Llama Model Family
Developed by Meta, Llama (Large Language Model Meta AI) is a family of state-of-the-art open-weight models designed for efficiency and performance. The latest versions feature Mixture-of-Experts (MoE) architectures, enabling cost-effective inference by dynamically activating subsets of parameters. With support for multimodal inputs (text + images) and extended context windows (up to 10M tokens), Llama excels in tasks like code generation, multilingual understanding, and long-form reasoning. The models support FP8 quantization and batch inference, ensuring low-latency, high-throughput performance for production workloads. With permissive licensing and robust tooling (e.g., Llama Guard for safety), Llama is ideal for developers seeking powerful, customizable AI with minimal overhead.
Featured Model: meta-llama/Llama-4-Scout-17B-16E-Instruct
The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding. Llama 4 Scout, a 17 billion parameter model with 16 experts
Price per 1M input tokens
$0.08
Price per 1M output tokens
$0.30
Release Date
04/5/2025
Context Size
327,680
Quantization
bfloat16
# Assume openai>=1.0.0
from openai import OpenAI
# Create an OpenAI client with your deepinfra token and endpoint
openai = OpenAI(
api_key="$DEEPINFRA_TOKEN",
base_url="https://api.deepinfra.com/v1/openai",
)
chat_completion = openai.chat.completions.create(
model="meta-llama/Llama-4-Scout-17B-16E-Instruct",
messages=[{"role": "user", "content": "Hello"}],
)
print(chat_completion.choices[0].message.content)
print(chat_completion.usage.prompt_tokens, chat_completion.usage.completion_tokens)
# Hello! It's nice to meet you. Is there something I can help you with, or would you like to chat?
# 11 25
Featured Model: meta-llama/Llama-4-Maverick-17B-128E-Instruct-Turbo
The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding. Llama 4 Maverick, a 17 billion parameter model with 128 experts
Price per 1M input tokens
$0.50
Price per 1M output tokens
$0.50
Release Date
05/16/2025
Context Size
8,192
Quantization
fp8
# Assume openai>=1.0.0
from openai import OpenAI
# Create an OpenAI client with your deepinfra token and endpoint
openai = OpenAI(
api_key="$DEEPINFRA_TOKEN",
base_url="https://api.deepinfra.com/v1/openai",
)
chat_completion = openai.chat.completions.create(
model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-Turbo",
messages=[{"role": "user", "content": "Hello"}],
)
print(chat_completion.choices[0].message.content)
print(chat_completion.usage.prompt_tokens, chat_completion.usage.completion_tokens)
# Hello! It's nice to meet you. Is there something I can help you with, or would you like to chat?
# 11 25
Available Llama 4 Models
The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences.
| Model | Context | $ per 1M input tokens | $ per 1M output tokens | Actions |
|---|---|---|---|---|
| Llama-4-Scout-17B-16E | 320k | $0.08 | $0.30 | |
| Llama-4-Maverick-17B-128E | 1024k | $0.15 | $0.60 | |
| Llama-4-Maverick-17B-128E-Turbo | 8k | $0.50 | $0.50 | |
| Llama-Guard-4-12B | 160k | $0.05 | $0.05 |
Available Llama 3 Models
Meta Llama 3 are a collection of pretrained and instruction tuned generative text models in 8B, 70B and 405B sizes.
| Model | Context | $ per 1M input tokens | $ per 1M output tokens | Actions |
|---|---|---|---|---|
| Llama-3.3-70B-Instruct | 128k | $0.23 | $0.40 | |
| Llama-3.3-70B-Instruct-Turbo | 128k | $0.038 | $0.12 | |
| Llama-3.2-11B-Vision-Instruct | 128k | $0.049 | $0.049 | |
| Llama-3.2-3B-Instruct | 128k | $0.003 | $0.006 | |
| Llama-3.2-1B-Instruct | 128k | $0.005 | $0.01 | |
| Meta-Llama-3.1-405B-Instruct | 32k | $0.80 | $0.80 | |
| Meta-Llama-3.1-70B-Instruct | 128k | $0.23 | $0.40 | |
| Meta-Llama-3.1-70B-Instruct-Turbo | 128k | $0.10 | $0.28 | |
| Meta-Llama-3.1-8B-Instruct | 128k | $0.03 | $0.05 | |
| Meta-Llama-3.1-8B-Instruct-Turbo | 128k | $0.015 | $0.02 | |
| Meta-Llama-3-70B-Instruct | 8k | $0.30 | $0.40 | |
| Meta-Llama-3-8B-Instruct | 8k | $0.03 | $0.06 |
FAQ
What is LLaMA AI?
What tasks are LLaMA models best suited for?
Are the LLaMA models on DeepInfra optimized for low latency?
How large are the context windows for LLaMA models?
Can I use LLaMA models for multimodal (image + text) applications?
How do I integrate Llama models into my application?
What are the pricing details for using Llama models on DeepInfra?
- Input Tokens: between $0.003 and $0.80 per million
- Output Tokens: between $0.006 and $0.80 per million
How do I get started using Llama on DeepInfra?
- Get your API key
- Test models directly from the browser, cURL, or SDKs
- Review pricing on your usage dashboard
