Mistral AI: Ministral 14B
mistralai/ministral-14b-2512
Try Ministral 14B 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/ministral-14b-2512"
}).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/ministral-14b-2512"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
Model Card
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.
Context Window 262K
tokens
Max Output 262K
tokens
Input Cost $0.2
per million tokens
Output Cost $0.2
per million tokens
Input text, image
modalities
Tool Use Yes
Knowledge Cutoff Dec 2025
Release Date Dec 16, 2025
Output Speed 79
tokens / sec
Latency 0.50s
time to first token
Try Ministral 14B for free
Try Ministral 14B instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How Ministral 14B performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 57.2% |
| Humanity's Last Exam Cross-domain reasoning | 4.6% |
| LiveCodeBench Recent coding problems | 35.1% |
| SciCode Scientific programming | 23.8% |
| AIME 2025 Advanced math exam | 30.0% |
| IFBench Instruction following | 32.0% |
| LCR Long-context reasoning | 26.3% |
| Terminal-Bench Hard Agentic terminal tasks | 4.5% |
| τ²-Bench Tool use / agents | 27.2% |
Scores sourced from Artificial Analysis.
Find other Mistral AI models →
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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
You can access Ministral 14B 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.
Ministral 14B 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.
Ministral 14B 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 | $0.2 |
| Output | $0.2 |
Ministral 14B was created by Mistral AI and released on Dec 16, 2025.
Ministral 14B supports a context window of 262K tokens. For reference, that is roughly equivalent to 524 pages of text.
Ministral 14B can generate up to 262K tokens in a single response.
Ministral 14B has a knowledge cutoff date of Dec 2025. This means the model was trained on data available up to that date.
Ministral 14B accepts the following input types: text, image. It produces: text.
Yes, Ministral 14B supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
Ministral 14B scores 6.0 on the Artificial Analysis Intelligence Index, outperforming 13% of tracked models. On coding, it scores 14.4 (outperforms 14% of models). On math, it scores 30.0 (outperforms 30% of models).
Yes — the Ministral 14B 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 Ministral 14B to your app for free
Developers can integrate Ministral 14B for free using the Puter.js AI API.
With the User-Pays Model, each user covers their own AI usage instead of the developer.