable Latest | 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 | 2026-06-09 | 2024-03-11 |
API Pricing Comparison
Input Price per Million Tokens
Claude Fable Latest
$10.00
Command R
$0.15
Output Price per Million Tokens
Claude Fable Latest
$50.00
Command R
$0.60
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.
Claude Fable Latest 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