Side-by-side comparison
Mistral Nemo vs Qwen3 235B A22B Instruct 2507
Qwen
Qwen3 235B A22B Instruct 2507
🔧 Tools
{} JSON
Input / 1M
$0.0710
Output / 1M
$0.1000
| Mistral Nemo | Qwen3 235B A22B Instruct 2507 | |
|---|---|---|
| Provider | Mistral | Qwen |
| 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). | tools, json_mode | tools, json_mode |
| Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary → | 0.0200 | 0.0710 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.0300 | 0.1000 |
Frequently asked questions
Which is cheaper, Mistral Nemo or Qwen3 235B A22B Instruct 2507?
Mistral Nemo is cheaper than Qwen3 235B A22B Instruct 2507 on a 50/50 input/output blend by about $0.0605 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, Mistral Nemo or Qwen3 235B A22B Instruct 2507?
Qwen3 235B A22B Instruct 2507 has the larger context window at 262k tokens versus 131k tokens for Mistral Nemo. That means Qwen3 235B A22B Instruct 2507 can ingest about 2.0x as much text per request.
What is the difference between Mistral Nemo and Qwen3 235B A22B Instruct 2507?
Mistral Nemo comes from Mistral; Qwen3 235B A22B Instruct 2507 comes from Qwen. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.