Mistral AI: Mistral Small 3.2
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Mistral Small 3.2 is a 24B parameter multimodal model with 128K context, improved instruction following, and reduced repetition. It handles text and images, runs on single RTX 4090 when quantized, and delivers 150 tokens/second under Apache 2.0.
Context Window 131K
tokens
Max Output 131K
tokens
Input Cost $0.1
per million tokens
Output Cost $0.3
per million tokens
Input text, image
modalities
Tool Use Yes
Knowledge Cutoff Mar 2025
Release Date Jun 20, 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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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. 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.
Frequently Asked Questions
Mistral Small 3.2 is no longer available through Puter.js. Explore other AI models for alternatives.
| Price per 1M tokens | |
|---|---|
| Input | $0.1 |
| Output | $0.3 |
Mistral Small 3.2 was created by Mistral AI and released on Jun 20, 2025.
Mistral Small 3.2 supports a context window of 131K tokens. For reference, that is roughly equivalent to 262 pages of text.
Mistral Small 3.2 can generate up to 131K tokens in a single response.
Mistral Small 3.2 has a knowledge cutoff date of Mar 2025. This means the model was trained on data available up to that date.
Mistral Small 3.2 accepts the following input types: text, image. It produces: text.
Yes, Mistral Small 3.2 supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
Mistral Small 3.2 scores 7.0 on the Artificial Analysis Intelligence Index, outperforming 22% of tracked models. On coding, it scores 12.5 (outperforms 12% of models). On math, it scores 27.0 (outperforms 28% of models).
Yes — the Mistral Small 3.2 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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