How to Use Llama API for Free in 2026

How to Use Llama API for Free in 2026

Meta's Llama family of models -- Llama 3.3, Llama 4 Scout, and Llama 4 Maverick -- are among the most capable open-weight large language models available. Because they are open-weight, multiple providers host them and offer free tiers that let you use these models via API without paying anything.

This guide covers every practical method to access Llama models for free in 2026, including hosted API providers, free-tier platforms, and self-hosting options.

Llama Model Lineup (2026)

Before choosing a provider, understand which Llama model fits your use case:

Model	Parameters	Architecture	Context Window	Best For
Llama 3.3 70B	70B	Dense	128K	General purpose, balanced quality/speed
Llama 4 Scout	17B active (109B total)	MoE (16 experts)	512K	Long context, efficient inference
Llama 4 Maverick	17B active (400B total)	MoE (128 experts)	256K	Highest quality, complex reasoning
Llama 3.1 8B	8B	Dense	128K	Fast, lightweight tasks
Llama 3.2 3B	3B	Dense	128K	Edge devices, minimal latency

The MoE (Mixture of Experts) architecture in Llama 4 models means they only activate a fraction of their parameters per request, making them faster and cheaper to run than equivalent dense models.

Method 1: Groq Free Tier (Fastest)

Groq runs Llama models on custom LPU hardware, delivering extremely fast inference. Their free tier is one of the most generous available.

Setup

1. Create an account at console.groq.com.
2. Generate an API key from the dashboard.
3. Install the SDK:

pip install groq

Usage

from groq import Groq

client = Groq(api_key="gsk_your_key_here")

response = client.chat.completions.create(
    model="llama-4-scout-17b-16e-instruct",
    messages=[
        {"role": "system", "content": "You are a helpful coding assistant."},
        {"role": "user", "content": "Write a Python function that finds all prime numbers up to n using the Sieve of Eratosthenes."}
    ],
    temperature=0.3,
    max_tokens=2048,
)

print(response.choices[0].message.content)

Free Tier Limits

Resource	Limit
Requests per minute	30
Requests per day	14,400
Tokens per minute	15,000
Tokens per day	~500,000
Available models	Llama 3.3 70B, Llama 4 Scout, Llama 3.1 8B

Groq's free tier is ideal for development and prototyping. The speed is the main draw -- responses typically arrive in under 2 seconds for medium-length completions.

OpenAI-Compatible API

Groq's API is OpenAI-compatible, so you can use the OpenAI SDK:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.groq.com/openai/v1",
    api_key="gsk_your_key_here",
)

response = client.chat.completions.create(
    model="llama-4-scout-17b-16e-instruct",
    messages=[
        {"role": "user", "content": "Explain Docker networking in simple terms."}
    ],
)

Method 2: Together AI Free Credits

Together AI provides $5 in free credits when you sign up, which goes a long way with Llama models given their low per-token pricing.

Setup

1. Sign up at api.together.xyz.
2. You receive $5 in free credits immediately.
3. Generate an API key.

pip install together

Usage

from together import Together

client = Together(api_key="your-api-key")

response = client.chat.completions.create(
    model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
    messages=[
        {"role": "user", "content": "Design a database schema for a project management app with users, projects, tasks, and comments."}
    ],
    max_tokens=4096,
    temperature=0.5,
)

print(response.choices[0].message.content)

How Far Does $5 Go?

Model	Price per 1M Input Tokens	Price per 1M Output Tokens	Approx. Requests with $5
Llama 4 Scout	$0.10	$0.30	~10,000+
Llama 4 Maverick	$0.27	$0.85	~4,000+
Llama 3.3 70B	$0.54	$0.54	~3,000+
Llama 3.1 8B	$0.10	$0.10	~25,000+

Together AI also offers dedicated free models that do not consume credits at all. Check their models page for the current list.

Method 3: Fireworks AI Free Tier

Fireworks AI offers a free tier with 1 million free tokens per month for select models.

Setup

1. Sign up at fireworks.ai.
2. Generate an API key.

pip install fireworks-ai

Usage

from openai import OpenAI

client = OpenAI(
    base_url="https://api.fireworks.ai/inference/v1",
    api_key="fw_your_key_here",
)

response = client.chat.completions.create(
    model="accounts/fireworks/models/llama-v3p3-70b-instruct",
    messages=[
        {"role": "user", "content": "Write a REST API endpoint in Express.js that handles file uploads with validation."}
    ],
    max_tokens=2048,
)

print(response.choices[0].message.content)

Free Tier Details

Feature	Limit
Free tokens per month	1,000,000
Rate limit	3 RPM (free), 600 RPM (paid)
Available models	Llama 3.3 70B, Llama 4 Scout, Llama 3.1 8B
API format	OpenAI-compatible

Method 4: Hugging Face Inference API

Hugging Face hosts Llama models and provides a free inference API for testing.

Setup

1. Create an account at huggingface.co.
2. Generate a token at Settings > Access Tokens.

Usage

from huggingface_hub import InferenceClient

client = InferenceClient(
    model="meta-llama/Llama-4-Scout-17B-16E-Instruct",
    token="hf_your_token_here",
)

response = client.chat.completions.create(
    messages=[
        {"role": "user", "content": "Explain the difference between REST and GraphQL APIs with examples."}
    ],
    max_tokens=2048,
)

print(response.choices[0].message.content)

Free Tier Limits

Feature	Limit
Rate limit	~5-10 requests per minute
Model loading	May have cold start delay
Concurrency	1 concurrent request
Token limit	Varies by model

The Hugging Face free tier is best for experimentation and testing. For sustained development, Groq or Together AI are more reliable.

Method 5: OpenRouter Free Models

OpenRouter aggregates models from multiple providers and offers some for free.

from openai import OpenAI

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key="sk-or-v1-your-key",
)

response = client.chat.completions.create(
    model="meta-llama/llama-4-scout:free",
    messages=[
        {"role": "user", "content": "Create a Python script that scrapes weather data and saves it to a CSV file."}
    ],
)

Free models on OpenRouter are rate-limited and queued behind paid requests, but they work well for development and testing.

Method 6: Self-Hosting with Ollama

If you have a local machine with sufficient hardware, you can run Llama models locally for completely free, unlimited access.

Hardware Requirements

Model	Minimum VRAM	Recommended VRAM
Llama 3.2 3B	4GB	6GB
Llama 3.1 8B	6GB	10GB
Llama 3.3 70B (quantized)	24GB	48GB
Llama 4 Scout (quantized)	24GB	48GB

Setup with Ollama

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull a Llama model
ollama pull llama3.3:70b

# Run the model
ollama run llama3.3:70b

Use the Local API

Ollama exposes an OpenAI-compatible API at http://localhost:11434:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:11434/v1",
    api_key="ollama",  # Any string works
)

response = client.chat.completions.create(
    model="llama3.3:70b",
    messages=[
        {"role": "user", "content": "Write unit tests for a React component that renders a sortable table."}
    ],
)

print(response.choices[0].message.content)

The main advantage of self-hosting is zero rate limits and complete privacy. The main disadvantage is hardware cost and setup time.

Comparison: All Free Methods

Method	Speed	Rate Limits	Setup Effort	Best For
Groq	Very fast	30 RPM	Easy	Fast prototyping
Together AI	Fast	Credit-based ($5 free)	Easy	Extended development
Fireworks AI	Fast	1M tokens/month	Easy	Medium-volume projects
Hugging Face	Moderate	~5-10 RPM	Easy	Quick experiments
OpenRouter	Moderate	Queued	Easy	Multi-model access
Ollama (local)	Depends on hardware	None	Moderate	Privacy, unlimited use

Tips for Maximizing Free Access

1. Stack multiple providers. Sign up for Groq, Together AI, and Fireworks AI. Use Groq for speed, Together AI when you need Maverick quality, and Fireworks as a fallback.

2. Use smaller models when possible. Llama 3.1 8B handles many tasks adequately and has higher rate limits on free tiers.

3. Cache responses. If you make repeated similar queries, cache the results locally to avoid wasting your free quota.

4. Use system prompts efficiently. A good system prompt reduces the number of follow-up messages needed.

5. Monitor your usage. Most providers show usage in their dashboard. Check regularly to avoid hitting limits unexpectedly.

Conclusion

The open-weight nature of Llama models means free access is widely available and will likely remain so. Whether you prefer the speed of Groq, the generous credits from Together AI, or the privacy of local hosting with Ollama, there is a free option that fits your workflow.

If your projects need AI media generation alongside LLM capabilities -- images, videos, talking avatars, or audio -- check out Hypereal AI. Hypereal provides a unified API for the latest generative models with pay-as-you-go pricing, making it easy to add visual and audio AI to any application.