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Qwen2.5 VL 32B Instruct vs Voxtral Small 24B 2507
This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.
Model
Qwen2.5 VL 32B Instruct
Context window
128K
128,000 tokens · ~96K words
Context window · side by side
Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.
Qwen2.5 VL 32B Instruct has about 4× the context window of the other in this pair.
Qwen2.5 VL 32B Instruct has 300% more context capacity (128K vs 32K tokens).
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Qwen2.5 VL 32B Instruct. Its 128K context fits entire documents without chunking (vs 32K).
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Qwen2.5 VL 32B Instruct | Voxtral Small 24B 2507 |
|---|---|---|
| Context window | 128,000 tokens (128K) | 32,000 tokens (32K) |
| Max output tokens | N/A | N/A |
| Speed tier | Balanced | Balanced |
| Vision | Yes | No |
| Function calling | No | Yes |
| Extended thinking | No | No |
| Prompt caching | No | Yes |
| Batch API | No | No |
| Release date | Mar 2025 | Oct 2025 |
Frequently asked questions
Powered by Mem0
Use a smaller model.
Get better results.
Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.
Example: a multi-turn chat session
80% less to send — works with any model