--- title: Llama 3.1 8B Turbo — Fast LLM | ModelsLab description: Run Meta Llama 3.1 8B Instruct Turbo for 131k context and function calling. Generate precise responses via API. Try now. url: https://modelslab-frontend-v2-927501783998.us-east4.run.app/meta-llama-31-8b-instruct-turbo canonical: https://modelslab-frontend-v2-927501783998.us-east4.run.app/meta-llama-31-8b-instruct-turbo type: website component: Seo/ModelPage generated_at: 2026-05-13T09:44:02.723785Z --- Available now on ModelsLab · Language Model Meta Llama 3.1 8B Instruct Turbo Turbocharge Llama Responses --- [Try Meta Llama 3.1 8B Instruct Turbo](/models/meta/meta-llama-Meta-Llama-3.1-8B-Instruct-Turbo) [API Documentation](https://docs.modelslab.com) Deploy Turbo Performance --- 131K Context ### Handle Long Inputs Process 131k token context window for extended dialogues and documents. Function Calling ### Enable Tool Use Supports function calling for structured outputs and agent workflows. 150 Tokens/Second ### Run High Throughput Achieve 150 tokens per second speed on Meta Llama 3.1 8B Instruct Turbo API. Examples See what Meta Llama 3.1 8B Instruct Turbo can create --- Copy any prompt below and try it yourself in the [playground](/models/meta/meta-llama-Meta-Llama-3.1-8B-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)” Text Summary “Summarize key points from this article on quantum computing advancements in 2024, focusing on hardware breakthroughs.” JSON Extraction “Extract product details as JSON from: Apple iPhone 15 Pro, 256GB, Titanium frame, A17 chip, released 2023.” Multilingual Query “Translate to French and explain: What is recursive neural network architecture used for in NLP tasks?” 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 ](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 8B Instruct Turbo --- [Read the docs ](https://docs.modelslab.com) ### What is Meta Llama 3.1 8B Instruct Turbo? Meta Llama 3.1 8B Instruct Turbo is an 8B parameter LLM optimized for instruction following. It supports 131k context and function calling. Use Meta Llama 3.1 8B Instruct Turbo model for efficient inference. ### How fast is Meta Llama 3.1 8B Instruct Turbo API? Delivers up to 150 tokens per second output speed. Latency averages 1.5-2.5s depending on provider. Ideal for real-time apps via meta llama 3.1 8b instruct turbo api. ### What context length for Meta Llama 3.1 8B Instruct Turbo? Supports 131k input tokens and up to 4k-131k output. Handles long documents without truncation. Key for Meta Llama 3.1 8B Instruct Turbo LLM tasks. ### Best Meta Llama 3.1 8B Instruct Turbo alternative? Compares to Llama 3.1 8B Instruct with added turbo optimizations for speed. Lower cost at $0.02-0.18 per million tokens. Check meta llama 3.1 8b instruct turbo model benchmarks. ### Does it support function calling? Yes, built for tool use and structured generation. Matches open-source benchmarks in reasoning. Access via Meta Llama 3.1 8B Instruct Turbo API. ### Pricing for meta llama 3.1 8b instruct turbo? Ranges $0.02 input / $0.03-0.18 output per million tokens. Cost-efficient for scale. Varies by provider for Meta Llama 3.1 8B Instruct Turbo. Ready to create? --- Start generating with Meta Llama 3.1 8B Instruct Turbo on ModelsLab. [Try Meta Llama 3.1 8B Instruct Turbo](/models/meta/meta-llama-Meta-Llama-3.1-8B-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 - 2026-05-13*