Side-by-side comparison
Llama 3.2 1B Instruct vs Gemma 3n 4B
| Llama 3.2 1B Instruct | Gemma 3n 4B | |
|---|---|---|
| Provider | Meta | |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 60,000 | 32,768 |
| 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 | 0.0600 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.2000 | 0.1200 |
Frequently asked questions
Which is cheaper, Llama 3.2 1B Instruct or Gemma 3n 4B?
Gemma 3n 4B is cheaper than Llama 3.2 1B Instruct on a 50/50 input/output blend by about $0.0235 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 Gemma 3n 4B?
Llama 3.2 1B Instruct has the larger context window at 60k tokens versus 33k tokens for Gemma 3n 4B. That means Llama 3.2 1B Instruct can ingest about 1.8x as much text per request.
What is the difference between Llama 3.2 1B Instruct and Gemma 3n 4B?
Llama 3.2 1B Instruct comes from Meta; Gemma 3n 4B 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.