Side-by-side comparison
o1-pro vs Llama 3.2 11B Vision Instruct
Meta
Llama 3.2 11B Vision Instruct
👁 Vision
{} JSON
Input / 1M
$0.2450
Output / 1M
$0.2450
| o1-pro | Llama 3.2 11B Vision Instruct | |
|---|---|---|
| Provider | OpenAI | Meta |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 200,000 | 131,072 |
| Capabilities Optional capabilities the model advertises: vision (images), tools (function calling), json_mode (structured output). | vision, json_mode | vision, json_mode |
| Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary → | 150.0000 | 0.2450 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 600.0000 | 0.2450 |
Frequently asked questions
Which is cheaper, o1-pro or Llama 3.2 11B Vision Instruct?
Llama 3.2 11B Vision Instruct is cheaper than o1-pro on a 50/50 input/output blend by about $374.755 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, o1-pro or Llama 3.2 11B Vision Instruct?
o1-pro has the larger context window at 200k tokens versus 131k tokens for Llama 3.2 11B Vision Instruct. That means o1-pro can ingest about 1.5x as much text per request.
What is the difference between o1-pro and Llama 3.2 11B Vision Instruct?
o1-pro comes from OpenAI; Llama 3.2 11B Vision 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.