Side-by-side comparison
Qwen2.5 7B Instruct vs Voxtral Small 24B 2507
Qwen
Qwen2.5 7B Instruct
🔧 Tools
{} JSON
Input / 1M
$0.0400
Output / 1M
$0.1000
Mistral
Voxtral Small 24B 2507
🔧 Tools
{} JSON
Input / 1M
$0.1000
Output / 1M
$0.3000
| Qwen2.5 7B Instruct | Voxtral Small 24B 2507 | |
|---|---|---|
| Provider | Qwen | Mistral |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 32,768 | 32,000 |
| 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 → | 0.0400 | 0.1000 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.1000 | 0.3000 |
Frequently asked questions
Which is cheaper, Qwen2.5 7B Instruct or Voxtral Small 24B 2507?
Qwen2.5 7B Instruct is cheaper than Voxtral Small 24B 2507 on a 50/50 input/output blend by about $0.13 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, Qwen2.5 7B Instruct or Voxtral Small 24B 2507?
Qwen2.5 7B Instruct has the larger context window at 33k tokens versus 32k tokens for Voxtral Small 24B 2507. That means Qwen2.5 7B Instruct can ingest about 1.0x as much text per request.
What is the difference between Qwen2.5 7B Instruct and Voxtral Small 24B 2507?
Qwen2.5 7B Instruct comes from Qwen; Voxtral Small 24B 2507 comes from Mistral. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.