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Meta Llama 3.3 70B Instruct TurboTurbocharge Llama Inference

Try Meta Llama 3.3 70B Instruct TurboAPI Documentation

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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

Meta Llama 3.3 70B Instruct Turbo is a text-only 70B instruction-tuned LLM with function calling. It outperforms Llama 3.1 70B on math, reasoning, and multilingual tasks. Context reaches 131K tokens.

Llama 3.3 Turbo matches Llama 3.1 405B on select benchmarks while using less compute. It scores higher on MMLU Pro (68.9 vs 66.4) and GPQA (50.5 vs 48.0). Speed hits 96 tokens/second.

Pricing starts at $0.1 per million input tokens and $0.32 output on DeepInfra. Other providers range $0.12-$0.90 input. Check endpoint for current rates.

Yes, it natively supports function calling and JSON schema. Use standard OpenAI-compatible formats. No vision or audio modalities.

Yes, as a Meta Llama 3.3 70B Instruct Turbo alternative, it offers superior efficiency over larger models. Ideal for dialogue and instruction tasks with 128K+ context.

Available on DeepInfra, Together AI, Groq, Fireworks AI, and AWS Bedrock. Supports OpenAI SDK with failover routing. Free tiers exist on some platforms.

Ready to create?

Start generating with Meta Llama 3.3 70B Instruct Turbo on ModelsLab.

Try Meta Llama 3.3 70B Instruct TurboAPI Documentation