--- title: Llama 3.1 70B Turbo — Fast LLM | ModelsLab description: Run Meta Llama 3.1 70B Instruct Turbo for 131k context and function calling. Generate complex responses via API now. url: https://modelslab.com/meta-llama-31-70b-instruct-turbo canonical: https://modelslab.com/meta-llama-31-70b-instruct-turbo type: website component: Seo/ModelPage generated_at: 2026-04-15T02:06:20.917778Z --- Available now on ModelsLab · Language Model Meta Llama 3.1 70B Instruct Turbo Turbocharge Llama Inference --- [Try Meta Llama 3.1 70B Instruct Turbo](/models/meta/meta-llama-Meta-Llama-3.1-70B-Instruct-Turbo) [API Documentation](https://docs.modelslab.com) Deploy Turbo Performance --- 131K Context ### Handle Long Inputs Process 131k input and output tokens for extended dialogues and documents. Function Calling ### Integrate Tools Seamlessly Call external functions directly in Meta Llama 3.1 70B Instruct Turbo API responses. Cost Efficient ### Scale Without Breaking Bank Access Meta Llama 3.1 70B Instruct Turbo model at $0.4 per million tokens. Examples See what Meta Llama 3.1 70B Instruct Turbo can create --- Copy any prompt below and try it yourself in the [playground](/models/meta/meta-llama-Meta-Llama-3.1-70B-Instruct-Turbo). Code Review “Review this Python function for bugs and suggest optimizations: def fibonacci(n): if n <= 1: return n else: return fibonacci(n-1) + fibonacci(n-2). Provide refactored code with memoization.” Document Summary “Summarize key points from this 10k token research paper on quantum computing advancements, focusing on practical applications and limitations. Extract main claims and evidence.” Multilingual Translation “Translate this technical spec from English to Spanish, German, and Hindi while preserving code snippets: 'API endpoint: POST /v1/completions with JSON payload {model: "llama", prompt: "hello"}'.” JSON Generation “Generate a valid JSON schema for a user profile API including fields for name, email, preferences array, and nested address object. Include validation rules.” For Developers A few lines of code. Turbo Llama. 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 3.1 70B Instruct Turbo --- [Read the docs ](https://docs.modelslab.com) ### What is Meta Llama 3.1 70B Instruct Turbo? ### How does Meta Llama 3.1 70B Instruct Turbo API compare to alternatives? ### What context length supports meta llama 3.1 70b instruct turbo model? ### Does Meta Llama 3.1 70B Instruct Turbo support function calling? ### What pricing for meta llama 3.1 70b instruct turbo api? ### Is Meta Llama 3.1 70B Instruct Turbo multilingual? Ready to create? --- Start generating with Meta Llama 3.1 70B Instruct Turbo on ModelsLab. [Try Meta Llama 3.1 70B Instruct Turbo](/models/meta/meta-llama-Meta-Llama-3.1-70B-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*