Mistral AI · language model
Voxtral Small 24B 2507: pricing, context and benchmarks
Voxtral Small 24B 2507 is a language model from Mistral AI released on October 30, 2025. It costs $0.1 per million input tokens and $0.3 per million output tokens, with a 32.8K-token context window. No independent score has been published for it so far.
What you need to know about Voxtral Small 24B 2507
Voxtral Small 24B 2507 was released by Mistral AI (France) on October 30, 2025. It accepts text, audio, and files as input and produces text. Its context window reaches 32,768 tokens, larger than 8 % of the models in the registry, and it can write up to 26,214 tokens in one go.
It has been available for about 12 months, long enough for its prices and scores to have settled.
Voxtral Small 24B 2507 pricing
On OpenRouter, Voxtral Small 24B 2507 costs $0.1 per million input tokens and $0.3 per million output tokens. Counting three tokens read for every token written, its average price is $0.15 per million, more expensive than 17 % of paid models (5.1 times cheaper than the median of $0.76).
A concrete example: a task that sends 2,000 tokens (about 1,500 words) and receives a 500-token answer costs $0.0004. At 1,000 tasks a day, budget $11 a month. The price calculator redoes this with your own volumes.
Tokens already sent and cached are billed at $0.01 per million.
Performance and benchmarks
Epoch AI does not yet publish a capabilities index for Voxtral Small 24B 2507. We therefore show no made-up performance rating: only price, context and features count towards its score.
Features, privacy and local use
- It supports tool calling, so it can drive an AI agent, call APIs or MCP servers.
- It can answer in an enforced JSON format (structured outputs).
Its weights are open and downloadable from Hugging Face: you can run it on your own machines without sending your data to a third party.
Other Mistral AI models
| Model | Released | Avg. price | ECI |
|---|---|---|---|
| Mistral Large 4 | Oct 6, 2026 | $2.07 | 150 |
| Mistral Medium 3.5 | Apr 30, 2026 | $3 | 141.4 |
| Mistral Small 4 | Mar 16, 2026 | $0.26 | — |
| Devstral 2 2512 | Dec 9, 2025 | $0.8 | — |
| Ministral 3 14B 2512 | Dec 2, 2025 | $0.2 | — |
| Ministral 3 8B 2512 | Dec 2, 2025 | $0.15 | — |
| Ministral 3 3B 2512 | Dec 2, 2025 | $0.1 | — |
| Mistral Large 3 2512 | Dec 1, 2025 | $0.75 | — |
Verdict
Voxtral Small 24B 2507 scores 63/100 on our grid, which only uses published figures.
Strengths
- Low price: cheaper than 83 % of paid models.
- Open weights: self-hostable, your data stays with you.
- Agent-ready: tool calling and structured JSON output.
Limitations
- No independent score published: performance cannot be verified.
Avoid if…
- you analyse long documents in one go.
Alternatives
- Qwen3 14B$0.15
- Qwen2.5 7B Instruct$0.13
- Reka Flash 3$0.13
- Qwen3 30B A3B$0.22
- Hy-MT2-30B-A3B$0.13
- Hy-MT2-7B$0.13
Comparisons and tools
Learn more
Frequently asked questions
How much does Voxtral Small 24B 2507 cost?
$0.1 per million input tokens and $0.3 per million output tokens on OpenRouter (checked October 25, 2026). A task with 2,000 tokens read and 500 written costs $0.0004.
How big is Voxtral Small 24B 2507's context window?
32,768 tokens, roughly 25,000 words, with a maximum of 26,214 output tokens.
Can Voxtral Small 24B 2507 run locally?
Yes, its weights are published.
Is Voxtral Small 24B 2507 suitable for an AI agent?
Technically yes: it supports tool calling and structured outputs. Then check its cost for your volume.
What are the alternatives to Voxtral Small 24B 2507?
The closest models on price and performance are Qwen3 14B, Qwen2.5 7B Instruct, and Reka Flash 3.
Related articles
- Mistral AI: models, pricing and agentsOctober 25, 2026
- LLM benchmarks: how to read model rankingsOctober 24, 2026
- Running a local LLM: hardware, software, and limitationsOctober 24, 2026
- Open-source or closed models for your AI agents?October 5, 2026
Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score