Mistral AI: Mistral Small 4

mistralai/mistral-small-2603

Try Mistral Small 4 for free in your browser, and add it to your app for free with Puter.js AI API.

Try it free Add to your app

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

puter.ai.chat("Explain quantum computing in simple terms", {
    model: "mistralai/mistral-small-2603"
}).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", {
            model: "mistralai/mistral-small-2603"
        }).then(response => {
            document.body.innerHTML = response.message.content;
        });
    </script>
</body>
</html>

Model Card

Mistral Small 4 is a 119B-parameter open-source Mixture-of-Experts model (6B active per token) released under Apache 2.0, unifying instruction-following, reasoning, multimodal (text + image), and agentic coding into a single deployment. It features 128 experts, a 256k context window, and configurable reasoning effort that lets developers toggle between fast responses and deep step-by-step reasoning per request. Compared to its predecessor Mistral Small 3, it delivers 40% lower latency and 3x higher throughput while matching or surpassing GPT-OSS 120B on key benchmarks.

Context Window 256K

tokens

Max Output 256K

tokens

Input Cost $0.15

per million tokens

Output Cost $0.6

per million tokens

Input text, image

modalities

Tool Use Yes

Knowledge Cutoff Jun 2025

Release Date Mar 16, 2026

Output Speed 172

tokens / sec

Latency 0.64s

time to first token

Try Mistral Small 4 for free

Try Mistral Small 4 instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.

Chat mistralai/mistral-small-2603

Chat with Mistral Small 4

Benchmarks

How Mistral Small 4 performs on standard evaluations.

Artificial Analysis

Intelligence Index

4.3

Better than 1% of tracked models

Benchmark	Score
GPQA Diamond Graduate-level science Q&A	76.9%
Humanity's Last Exam Cross-domain reasoning	9.9%
SciCode Scientific programming	38.8%
IFBench Instruction following	48.2%
LCR Long-context reasoning	49.7%
Terminal-Bench Hard Agentic terminal tasks	17.4%
τ²-Bench Tool use / agents	41.2%

Scores sourced from Artificial Analysis.

Find other Mistral AI models →

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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.

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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.

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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 Mistral Small 4?

You can access Mistral Small 4 by Mistral AI through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.

Can I try Mistral Small 4 for free?

Mistral Small 4 is free to try with a Puter account. Every account includes a free AI allowance, and you can chat with it in the playground on this page. You can upgrade your account anytime for a larger allowance.

Is the Mistral Small 4 API free for developers?

Mistral Small 4 is free to integrate using the Puter.js AI API. With the User-Pays Model, you can add AI to your app for $0, since users cover their own AI usage through their Puter account.

What is the pricing for Mistral Small 4?

Mistral Small 4 costs $0.15 per 1M input tokens and $0.6 per 1M output tokens.

	Price per 1M tokens
Input	$0.15
Output	$0.6

Who created Mistral Small 4?

Mistral Small 4 was created by Mistral AI and released on Mar 16, 2026.

What is the context window of Mistral Small 4?

Mistral Small 4 supports a context window of 256K tokens. For reference, that is roughly equivalent to 512 pages of text.

What is the max output length of Mistral Small 4?

Mistral Small 4 can generate up to 256K tokens in a single response.

What is the knowledge cutoff of Mistral Small 4?

Mistral Small 4 has a knowledge cutoff date of Jun 2025. This means the model was trained on data available up to that date.

What types of input can Mistral Small 4 process?

Mistral Small 4 accepts the following input types: text, image. It produces: text.

Does Mistral Small 4 support tool use (function calling)?

Yes, Mistral Small 4 supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.

How does Mistral Small 4 perform on benchmarks?

Mistral Small 4 scores 4.3 on the Artificial Analysis Intelligence Index, outperforming 1% of tracked models.

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

Yes — the Mistral Small 4 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.

Add Mistral Small 4 to your app for free

Developers can integrate Mistral Small 4 for free using the Puter.js AI API.
With the User-Pays Model, each user covers their own AI usage instead of the developer.

Get started How pricing works