Mistral: Voxtral Small 24B 2507

Mistral: Voxtral Small 24B 2507

Mistral AILLM

Voxtral Small is an enhancement of Mistral Small 3, incorporating state-of-the-art audio input capabilities while retaining best-in-class text performance. It excels at speech transcription, translation and audio understanding. Input audio...

MultimodalOpen SourceAPI AvailableAudioTool Calling

Specifications

Context Window

32K tokens

Input Price/1M

$0.10

Output Price/1M

$0.30

Parameters

Information

Tool Calling
✅ Supported
Vision
❌ Not supported
Audio
✅ Supported

Full Analysis: Mistral: Voxtral Small 24B 2507

What is Mistral: Voxtral Small 24B 2507?

Mistral: Voxtral Small 24B 2507 is an AI model developed by Mistral AI, classified as a large language model (LLM). It is a multimodal model, capable of processing text, images, and potentially other media types. As an open source model, it is available for download, customization, and on-premises deployment. With a context window of 32K tokens, it is suitable for processing medium-sized documents like articles, reports, and code sections.

Pricing & Costs in 2026

Mistral: Voxtral Small 24B 2507 is usage-based, priced at $0.1/1M input tokens and $0.3/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. At this aggressive price point, it is one of the most cost-effective options on the market, ideal for high-volume applications like chatbots, bulk document analysis, and automation.

Benchmarks & Performance

We don't have detailed benchmark results for Mistral: Voxtral Small 24B 2507 yet. Benchmarks are updated weekly as new data becomes available from sources like Artificial Analysis, LM Arena, and LiveBench.

It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.

Recommended Use Cases

Mistral: Voxtral Small 24B 2507 is suitable for a wide range of AI applications: automation with tool calling (API integration, databases, external systems), multimodal processing combining text and images, high-volume chatbots and automated support, text generation, summarization, translation, and general assistance.

Comparison with Alternatives

In the 2026 AI model ecosystem, Mistral: Voxtral Small 24B 2507 competes directly with similarly capable models. Mistral AI competes in this segment against OpenAI, Anthropic, Google, and Meta. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.

For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.

Frequently Asked Questions

What is Mistral: Voxtral Small 24B 2507?

Voxtral Small is an enhancement of Mistral Small 3, incorporating state-of-the-art audio input capabilities while retaining best-in-class text performance. It excels at speech transcription, translation and audio understanding. Input audio...

How much does Mistral: Voxtral Small 24B 2507 cost?

Mistral: Voxtral Small 24B 2507 costs $0.1/1M input tokens and $0.3/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.

How does Mistral: Voxtral Small 24B 2507 compare with other models?

We don't have detailed benchmarks for Mistral: Voxtral Small 24B 2507 yet. Check the main benchmark page to compare available models.

Is Mistral: Voxtral Small 24B 2507 open source?

Yes, Mistral: Voxtral Small 24B 2507 is an open source model. You can deploy it on-premises, customize it via fine-tuning, and maintain full control over your data. Check the official repository for the specific license.

What is Mistral: Voxtral Small 24B 2507 best for?

Mistral: Voxtral Small 24B 2507 excels at general-purpose language tasks. It supports tool calling for API integrations and automation.

Last updated: May 24, 2026 View methodology →