Command R vs GPT-3.5 Turbo
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 GPT-3.5 Turbo.
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.
GPT-3.5 Turbo
GPT-3.5 Turbo is OpenAI's fastest model. It can understand and generate natural language or code, and is optimized for chat and traditional completion tasks. Training data up to Sep 2021.
Technical Specifications
| Specification | Command R | GPT-3.5 Turbo |
|---|---|---|
| Provider | Cohere | OpenAI |
| Context Window | 128,000 tokens | 16,385 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 | 2023-05-28 |
API Pricing Comparison
Input Price per Million Tokens
Command R
$0.15
GPT-3.5 Turbo
$0.50
Output Price per Million Tokens
Command R
$0.60
GPT-3.5 Turbo
$1.50
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.
Command R Quirks & Gotchas
- โธCost-effective RAG model โ strong multilingual search performance
- โธLimited agentic capability โ use Command R+ for complex multi-step tool use
GPT-3.5 Turbo Quirks & Gotchas
No developer gotchas reported.