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Mistral Mistral Large 2402 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
Mistral Mistral Large 2402
Context window
32K
32,000 tokens · ~24K 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.
Same context window size for both models.
Mistral Mistral Large 2402 and Voxtral Small 24B 2507 have identical context windows (32K tokens).
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Mistral Mistral Large 2402 | Voxtral Small 24B 2507 |
|---|---|---|
| Context window | 32,000 tokens (32K) | 32,000 tokens (32K) |
| Max output tokens | 8,191 tokens (8K) | N/A |
| Speed tier | Deep | Balanced |
| Vision | No | No |
| Function calling | Yes | Yes |
| Extended thinking | No | No |
| Prompt caching | No | Yes |
| Batch API | No | No |
| Release date | N/A | Oct 2025 |
Pricing matrix
Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.
| Provider | Mistral Mistral Large 2402 in | Mistral Mistral Large 2402 out | Voxtral Small 24B 2507 in | Voxtral Small 24B 2507 out |
|---|---|---|---|---|
| Aws Bedrock | $10.40/M | $31.20/M | — | — |
Frequently asked questions
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Use a smaller model.
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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