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MiniMax M2-her 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.
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.
MiniMax M2-her has about 2× the context window of the other in this pair.
MiniMax M2-her has 104% more context capacity (65K vs 32K tokens).
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use MiniMax M2-her. Its 65K 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 | MiniMax M2-her | Voxtral Small 24B 2507 |
|---|---|---|
| Context window | 65,536 tokens (65K) | 32,000 tokens (32K) |
| Max output tokens | 2,048 tokens (2K) | N/A |
| Speed tier | Fast | Balanced |
| Vision | No | No |
| Function calling | No | Yes |
| Extended thinking | No | No |
| Prompt caching | Yes | Yes |
| Batch API | No | No |
| Release date | Jan 2026 | 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