Side-by-side comparison
Mistral Nemo vs Llama 3.1 8B Instruct
Meta
Llama 3.1 8B Instruct
🔧 Tools
{} JSON
Input / 1M
$0.0200
Output / 1M
$0.0500
| Mistral Nemo | Llama 3.1 8B Instruct | |
|---|---|---|
| Provider | Mistral | Meta |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 131,072 | 16,384 |
| 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 → | 0.0200 | 0.0200 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.0300 | 0.0500 |
Frequently asked questions
Which is cheaper, Mistral Nemo or Llama 3.1 8B Instruct?
Mistral Nemo is cheaper than Llama 3.1 8B Instruct on a 50/50 input/output blend by about $0.01 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, Mistral Nemo or Llama 3.1 8B Instruct?
Mistral Nemo has the larger context window at 131k tokens versus 16k tokens for Llama 3.1 8B Instruct. That means Mistral Nemo can ingest about 8.0x as much text per request.
What is the difference between Mistral Nemo and Llama 3.1 8B Instruct?
Mistral Nemo comes from Mistral; Llama 3.1 8B 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.