Side-by-side comparison
Command R (08-2024) vs Llama 3.1 70B Instruct
Cohere
Command R (08-2024)
🔧 Tools
{} JSON
Input / 1M
$0.1500
Output / 1M
$0.6000
Meta
Llama 3.1 70B Instruct
🔧 Tools
{} JSON
Input / 1M
$0.4000
Output / 1M
$0.4000
| Command R (08-2024) | Llama 3.1 70B Instruct | |
|---|---|---|
| Provider | Cohere | Meta |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 128,000 | 131,072 |
| Capabilities Optional capabilities the model advertises: vision (images), tools (function calling), json_mode (structured output). | tools, json_mode | tools, json_mode |
| Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary → | 0.1500 | 0.4000 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.6000 | 0.4000 |
Frequently asked questions
Which is cheaper, Command R (08-2024) or Llama 3.1 70B Instruct?
Command R (08-2024) is cheaper than Llama 3.1 70B Instruct on a 50/50 input/output blend by about $0.025 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 R (08-2024) or Llama 3.1 70B Instruct?
Llama 3.1 70B Instruct has the larger context window at 131k tokens versus 128k tokens for Command R (08-2024). That means Llama 3.1 70B Instruct can ingest about 1.0x as much text per request.
What is the difference between Command R (08-2024) and Llama 3.1 70B Instruct?
Command R (08-2024) comes from Cohere; Llama 3.1 70B Instruct comes from Meta. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.