--- title: Llama 3 70B Instruct Turbo — Fast LLM | ModelsLab description: Access Meta Llama 3 70B Instruct Turbo API for efficient instruction-tuned generation with 131K context. Try the Meta Llama 3 70B Instruct Turbo model now. url: https://modelslab.com/meta-llama-3-70b-instruct-turbo canonical: https://modelslab.com/meta-llama-3-70b-instruct-turbo type: website component: Seo/ModelPage generated_at: 2026-04-15T02:06:22.398064Z --- Available now on ModelsLab · Language Model Meta Llama 3 70B Instruct Turbo Turbocharge Llama 3 Inference --- [Try Meta Llama 3 70B Instruct Turbo](/models/meta/meta-llama-Meta-Llama-3-70B-Instruct-Turbo) [API Documentation](https://docs.modelslab.com) 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](/models/meta/meta-llama-Meta-Llama-3-70B-Instruct-Turbo). 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 ](https://docs.modelslab.com) PythonJavaScriptcURL Copy ```

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 ](https://docs.modelslab.com) ### What is Meta Llama 3 70B Instruct Turbo? ### How to use Meta Llama 3 70B Instruct Turbo API? ### What is the context length of meta llama 3 70b instruct turbo model? ### Is Meta Llama 3 70B Instruct Turbo a good alternative? ### Does meta llama 3 70b instruct turbo api support function calling? ### What are pricing details for Meta Llama 3 70B Instruct Turbo LLM? Ready to create? --- Start generating with Meta Llama 3 70B Instruct Turbo on ModelsLab. [Try Meta Llama 3 70B Instruct Turbo](/models/meta/meta-llama-Meta-Llama-3-70B-Instruct-Turbo) [API Documentation](https://docs.modelslab.com) --- *This markdown version is optimized for AI agents and LLMs.* **Links:** - [Website](https://modelslab.com) - [API Documentation](https://docs.modelslab.com) - [Blog](https://modelslab.com/blog) --- *Generated by ModelsLab - 2026-04-15*