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+
Cohere's enterprise-optimized model built for advanced Retrieval-Augmented Generation (RAG) and multi-step tool use. Highly effective for multilingual business processes.
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 | 86/100 | 90/100 |
| Time to First Token (TTFT) | 350 ms | 550 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2024-04-04 | 2024-07-23 |
API Pricing Comparison
Input Price per Million Tokens
Command R+
$2.50
Llama 3.1 405B
$0.80
Output Price per Million Tokens
Command R+
$10.00
Llama 3.1 405B
$0.80
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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.
Command R+ Quirks & Gotchas
- ▸Optimized for RAG workflows — best enterprise document search model
- ▸Tool calling requires explicit step definitions in Cohere's tool-use format
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