Mistral AI: Mistral Medium 3

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Mistral Medium 3 delivers frontier performance at $0.4/$2 per million tokens, performing at 90%+ of Claude Sonnet 3.7 across benchmarks. It's deployable on 4+ GPUs and surpasses Llama 4 Maverick and Cohere Command A.

Context Window 131K

tokens

Max Output N/A

tokens

Input Cost $0.4

per million tokens

Output Cost $2

per million tokens

Release Date May 7, 2025

 

Output Speed 43

tokens / sec

Latency 0.50s

time to first token

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

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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.

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Mistral 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.

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Ministral 14B

Ministral 14B is part of the Ministral 3 family, a 14B parameter multimodal model with vision capabilities under Apache 2.0. It offers advanced capabilities for local deployment with instruct, base, and reasoning variants achieving 85% on AIME'25.

Frequently Asked Questions

How do I use Mistral Medium 3?

You can access Mistral Medium 3 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.

Is Mistral Medium 3 free?

Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add Mistral Medium 3 to your app at no cost — your users pay for their own AI usage directly, making it completely free for you as a developer.

What is the pricing for Mistral Medium 3?
Mistral Medium 3 costs $0.4 per 1M input tokens and $2 per 1M output tokens.
Price per 1M tokens
Input$0.4
Output$2
Who created Mistral Medium 3?

Mistral Medium 3 was created by Mistral AI and released on May 7, 2025.

What is the context window of Mistral Medium 3?

Mistral Medium 3 supports a context window of 131K tokens. For reference, that is roughly equivalent to 262 pages of text.

How does Mistral Medium 3 perform on benchmarks?

Mistral Medium 3 scores 18.8 on the Artificial Analysis Intelligence Index, outperforming 48% of tracked models. On coding, it scores 13.6 (outperforms 32% of models). On math, it scores 30.3 (outperforms 31% of models).

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

Yes — the Mistral Medium 3 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.

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