Top 14B AI Models Compared (2025) - Which One Runs Best Locally?
AI Summary
Video Summary on AI Model Comparison
- Presenter: Fahad Miza
- Focus: Comparison of 14 billion parameters AI models across different modalities.
- Purpose: Identify the best-suited models for various real-world use cases based on the presenter’s two years of experience.
Models Covered:
- Chat TS (PyTense): Excels in time series understanding.
- Cojito: Advanced reasoning and code generation capabilities.
- Van 2.1: Video generation from text or images.
- Quen 2.5: Suitable for long context and short tasks.
- Gemma 3: Versatile model for text generation and image understanding.
- Quen 2.5 Coder: Suggested for coding tasks.
Model Overview:
- Comparative table of each model’s parameters, context length, and multimodal capabilities provided.
- Models have been categorized based on observed use cases in the real world.
Performance Highlights:
- Chat TS outperforms time series models.
- Cojito excels in specific reasoning tasks.
- Van 2.1 achieves state-of-the-art performance in video generation.
- Gemma 3 showcases strong capabilities in both text and image tasks.
Recommendations:
- Time-Series Analysis: Use Chat TS.
- Advanced Reasoning & Code Generation: Consider Cojito.
- Long Context Tasks: Opt for Quen 2.5.
- Video Generation: Use Van 2.1.
- Text Generation & Image Understanding: Choose Gemma 3.
- For Code Tasks: Quen 2.5 Coder is recommended.
Conclusion:
- Viewers encouraged to try multiple models for flexibility.
- Avoid reliance on a single model due to varying performance across tasks.
Sponsor:
- Featured sponsor: Camel AI, focusing on multi-agent infrastructures for data generation and fault simulation.
- Link to Camel AI website provided in video description.