> ## Documentation Index
> Fetch the complete documentation index at: https://docs.aieev.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Llama3.3-70B
> Meta's multilingual 70B instruct model for general-purpose chat and text tasks.
**Model ID:** `meta-llama/Meta-Llama-3.3-70B-Instruct` | **Endpoint ID:** `llama3-3-70b` | **Parameters:** 70B | **Released:** 2024-12-06
| Input Price | Output Price | Context | Modality |
| :----------: | :----------: | :-----: | :---------: |
| \$0.100 / 1M | \$0.320 / 1M | 131,072 | text → text |
## Overview
Meta Llama 3.3 70B Instruct is a dense 70B-parameter model fine-tuned for instruction following and conversational use. It delivers strong multilingual instruction following across a wide range of languages and supports a 131,072-token context window for long conversations and documents. Built on the proven open-source Llama family, it is well suited for general-purpose chat, summarization, and coding assistance. The model focuses on reliable text generation rather than reasoning traces or multimodal input.
Try the model in the playground.
Deploy with AIR Container.
Connect with the OpenAI SDK as-is.
## Pricing
| Currency | Input (1M tokens) | Output (1M tokens) |
| :------- | ----------------: | -----------------: |
| USD | \$0.100 | \$0.320 |
| KRW | ₩150 | ₩480 |
## Supported Features
All features are available through the [OpenAI Compatible API](/docs/air-api/openai-compatible-api).
| Feature | Supported | Notes |
| :-------------------------------------------------------------------------------- | :-------: | :----------------------------- |
| [Streaming](/docs/air-api/openai-compatible-api#streaming) | ✅ | SSE, `include_usage` supported |
| [Tool Calling](/docs/air-api/openai-compatible-api#tool-calling-function-calling) | ✅ | Streaming tool calls supported |
| [Vision](/docs/air-api/openai-compatible-api#vision-image-input) | ❌ | Not supported |
| [Reasoning](/docs/air-api/openai-compatible-api#reasoning) | ❌ | Not supported |
| [JSON Mode](/docs/air-api/openai-compatible-api#json-mode-structured-outputs) | ✅ | `response_format: json_object` |
## Key Features
* Strong multilingual instruction following across a wide range of languages
* 128K-token context window for long conversations and documents
* Tool calling and JSON mode support for structured, agentic workflows
* Built on the proven open-source Llama family
## Use Cases
### General Q\&A & Conversation
Handle general-purpose Q\&A and multi-turn conversation with reliable instruction following.
```text Input theme={null}
Explain how transformers work in simple terms and give a real-world analogy.
```
```text Output theme={null}
Transformers process text by modeling relationships between tokens in parallel
rather than strictly one by one...
A simple analogy is a group discussion where each person listens to everyone
else to understand the full context before responding.
```
### Code Generation & Assistance
Generate and explain code for practical development tasks.
```text Input theme={null}
Write a Python function to check if a number is prime and explain the logic.
```
```python Output theme={null}
def is_prime(n):
if n <= 1:
return False
for i in range(2, int(n ** 0.5) + 1):
if n % i == 0:
return False
return True
# This function checks divisibility only up to the square root of n,
# which improves efficiency.
```
### Summarization & Analysis
Summarize long documents and analyze text content across multiple languages.
### Multilingual Conversation
Carry on natural conversations and follow instructions across many languages with consistent quality.
## Parameters
| Parameter | Type | Required | Default | Description |
| -------------------- | ---------------- | -------- | ------- | -------------------------------------------------------------- |
| `messages` | array | Required | - | List of input messages for chat-based generation. |
| `max_tokens` | integer | Optional | - | Maximum output tokens to generate |
| `temperature` | number | Optional | 1 | Sampling temperature (0.0-2.0) |
| `top_p` | number | Optional | 1 | Nucleus sampling threshold |
| `top_k` | integer | Optional | - | Limit sampling to the top-k most likely tokens |
| `min_p` | number | Optional | - | Minimum probability threshold for token sampling |
| `frequency_penalty` | number | Optional | 0 | Penalty for token frequency |
| `presence_penalty` | number | Optional | 0 | Penalty for token presence |
| `repetition_penalty` | number | Optional | - | Penalty for repeated token generation |
| `stop` | string \| array | Optional | - | Stop sequence(s) that end generation |
| `seed` | integer | Optional | - | Random seed for reproducible sampling |
| `stream` | boolean | Optional | false | Enable streaming responses |
| `tools` | array | Optional | - | Tool calling function definitions |
| `tool_choice` | string \| object | Optional | auto | How tools are invoked (`auto`, `none`, or a specific function) |
| `response_format` | object | Optional | - | JSON mode (`{"type": "json_object"}`) |
## Model Details
| Property | Value |
| ------------------ | ---------------------------- |
| Context Length | 131,072 |
| Max Output Length | 131,072 |
| Input Modalities | text |
| Output Modalities | text |
| Supported Features | tools, streaming, json\_mode |
## Quick Start
Generate an API key from your [AirCloud account](https://aieev.com).
Replace `YOUR_API_KEY` with your actual key and choose your preferred language (Python, cURL, Node.js).
```python Python theme={null}
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://ap-1.aieev.cloud/endpoints/llama3-3-70b/v1"
)
response = client.chat.completions.create(
model="meta-llama/Meta-Llama-3.3-70B-Instruct",
messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)
```
```bash cURL theme={null}
curl https://ap-1.aieev.cloud/endpoints/llama3-3-70b/v1/chat/completions \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "meta-llama/Meta-Llama-3.3-70B-Instruct",
"messages": [{"role": "user", "content": "Hello!"}]
}'
```
```javascript Node.js theme={null}
import OpenAI from "openai";
const client = new OpenAI({
apiKey: "YOUR_API_KEY",
baseURL: "https://ap-1.aieev.cloud/endpoints/llama3-3-70b/v1",
});
const response = await client.chat.completions.create({
model: "meta-llama/Meta-Llama-3.3-70B-Instruct",
messages: [{ role: "user", content: "Hello!" }],
});
console.log(response.choices[0].message.content);
```
## Tags
`open-source` `conversational` `70B` `multilingual`