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Available now on ModelsLab · Language Model

Meta Llama 3.3 70B Instruct TurboTurbocharge Llama Inference

Try Meta Llama 3.3 70B Instruct TurboAPI Documentation

Run Llama 3.3 Turbo Now

131K Context

Massive Token Window

Handles 131K input and output tokens for long-context tasks.

Function Calling

Tool Integration Ready

Supports structured function calls in Meta Llama 3.3 70B Instruct Turbo API.

Cost Efficient

Low Token Pricing

Starts at $0.1/M input, $0.32/M output on select providers.

Examples

See what Meta Llama 3.3 70B Instruct Turbo can create

Copy any prompt below and try it yourself in the playground.

Code Review

“<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are a senior software engineer. Review this Python code for bugs and optimizations.<|eot_id|><|start_header_id|>user<|end_header_id|>def fibonacci(n): if n <= 1: return n return fibonacci(n-1) + fibonacci(n-2) print(fibonacci(10))<|eot_id|>”

SQL Query

“<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are a database expert. Write efficient SQL for this schema.<|eot_id|><|start_header_id|>user<|end_header_id|>Schema: users(id, name, email). Find users with gmail addresses, ordered by name.<|eot_id|>”

JSON Schema

“<|begin_of_text|><|start_header_id|>system<|end_header_id|>Generate valid JSON schemas for APIs.<|eot_id|><|start_header_id|>user<|end_header_id|>Create schema for a product catalog with id, name, price, and tags array.<|eot_id|>”

Math Proof

“<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are a mathematician. Provide step-by-step proofs.<|eot_id|><|start_header_id|>user<|end_header_id|>Prove that the sum of first n odd numbers equals n squared.<|eot_id|>”

For Developers

A few lines of code.
Instruct Turbo. One Call.

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

  • Serverless: scales to zero, scales to millions
  • Pay per token, no minimums
  • Python and JavaScript SDKs, plus REST API
API Documentation 
import requests


response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about Meta Llama 3.3 70B Instruct Turbo

Read the docs

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Try Meta Llama 3.3 70B Instruct TurboAPI Documentation