Mistral AI: Mistral Large 4
mistralai/mistral-large-4-0
Try Mistral Large 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-large-4-0"
}).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-large-4-0"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
Model Card
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 (524K tokens) and image understanding. Open weights are planned for release after the API preview.
Context Window 524K
tokens
Max Output 262K
tokens
Input Cost $0.68
per million tokens
Output Cost $2.09
per million tokens
Input text, image
modalities
Tool Use Yes
Release Date Oct 6, 2026
Try Mistral Large 4 for free
Try Mistral Large 4 instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
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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.
ChatMistral 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.
ChatMistral Small 4
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.
Frequently Asked Questions
You can access Mistral Large 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.
Mistral Large 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.
Mistral Large 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.
| Price per 1M tokens | |
|---|---|
| Input | $0.68 |
| Output | $2.09 |
Mistral Large 4 was created by Mistral AI and released on Oct 6, 2026.
Mistral Large 4 supports a context window of 524K tokens. For reference, that is roughly equivalent to 1,049 pages of text.
Mistral Large 4 can generate up to 262K tokens in a single response.
Mistral Large 4 accepts the following input types: text, image. It produces: text.
Yes, Mistral Large 4 supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
Yes — the Mistral Large 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 Large 4 to your app for free
Developers can integrate Mistral Large 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.