Model Library/Llama 3.3 70B Instruct
Llama 3.3 70B Instruct

Llama 3.3 70B Instruct

meta-llama/llama-3.3-70b-instruct
by Meta
Input price
$0.135/ M tokens
Output price
$0.4/ M tokens
Context window
12Ktokens
Max output
12Ktokens

The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model is optimized for multilingual dialogue use cases and outperforms many of the available open source and closed chat models on common industry benchmarks. Supported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.

Features

Serverless API

Docs

meta-llama/llama-3.3-70b-instruct is available via Novita's serverless API, where you pay per token. There are several ways to call the API, including OpenAI-compatible endpoints with exceptional reasoning performance.

On-demand Deployments

Docs

On-demand deployments allow you to use meta-llama/llama-3.3-70b-instruct on dedicated GPUs with high-performance serving stack with high reliability and no rate limits.

Available Serverless

Run queries immediately, pay only for usage

Input$0.135 / M Tokens
Output$0.4 / M Tokens
  1. Get your API key

    Sign up and create a key to run the code below. The API is OpenAI-compatible.

  2. Make your first request

    The API is OpenAI-compatible: point any OpenAI SDK at the base URL and pass this model ID.

    Base URL
    https://api.novita.ai/openai
    Model ID
    meta-llama/llama-3.3-70b-instruct
    API Endpoint
    chat/completionscompletions
    1from openai import OpenAI
    2
    3client = OpenAI(
    4    api_key="<Your API Key>",
    5    base_url="https://api.novita.ai/openai"
    6)
    7
    8response = client.chat.completions.create(
    9    model="meta-llama/llama-3.3-70b-instruct",
    10    messages=[
    11        {"role": "system", "content": "You are a helpful assistant."},
    12        {"role": "user", "content": "Hello, how are you?"}
    13    ],
    14    max_tokens=12288,
    15    temperature=0.7
    16)
    17
    18print(response.choices[0].message.content)