Qwen3-14B or Gemma3-12B? Hottest Open-Source LLMs!
AI Summary
Video Summary: Comparison of Quen 3 (14 Billion) and Jamma 3 (12 Billion)
- Introduction
- Host: Fahad Miraza
- Focus: Comparison of Quen 3 and Jamma 3 language models.
- Model Overview
- Quen 3: 14 billion parameters, text-focused.
- Jamma 3: 12 billion parameters, multimodal (text and images).
- Both models target enterprise applications, not hobbyist use.
- Architectural Comparison
- Quen 3: 40 layers, supports 131072 token context length.
- Jamma 3: 128K context window.
- Ratings: Equal performance despite parameter differences.
- Data and Training Comparison
- Jamma 3 uses diverse datasets including images, leveraging Google’s TPU infrastructure.
- Quen 3’s specifics are less disclosed.
- Features and Deployment
- Jamma 3 excels in multimodality and language coverage.
- Quen 3 offers advanced code agent tools and seamless deployment.
- Hybrid modes discussed for cost and latency optimization.
- Benchmarking Performance
- MMLU, math, and coding benchmarks favor Quen 3.
- Quen 3 scores:
- MMLU: 80.4 vs. Jamma 3: 74.5
- AGI Eval: 65.8 vs. Jamma 3: 57.4
- Math: 65.6 vs. Jamma 3: 43.3
- Jamma 3 shows strong results in grade school math: 71.
- General Reasoning and Multilingual Abilities
- Quen 3 leads in common sense reasoning benchmarks (Hela Swag).
- Multilingual benchmarks show parity with slight advantage to Quen 3.
- Conclusion
- Recommended uses:
- Jamma 3 for image and text tasks with cultural context.
- Quen 3 for non-reasoning tasks, math, and coding.
- Both models offer strong performance for their intended uses.