Side-by-side comparison
Qwen3 VL 32B Instruct vs GPT-5 Nano
Qwen
Qwen3 VL 32B Instruct
👁 Vision
🔧 Tools
{} JSON
Input / 1M
$0.1040
Output / 1M
$0.4160
| Qwen3 VL 32B Instruct | GPT-5 Nano | |
|---|---|---|
| Provider | Qwen | OpenAI |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 131,072 | 400,000 |
| 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 → | 0.1040 | 0.0500 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.4160 | 0.4000 |
Frequently asked questions
Which is cheaper, Qwen3 VL 32B Instruct or GPT-5 Nano?
GPT-5 Nano is cheaper than Qwen3 VL 32B Instruct on a 50/50 input/output blend by about $0.035 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 VL 32B Instruct or GPT-5 Nano?
GPT-5 Nano has the larger context window at 400k tokens versus 131k tokens for Qwen3 VL 32B Instruct. That means GPT-5 Nano can ingest about 3.1x as much text per request.
What is the difference between Qwen3 VL 32B Instruct and GPT-5 Nano?
Qwen3 VL 32B Instruct comes from Qwen; GPT-5 Nano comes from OpenAI. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.