Command R vs Llama 3.1 405B
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 Llama 3.1 405B.
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.
Llama 3.1 405B
Llama 3.1 405B is Meta's largest open-weight language model and one of the most capable openly available models in the world. With 405 billion parameters, it achieves performance competitive with GPT-4 and Claude Opus across benchmarks spanning general knowledge, mathematics, coding, and multilingual tasks. Llama 3.1 405B is released under Meta's custom commercial license, supporting broad use cases including deployment via major cloud providers (AWS, GCP, Azure) and self-hosted inference with multi-GPU configurations.
Technical Specifications
| Specification | Command R | Llama 3.1 405B |
|---|---|---|
| Provider | Cohere | Meta |
| Context Window | 128,000 tokens | 131,072 tokens |
| Agent Suitability | 78/100 | 90/100 |
| Time to First Token (TTFT) | 200 ms | 550 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2024-03-11 | 2024-07-23 |
API Pricing Comparison
Input Price per Million Tokens
Command R
$0.15
Llama 3.1 405B
$0.80
Output Price per Million Tokens
Command R
$0.60
Llama 3.1 405B
$0.80
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
- ▸Cost-effective RAG model — strong multilingual search performance
- ▸Limited agentic capability — use Command R+ for complex multi-step tool use
Llama 3.1 405B Quirks & Gotchas
- ▸Massive model — requires 8× A100 80GB for FP16 inference
- ▸Available via Together AI, Fireworks, and Bedrock as managed API