Side-by-side comparison
Command R7B (12-2024) vs Llama 3 8B Lunaris
Cohere
Command R7B (12-2024)
{} JSON
Input / 1M
$0.0375
Output / 1M
$0.1500
| Command R7B (12-2024) | Llama 3 8B Lunaris | |
|---|---|---|
| Provider | Cohere | Sao10K |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 128,000 | 8,192 |
| Capabilities Optional capabilities the model advertises: vision (images), tools (function calling), json_mode (structured output). | json_mode | json_mode |
| Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary → | 0.0375 | 0.0400 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.1500 | 0.0500 |
Frequently asked questions
Which is cheaper, Command R7B (12-2024) or Llama 3 8B Lunaris?
Llama 3 8B Lunaris is cheaper than Command R7B (12-2024) on a 50/50 input/output blend by about $0.0488 per 1M tokens. Exact savings depend on your input-vs-output ratio — use the cost calculator on this page for a workload-specific estimate.
Which has a larger context window, Command R7B (12-2024) or Llama 3 8B Lunaris?
Command R7B (12-2024) has the larger context window at 128k tokens versus 8k tokens for Llama 3 8B Lunaris. That means Command R7B (12-2024) can ingest about 15.6x as much text per request.
What is the difference between Command R7B (12-2024) and Llama 3 8B Lunaris?
Command R7B (12-2024) comes from Cohere; Llama 3 8B Lunaris comes from Sao10K. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.