Mistral AI: Pixtral Large

This model is no longer available.

Add AI to your application with Puter.js.

Explore Other Models

Model Card

Pixtral Large is a 124B parameter open-weights multimodal model built on Mistral Large 2, achieving frontier-level image understanding. It processes up to 30 high-resolution images per input with 128K context, excelling in document and chart analysis.

Context Window 131K

tokens

Max Output 131K

tokens

Input Cost $2

per million tokens

Output Cost $6

per million tokens

Input text, image

modalities

Tool Use Yes

Knowledge Cutoff Nov 2024

Release Date Nov 19, 2024

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 Pixtral Large?

Pixtral Large is no longer available through Puter.js. Explore other AI models for alternatives.

What is the pricing for Pixtral Large?

Pixtral Large costs $2 per 1M input tokens and $6 per 1M output tokens.

	Price per 1M tokens
Input	$2
Output	$6

Who created Pixtral Large?

Pixtral Large was created by Mistral AI and released on Nov 19, 2024.

What is the context window of Pixtral Large?

Pixtral Large supports a context window of 131K tokens. For reference, that is roughly equivalent to 262 pages of text.

What is the max output length of Pixtral Large?

Pixtral Large can generate up to 131K tokens in a single response.

What is the knowledge cutoff of Pixtral Large?

Pixtral Large has a knowledge cutoff date of Nov 2024. This means the model was trained on data available up to that date.

What types of input can Pixtral Large process?

Pixtral Large accepts the following input types: text, image. It produces: text.

Does Pixtral Large support tool use (function calling)?

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

How does Pixtral Large perform on benchmarks?

Pixtral Large scores 7.1 on the Artificial Analysis Intelligence Index, outperforming 23% of tracked models. On math, it scores 2.3 (outperforms 3% of models).

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

Yes — the Pixtral Large 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