Quickstart

Make your first free LLM API call in under 5 minutes

Overview

This guide will walk you through making your first API call to a free LLM provider. We’ll use OpenRouter as our example since it offers 20+ free models with a simple OpenAI-compatible API.

1. Choose a Provider

   For this quickstart, we’ll use OpenRouter which offers:

   • 20+ free models including Llama 3.3 70B and Gemma 3
   • OpenAI-compatible API (easy migration)
   • 20 requests/minute, 50 requests/day (1000/day with $10 lifetime top-up)

   Other great options for getting started: Groq (fastest inference), Cerebras (high token limits), or GitHub Models (40+ models).

2. Get Your API Key

   1. Visit OpenRouter.ai
   2. Sign up for a free account
   3. Navigate to API Keys
   4. Create a new API key and copy it

   Keep your API key secure! Never commit it to version control or share it publicly.

3. Install Dependencies

   Install the OpenAI SDK (works with OpenRouter’s compatible endpoint):

   Python

   pip install openai
   
4. Make Your First API Call

   Python

   from openai import OpenAI
   
   # Initialize client with OpenRouter
   client = OpenAI(
       base_url="https://openrouter.ai/api/v1",
       api_key="YOUR_OPENROUTER_API_KEY",
   )
   
   # Make a chat completion request
   response = client.chat.completions.create(
       model="meta-llama/llama-3.3-70b-instruct:free",
       messages=[
           {"role": "user", "content": "Explain quantum computing in simple terms"}
       ],
   )
   
   print(response.choices[0].message.content)
   

Available Free Models on OpenRouter

OpenRouter offers 20+ free models. Here are some popular options:

Llama 3.3 70B

General-purpose powerhouse - great for reasoning and complex tasks

meta-llama/llama-3.3-70b-instruct:free

Gemma 3 27B

Google’s efficient model - balanced performance and speed

google/gemma-3-27b-it:free

Mistral Small 24B

Fast and capable for most tasks

mistralai/mistral-small-3.1-24b-instruct:free

Qwen 3 Coder

Specialized for code generation

qwen/qwen3-coder:free

Try Other Providers

All providers with OpenAI-compatible APIs work similarly - just change the base_url and model name:

Groq (Ultra-Fast Inference)

Python

from openai import OpenAI

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

response = client.chat.completions.create(
    model="llama-3.3-70b-versatile",
    messages=[{"role": "user", "content": "Write a Python function to calculate fibonacci"}],
)

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

Groq Limits: 14,400 requests/day for Llama 3.1 8B, 1,000 requests/day for Llama 3.3 70B

Cerebras (High Token Limits)

Python

from openai import OpenAI

client = OpenAI(
    base_url="https://api.cerebras.ai/v1",
    api_key="YOUR_CEREBRAS_API_KEY",
)

response = client.chat.completions.create(
    model="llama3.3-70b",
    messages=[{"role": "user", "content": "What are the benefits of edge computing?"}],
)

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

Cerebras Limits: 14,400 requests/day, 1,000,000 tokens/day - generous for most projects!

Advanced: Streaming Responses

For real-time applications like chatbots, use streaming to display responses as they’re generated:

Python

from openai import OpenAI

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key="YOUR_OPENROUTER_API_KEY",
)

stream = client.chat.completions.create(
    model="meta-llama/llama-3.3-70b-instruct:free",
    messages=[{"role": "user", "content": "Write a short story about a robot"}],
    stream=True,
)

for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

Environment Variables Setup

Never hardcode API keys! Use environment variables:

.env

OPENROUTER_API_KEY=sk-or-v1-...
GROQ_API_KEY=gsk_...
CEREBRAS_API_KEY=...

Rate Limit Management

Most free providers have rate limits. Handle them gracefully:

Python

from openai import OpenAI, RateLimitError
import time

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key=os.environ.get("OPENROUTER_API_KEY"),
)

def make_request_with_retry(messages, max_retries=3):
    for attempt in range(max_retries):
        try:
            response = client.chat.completions.create(
                model="meta-llama/llama-3.3-70b-instruct:free",
                messages=messages,
            )
            return response
        except RateLimitError:
            if attempt < max_retries - 1:
                wait_time = 2 ** attempt  # Exponential backoff
                print(f"Rate limited. Waiting {wait_time} seconds...")
                time.sleep(wait_time)
            else:
                raise

response = make_request_with_retry(
    [{"role": "user", "content": "Hello!"}]
)

Next Steps

Free Providers

Browse all 13 always-free providers

Trial Credits

Explore providers offering trial credits

Best Practices

Learn tips for optimal API usage

Choosing a Provider

Find the best provider for your needs

Pro Tip: Start with multiple providers and implement fallback logic. If one hits rate limits, automatically switch to another!