Mistral AI: Mistral Medium 3

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Model Card

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 105K

tokens

Input Cost $0.4

per million tokens

Output Cost $2

per million tokens

Input text, image, file

modalities

Tool Use Yes

Release Date May 7, 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>

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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 Medium 3?

Mistral Medium 3 is no longer available through Puter.js. Explore other AI models for alternatives.

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.

What is the max output length of Mistral Medium 3?

Mistral Medium 3 can generate up to 105K tokens in a single response.

What types of input can Mistral Medium 3 process?

Mistral Medium 3 accepts the following input types: text, image, file. It produces: text.

Does Mistral Medium 3 support tool use (function calling)?

Yes, Mistral Medium 3 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 Medium 3 perform on benchmarks?

Mistral Medium 3 scores 9.0 on the Artificial Analysis Intelligence Index, outperforming 36% of tracked 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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