--- title: Llama 3.3 70B Turbo — Fast Instruct LLM | ModelsLab description: Access Meta Llama 3.3 70B Instruct Turbo API for function calling and 131K context. Generate precise multilingual responses via simple LLM endpoint. url: https://modelslab-frontend-v2-927501783998.us-east4.run.app/meta-llama-33-70b-instruct-turbo canonical: https://modelslab-frontend-v2-927501783998.us-east4.run.app/meta-llama-33-70b-instruct-turbo type: website component: Seo/ModelPage generated_at: 2026-05-13T08:42:40.140119Z --- Available now on ModelsLab · Language Model Meta Llama 3.3 70B Instruct Turbo Turbocharge Llama Inference --- [Try Meta Llama 3.3 70B Instruct Turbo](/models/meta/meta-llama-Llama-3.3-70B-Instruct-Turbo) [API Documentation](https://docs.modelslab.com) Run Llama 3.3 Turbo Now --- 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](/models/meta/meta-llama-Llama-3.3-70B-Instruct-Turbo). 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 ](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 3.3 70B Instruct Turbo --- [Read the docs ](https://docs.modelslab.com) ### What is Meta Llama 3.3 70B Instruct Turbo? 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. ### How does Meta Llama 3.3 70B Instruct Turbo API compare to Llama 3.1? 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. ### What is the pricing for meta llama 3.3 70b instruct turbo model? 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. ### Does Meta Llama 3.3 70B Instruct Turbo support function calling? Yes, it natively supports function calling and JSON schema. Use standard OpenAI-compatible formats. No vision or audio modalities. ### Is Meta Llama 3.3 70B Instruct Turbo a good alternative? 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. ### What providers host meta llama 3.3 70b instruct turbo api? 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 Turbo](/models/meta/meta-llama-Llama-3.3-70B-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 - 2026-05-13*