Side-by-side comparison
Qwen3.5-9B vs Gemma 3 27B
Google
Gemma 3 27B
👁 Vision
🔧 Tools
{} JSON
Input / 1M
$0.0800
Output / 1M
$0.1600
| Qwen3.5-9B | Gemma 3 27B | |
|---|---|---|
| Provider | Qwen | |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 262,144 | 131,072 |
| 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 → | 0.0400 | 0.0800 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.1500 | 0.1600 |
Frequently asked questions
Which is cheaper, Qwen3.5-9B or Gemma 3 27B?
Qwen3.5-9B is cheaper than Gemma 3 27B on a 50/50 input/output blend by about $0.025 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, Qwen3.5-9B or Gemma 3 27B?
Qwen3.5-9B has the larger context window at 262k tokens versus 131k tokens for Gemma 3 27B. That means Qwen3.5-9B can ingest about 2.0x as much text per request.
What is the difference between Qwen3.5-9B and Gemma 3 27B?
Qwen3.5-9B comes from Qwen; Gemma 3 27B 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.