Meta Llama: Llama 3.3 70B Instruct
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Llama 3.3 70B Instruct is Meta's refined 70 billion parameter multilingual model with improved instruction following and tool use capabilities. It supports 8 languages and offers enhanced reasoning performance over previous versions.
Context Window 131K
tokens
Max Output 120K
tokens
Input Cost $0.14
per million tokens
Output Cost $0.4
per million tokens
Release Date Dec 6, 2024
Output Speed 86
tokens / sec
Latency 0.62s
time to first token
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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ChatLlama 4 Maverick
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Frequently Asked Questions
You can access Llama 3.3 70B Instruct by Meta Llama 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 Llama 3.3 70B Instruct 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 | $0.14 |
| Output | $0.4 |
Llama 3.3 70B Instruct was created by Meta Llama and released on Dec 6, 2024.
Llama 3.3 70B Instruct supports a context window of 131K tokens. For reference, that is roughly equivalent to 262 pages of text.
Llama 3.3 70B Instruct can generate up to 120K tokens in a single response.
Llama 3.3 70B Instruct scores 9.3 on the Artificial Analysis Intelligence Index, outperforming 30% of tracked models. On coding, it scores 11.9 (outperforms 11% of models). On math, it scores 7.7 (outperforms 11% of models).
Yes — the Llama 3.3 70B Instruct 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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