Access the world's best open language models in one place!
OllamaFreeAPI provides free access to leading open-source LLMs including:
- 🦙 LLaMA (Meta)
- 🌪️ Mistral (Mistral AI)
- 🔍 DeepSeek (DeepSeek)
- 🦄 Qwen (Alibaba Cloud)
No payments. No credit cards. Just pure AI power at your fingertips.
# Install or upgrade to the latest version
pip install ollamafreeapi --upgrade- API Reference - Complete API documentation
- Usage Examples - Practical code examples
- Model Catalog - Available models and their capabilities
| Feature | Others | OllamaFreeAPI |
|---|---|---|
| Free Access | ❌ Limited trials | ✅ Always free |
| Model Variety | 3-5 models | Verified endpoints only |
| Reliability | Highly variable | Validated active models |
| Ease of Use | Complex setup | Zero-config |
| Community Support | Paid only | Free & active |
Here are some key statistics about the current state of OllamaFreeAPI:
- Active Models: 16 (Ready to use and tested)
- Model Families: 3 (gemma, llama, qwen)
- Endpoints: 6 highly reliable server nodes
from ollamafreeapi import OllamaFreeAPI
client = OllamaFreeAPI()
# Stream responses in real-time
for chunk in client.stream_chat('What is quantum computing?', model='llama3.2:3b'):
print(chunk, end='', flush=True)from ollamafreeapi import OllamaFreeAPI
client = OllamaFreeAPI()
# Get instant responses
response = client.chat(
model="gpt-oss:20b",
prompt="Explain neural networks like I'm five",
temperature=0.7
)
print(response)llama3.2:3b- Meta's efficient 3.2B parameter modeldeepseek-r1:latest- Strong reasoning capabilities built on Qwengpt-oss:20b- Powerful Gemma-based 20B completion modelmistral:latest- High-performance baseline Mistral model
mistral-nemo:custom- 12.2B open weights language modelbakllava:latest- Vision and language modelsmollm2:135m- Extremely lightweight assistant
Our free API is powered by distributed community nodes:
- Fast response times
- Automatic load balancing and server selection
- Real-time availability checks
# List available models
api.list_models()
# Get model details
api.get_model_info("mistral:latest")
# Generate text
api.chat(model="llama3.2:3b", prompt="Your message")
# Stream responses
for chunk in api.stream_chat(prompt="Hello!", model="llama3:latest"):
print(chunk, end='')# Check server locations
api.get_model_servers("deepseek-r1:latest")
# Generate raw API request
api.generate_api_request(model="llama3.2:3b", prompt="Hello")
# Get random model parameters (useful for LangChain integration)
api.get_llm_params()We welcome contributions! Please see our Contributing Guide for details.
Open-source MIT license - View License
