LLaMA 3
4.0600000000000005 (0 reviews)
ProductivityVisit Website
4.0600000000000005
Avg RatingMost advanced open-weight model, performs close to GPT-4 for many tasks
FreeOpen SourceMetaSocials
Tool Type
Open Source
Model Used
LLaMA 3
Integrations
Self-hosted deploymentCustom API endpointsHugging Face
Detailed Ratings
accuracy
4.2
ease Of Use
2.8
speed
4.5
creativity
4
value For Money
4.8
Features
- Open-source model
- High performance
- Supports text and image understanding (multimodal)
- Can handle very long conversations/documents
- Available in different sizes for different needs
- Strong at reasoning and coding
- Multilingual capabilities
Use Cases
- Custom AI applications
- Agent development
- Automation workflows
- Research and development
- Specialized tasks
- Enterprise deployments
Reviews
Pros
- You can run it yourself for more control and privacy
- Highly customizable to your specific needs
- Good for multiple languages
- Now accessible via Meta's Llama API and cloud partners, making it easier to use
- Active community and ongoing development
- Designed for natural, social, and personalized conversation styles
Cons
- Requires technical expertise to deploy and use
- No managed service or API access
- Limited ecosystem integrations
- Resource-intensive to run locally
- No official support or documentation
Areas for Improvement
- Improve ease of deployment and setup
- Add more managed service options
- Enhance ecosystem integrations
- Provide better documentation and support
Pricing
Open Source
Free- Free to use
- Self-hosted
- Customizable
- No restrictions
Capabilities
✅
Vision input
Natively supported in Llama 4 and 3.2 Vision models
🟡
Voice
Enabled through integrations/third-party applications, not native to the core model directly
✅
API access
Available via Meta's Llama API and cloud providers like AWS Bedrock
✅
File upload
Supported for image and text files via multimodal models and API integrations
✅
Fine-tuning
Full fine-tuning support with various tools
✅
Memory
Enhanced by very long context windows (e.g., Llama 4 Scout's 10M tokens)
🟡
Mobile app
Can be deployed on-device (e.g., iOS via PyTorch ExecuTorch) for specific apps
🟡
Code execution
Strong code generation; custom implementation often needed for direct execution
🟡
Real-time data
Can be integrated with real-time data sources via RAG/tool-use, not inherent
✅
Multi-modal
Native text, image, and increasingly video understanding (Llama 4)
Performance
Max Tokens
10M tokens
Response Time
1-3 seconds
Uptime
Self-hosted
Cost per 1K Tokens
Free
Rate Limits
Free: No limits
Paid: N/A
