Mistral AI: Pixtral Large
mistralai/pixtral-large-2411
Access Pixtral Large 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/pixtral-large-2411"
}).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/pixtral-large-2411"
}).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/pixtral-large-2411",
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/pixtral-large-2411",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
Pixtral Large is a 124B parameter open-weights multimodal model built on Mistral Large 2, achieving frontier-level image understanding. It processes up to 30 high-resolution images per input with 128K context, excelling in document and chart analysis.
Context Window 131K
tokens
Max Output 131K
tokens
Input Cost $2
per million tokens
Output Cost $6
per million tokens
Input text, image
modalities
Tool Use Yes
Knowledge Cutoff Nov 2024
Release Date Nov 1, 2024
Output Speed 52
tokens / sec
Latency 0.47s
time to first token
Model Playground
Try Pixtral Large instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How Pixtral Large performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 50.5% |
| Humanity's Last Exam Cross-domain reasoning | 3.6% |
| LiveCodeBench Recent coding problems | 26.1% |
| SciCode Scientific programming | 29.2% |
| MATH-500 Competition math | 71.4% |
| AIME 2024 Advanced math exam | 7.0% |
| AIME 2025 Advanced math exam | 2.3% |
| IFBench Instruction following | 34.5% |
| LCR Long-context reasoning | 10.3% |
| τ²-Bench Tool use / agents | 36.5% |
Scores sourced from Artificial Analysis.
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Frequently Asked Questions
You can access Pixtral Large 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 Pixtral Large 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 | $2 |
| Output | $6 |
Pixtral Large was created by Mistral AI and released on Nov 1, 2024.
Pixtral Large supports a context window of 131K tokens. For reference, that is roughly equivalent to 262 pages of text.
Pixtral Large can generate up to 131K tokens in a single response.
Pixtral Large has a knowledge cutoff date of Nov 2024. This means the model was trained on data available up to that date.
Pixtral Large accepts the following input types: text, image. It produces: text.
Yes, Pixtral Large supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
Yes — the Pixtral Large 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 Pixtral Large to your app without worrying about API keys or setup.
Read the Docs View Tutorials