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Meta Llama 3 70B Instruct TurboTurbocharge Llama 3 Inference

Try Meta Llama 3 70B Instruct TurboAPI Documentation

Deploy Llama 3 Turbo Fast

131K Context

Extended Token Window

Handles 131K input and output tokens for long-context tasks in Meta Llama 3 70B Instruct Turbo.

Function Calling

Tool Integration Ready

Supports function calling in Meta Llama 3 70B Instruct Turbo API for structured responses.

Cost Efficient

Low Token Pricing

Priced at $0.1/M input, $0.32/M output for Meta Llama 3 70B Instruct Turbo model.

Examples

See what Meta Llama 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 code reviewer. Analyze this Python function for bugs, efficiency, and best practices.<|eot_id|><|start_header_id|>user<|end_header_id|>def fibonacci(n): if n <= 1: return n return fibonacci(n-1) + fibonacci(n-2)<|eot_id|>”

JSON Extraction

“<|begin_of_text|><|start_header_id|>system<|end_header_id|>Extract key facts as JSON from the text provided.<|eot_id|><|start_header_id|>user<|end_header_id|>Tesla reported Q3 earnings of $2.2B profit on $25.7B revenue, up 20% YoY.<|eot_id|>”

Tech Summary

“<|begin_of_text|><|start_header_id|>system<|end_header_id|>Summarize technical documents concisely while preserving key details.<|eot_id|><|start_header_id|>user<|end_header_id|>Explain grouped query attention (GQA) in transformer models and its inference benefits.<|eot_id|>”

Reasoning Chain

“<|begin_of_text|><|start_header_id|>system<|end_header_id|>Use step-by-step reasoning for complex math problems.<|eot_id|><|start_header_id|>user<|end_header_id|>If a train leaves at 60 mph and another at 80 mph from stations 300 miles apart, when do they meet?<|eot_id|>”

For Developers

A few lines of code.
Llama 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 70B Instruct Turbo

Read the docs

Meta Llama 3 70B Instruct Turbo is an optimized 70B parameter instruction-tuned LLM for dialogue and tasks. It supports 131K context with function calling. Outperforms prior Llama 3.1 70B on benchmarks.

Access via standard OpenAI-compatible endpoints. Send chat messages in Llama 3 format with system and user roles. Supports streaming and JSON mode.

Supports 131K token context window for input and output. Ideal for long documents and conversations.

Yes, Meta Llama 3 70B Instruct Turbo alternative offers cost efficiency at $0.1/M input tokens. Matches or exceeds Llama 3.1 70B performance.

Yes, includes function calling for tool use. Compatible with OpenAI SDK and structured outputs.

Input: $0.1 per million tokens. Output: $0.32 per million tokens. Among lowest for 70B class models.

Ready to create?

Start generating with Meta Llama 3 70B Instruct Turbo on ModelsLab.

Try Meta Llama 3 70B Instruct TurboAPI Documentation