Mistral AI: Mistral Large 3
mistralai/mistral-large-2512
Access Mistral Large 3 from Mistral AI using Puter.js AI API.
Get Started// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "mistralai/mistral-large-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/mistral-large-2512"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
# pip install openai
from openai import OpenAI
client = OpenAI(
base_url="https://api.puter.com/puterai/openai/v1/",
api_key="YOUR_PUTER_AUTH_TOKEN",
)
response = client.chat.completions.create(
model="mistralai/mistral-large-2512",
messages=[
{"role": "user", "content": "Explain quantum computing in simple terms"}
],
)
print(response.choices[0].message.content)
curl https://api.puter.com/puterai/openai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_PUTER_AUTH_TOKEN" \
-d '{
"model": "mistralai/mistral-large-2512",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
Mistral Large 3 is a 675B parameter sparse MoE model (41B active) trained on 3000 H200 GPUs, representing Mistral's frontier open-weight multimodal model. It supports 256K context, native vision, and excels in agentic workflows and enterprise applications.
Context Window 262K
tokens
Max Output 262K
tokens
Input Cost $0.5
per million tokens
Output Cost $1.5
per million tokens
Input text, image
modalities
Tool Use Yes
Knowledge Cutoff Nov 2024
Release Date Nov 1, 2024
Output Speed 58
tokens / sec
Latency 0.57s
time to first token
Model Playground
Try Mistral Large 3 instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How Mistral Large 3 performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 68.0% |
| Humanity's Last Exam Cross-domain reasoning | 4.1% |
| LiveCodeBench Recent coding problems | 46.5% |
| SciCode Scientific programming | 36.2% |
| AIME 2025 Advanced math exam | 38.0% |
| IFBench Instruction following | 36.2% |
| LCR Long-context reasoning | 34.7% |
| Terminal-Bench Hard Agentic terminal tasks | 15.9% |
| τ²-Bench Tool use / agents | 24.6% |
Scores sourced from Artificial Analysis.
Find other Mistral AI models →
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.
ChatMistral Small Creative
Mistral Small Creative is a specialized Labs model variant optimized for creative content generation. It builds on the Mistral Small architecture with adjustments for more imaginative and varied outputs in writing tasks.
ChatMinistral 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
You can access Mistral Large 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.
Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add Mistral Large 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.
| Price per 1M tokens | |
|---|---|
| Input | $0.5 |
| Output | $1.5 |
Mistral Large 3 was created by Mistral AI and released on Nov 1, 2024.
Mistral Large 3 supports a context window of 262K tokens. For reference, that is roughly equivalent to 524 pages of text.
Mistral Large 3 can generate up to 262K tokens in a single response.
Mistral Large 3 has a knowledge cutoff date of Nov 2024. This means the model was trained on data available up to that date.
Mistral Large 3 accepts the following input types: text, image. It produces: text.
Yes, Mistral Large 3 supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
Yes — the Mistral Large 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.
Get started with Puter.js
Add Mistral Large 3 to your app without worrying about API keys or setup.
Read the Docs View Tutorials