Side-by-side comparison
Gemma 3 27B vs Llama 4 Scout
Google
Gemma 3 27B
👁 Vision
🔧 Tools
{} JSON
Input / 1M
$0.0800
Output / 1M
$0.1600
Meta
Llama 4 Scout
👁 Vision
🔧 Tools
{} JSON
Input / 1M
$0.0800
Output / 1M
$0.3000
| Gemma 3 27B | Llama 4 Scout | |
|---|---|---|
| Provider | Meta | |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 131,072 | 327,680 |
| Capabilities Optional capabilities the model advertises: vision (images), tools (function calling), json_mode (structured output). | vision, tools, json_mode | vision, tools, json_mode |
| Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary → | 0.0800 | 0.0800 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.1600 | 0.3000 |
Frequently asked questions
Which is cheaper, Gemma 3 27B or Llama 4 Scout?
Gemma 3 27B is cheaper than Llama 4 Scout on a 50/50 input/output blend by about $0.07 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, Gemma 3 27B or Llama 4 Scout?
Llama 4 Scout has the larger context window at 328k tokens versus 131k tokens for Gemma 3 27B. That means Llama 4 Scout can ingest about 2.5x as much text per request.
What is the difference between Gemma 3 27B and Llama 4 Scout?
Gemma 3 27B comes from Google; Llama 4 Scout 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.