Side-by-side comparison
Llama 3.2 11B Vision Instruct vs Gemma 4 31B
Meta
Llama 3.2 11B Vision Instruct
👁 Vision
{} JSON
Input / 1M
$0.2450
Output / 1M
$0.2450
Google
Gemma 4 31B
👁 Vision
🔧 Tools
{} JSON
Input / 1M
$0.1200
Output / 1M
$0.3700
| Llama 3.2 11B Vision Instruct | Gemma 4 31B | |
|---|---|---|
| Provider | Meta | |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 131,072 | 262,144 |
| Capabilities Optional capabilities the model advertises: vision (images), tools (function calling), json_mode (structured output). | vision, json_mode | vision, tools, json_mode |
| Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary → | 0.2450 | 0.1200 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.2450 | 0.3700 |
Frequently asked questions
Which is cheaper, Llama 3.2 11B Vision Instruct or Gemma 4 31B?
Llama 3.2 11B Vision Instruct is cheaper than Gemma 4 31B on a 50/50 input/output blend by about $0 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 11B Vision Instruct or Gemma 4 31B?
Gemma 4 31B has the larger context window at 262k tokens versus 131k tokens for Llama 3.2 11B Vision Instruct. That means Gemma 4 31B can ingest about 2.0x as much text per request.
What is the difference between Llama 3.2 11B Vision Instruct and Gemma 4 31B?
Llama 3.2 11B Vision Instruct comes from Meta; Gemma 4 31B comes from Google. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.