Side-by-side comparison
Llama 3.2 1B Instruct vs Mistral 7B Instruct v0.1
Mistral
Mistral 7B Instruct v0.1
Input / 1M
$0.1100
Output / 1M
$0.1900
| Llama 3.2 1B Instruct | Mistral 7B Instruct v0.1 | |
|---|---|---|
| Provider | Meta | Mistral |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 60,000 | 2,824 |
| Capabilities Optional capabilities the model advertises: vision (images), tools (function calling), json_mode (structured output). | text-only | text-only |
| Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary → | 0.0270 | 0.1100 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.2000 | 0.1900 |
Frequently asked questions
Which is cheaper, Llama 3.2 1B Instruct or Mistral 7B Instruct v0.1?
Llama 3.2 1B Instruct is cheaper than Mistral 7B Instruct v0.1 on a 50/50 input/output blend by about $0.0365 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, Llama 3.2 1B Instruct or Mistral 7B Instruct v0.1?
Llama 3.2 1B Instruct has the larger context window at 60k tokens versus 3k tokens for Mistral 7B Instruct v0.1. That means Llama 3.2 1B Instruct can ingest about 21.2x as much text per request.
What is the difference between Llama 3.2 1B Instruct and Mistral 7B Instruct v0.1?
Llama 3.2 1B Instruct comes from Meta; Mistral 7B Instruct v0.1 comes from Mistral. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.