Codestral 2508 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 Codestral 2508 and Command R.
Codestral 2508
Mistral's cutting-edge language model for coding released end of July 2025. Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code correction and test generation. [Blog Post](https://mistral.ai/news/codestral-25-08)
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 | Codestral 2508 | Command R |
|---|---|---|
| Provider | Mistral | Cohere |
| Context Window | 256,000 tokens | 128,000 tokens |
| Agent Suitability | N/A | 78/100 |
| Time to First Token (TTFT) | N/A | 200 ms |
| Deployment Model | self hostable | managed api |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2025-08-01 | 2024-03-11 |
API Pricing Comparison
Input Price per Million Tokens
Codestral 2508
$0.30
Command R
$0.15
Output Price per Million Tokens
Codestral 2508
$0.90
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.
Codestral 2508 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