Ship a Full-Stack App with One Prompt

Copy this prompt into your AI coding agent, or open it in one below.

Give this to your AI Create a to-do list app using Puter.js

Coding manually? see the guide

Mistral AI: Voxtral Small

This model is no longer available.

Add AI to your application with Puter.js.

Explore Other Models

Model Card

Voxtral Small is a 24B parameter speech understanding model built on Mistral Small 3.1 under Apache 2.0. It supports 30-minute transcription, 40-minute audio understanding, Q&A, summarization, and function calling from voice in 8+ languages.

Context Window 33K

tokens

Max Output 33K

tokens

Input Cost $0.1

per million tokens

Output Cost $0.3

per million tokens

Input text, audio

modalities

Tool Use Yes

 

Knowledge Cutoff Jul 2025

 

Release Date Jul 15, 2025

 

Code Example

Add AI to your app with the Puter.js AI API, no API keys or setup required.

// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';

puter.ai.chat("Explain quantum computing in simple terms").then(response => {
    document.body.innerHTML = response.message.content;
});
<html>
<body>
    <script src="https://js.puter.com/v2/"></script>
    <script>
        puter.ai.chat("Explain quantum computing in simple terms").then(response => {
            document.body.innerHTML = response.message.content;
        });
    </script>
</body>
</html>

More AI Models From Mistral AI

Find other Mistral AI models →

Chat

Mistral Large 4

Mistral Large 4 is a multimodal sparse mixture-of-experts model from Mistral AI with 1 trillion total parameters and 49 billion active. It accepts text and image input and produces text output, and it supports tool calling and structured responses through the API. Mistral positions it for coding, cybersecurity, finance, manufacturing, and visual grounding. In Mistral's preview results it scores 62% on DeepSWE v1.1, 67% on FinWorkBench, and 73% on DIOR-RSVG, and it solves 93% of the 40 Cybench exercises. It is suited to developers building coding agents, security analysis tools, and enterprise workflows that need long context (1M tokens) and image understanding. Open weights are planned for release after the API preview.

Chat

Mistral Medium 3.5

Mistral Medium 3.5 is a dense 128-billion-parameter multimodal model from Mistral AI that unifies instruction-following, reasoning, and coding into a single set of weights. It features a 256k-token context window, native function calling, structured JSON output, and vision capabilities via a custom-trained encoder that handles variable image sizes. A per-request reasoning_effort parameter lets you toggle between fast responses and deeper chain-of-thought processing, making the same model suitable for quick chat replies and complex agentic workflows. On benchmarks, it scores 77.6% on SWE-Bench Verified and 91.4% on τ³-Telecom. It replaces Mistral's previous Medium 3.1, Magistral, and Devstral 2 models. Priced at $1.50 per million input tokens and $7.50 per million output tokens, it's a strong fit for developers building tool-calling agents, long-horizon coding tasks, and multi-step automation pipelines.

Chat

Mistral Medium 3.5

Mistral Medium 3.5 is a dense 128-billion-parameter multimodal model from Mistral AI that unifies instruction-following, reasoning, and coding into a single set of weights. This entry is Mistral's own dated direct-integration id for the same release available as mistralai/mistral-medium-3-5 through OpenRouter. It features a 256k-token context window, native function calling, structured JSON output, and vision capabilities via a custom-trained encoder that handles variable image sizes. A per-request reasoning_effort parameter lets you toggle between fast responses and deeper chain-of-thought processing, making the same model suitable for quick chat replies and complex agentic workflows. On benchmarks, it scores 77.6% on SWE-Bench Verified and 91.4% on τ³-Telecom. It replaces Mistral's previous Medium 3.1, Magistral, and Devstral 2 models. Priced at $1.50 per million input tokens and $7.50 per million output tokens, it's a strong fit for developers building tool-calling agents, long-horizon coding tasks, and multi-step automation pipelines.

Frequently Asked Questions

How do I use Voxtral Small?

Voxtral Small is no longer available through Puter.js. Explore other AI models for alternatives.

What is the pricing for Voxtral Small?
Voxtral Small costs $0.1 per 1M input tokens and $0.3 per 1M output tokens.
Price per 1M tokens
Input$0.1
Output$0.3
Who created Voxtral Small?

Voxtral Small was created by Mistral AI and released on Jul 15, 2025.

What is the context window of Voxtral Small?

Voxtral Small supports a context window of 33K tokens. For reference, that is roughly equivalent to 66 pages of text.

What is the max output length of Voxtral Small?

Voxtral Small can generate up to 33K tokens in a single response.

What is the knowledge cutoff of Voxtral Small?

Voxtral Small has a knowledge cutoff date of Jul 2025. This means the model was trained on data available up to that date.

What types of input can Voxtral Small process?

Voxtral Small accepts the following input types: text, audio. It produces: text.

Does Voxtral Small support tool use (function calling)?

Yes, Voxtral Small supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.

Does it work with React / Vue / Vanilla JS / Node / etc.?

Yes — the Voxtral Small API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.

Get started with Puter.js

Add AI to your application without worrying about API keys or setup.

Explore Models View Tutorials