The video compares three open-source large language models—OpenAI’s GPT OSS, Alibaba Cloud’s Quen 3, and Deepseek’s V3—highlighting their architectures, training methods, and unique approaches to extending context length, such as yarn scaling and novel attention mechanisms. It emphasizes that beyond benchmark scores, understanding each model’s distinct engineering innovations, training data, and post-training techniques is crucial to appreciating their performance and capabilities.
The video provides an in-depth comparison of three prominent open-source large language models (LLMs): OpenAI’s GPT OSS, Alibaba Cloud’s Quen 3, and Deepseek’s V3. GPT OSS marks OpenAI’s return to releasing open-weight models since GPT-2, featuring a mixture of experts architecture with two sizes—120 billion and 20 billion parameters. It incorporates modern transformer features such as grouped query attention (GQA), swiggloo activations, rotary positional embeddings (rope), and RMS normalization. Notably, GPT OSS supports an exceptionally long context window of 131,000 tokens, achieved by applying yarn scaling during pre-training, and is released in a quantized format optimized for deployment on modest hardware.
Quen 3, launched by Alibaba Cloud, offers both dense and mixture of experts models ranging from 6 billion to 235 billion parameters. Architecturally, it shares many features with GPT OSS, including GQA, swiggloo, rope, and RMS norm, but introduces innovations like QK norm to stabilize attention scores at scale. Quen 3 was trained on an extensive dataset of 36 trillion tokens, including synthetic data generated by earlier Quen models. Its training occurred in three stages, culminating in a long context stage with sequences over 32,000 tokens, supported by algorithmic optimizations such as ABF, yarn scaling, and dual chunk attention. Quen 3 also features a unique four-step post-training pipeline that enhances reasoning capabilities and allows users to toggle between reasoning and non-reasoning modes.
Deepseek’s V3 model, released earlier, is a massive mixture of experts model with 671 billion parameters, activating 37 billion per token. It stands out for its use of MLA (Memory Latent Attention), a novel attention mechanism that compresses key-value caches to reduce memory usage and improve performance, especially for long contexts. V3 was trained natively in 8-bit precision to reduce costs and recently updated to V3.1, which adds a two-phase long context training approach and a hybrid thinking mode for flexible reasoning. Deepseek’s approach to extending context length involves staged fine-tuning to reach up to 128,000 tokens, contrasting with GPT OSS’s native long context training and Quen’s inference-time scaling.
A key theme across these models is their differing approaches to extending context length using yarn scaling and rope positional embeddings. GPT OSS integrates yarn scaling directly during pre-training, enabling native support for extremely long contexts. Deepseek adopts a stepwise fine-tuning strategy to gradually increase context length, while Quen 3 applies yarn scaling at inference time without additional retraining to push beyond its 32,000-token training limit. Despite similarities in core components like attention mechanisms and activation functions, each model employs distinct techniques and optimizations, reflecting empirical rather than theoretical justifications for their design choices.
The video concludes by emphasizing the importance of looking beyond benchmark scores and headline statistics when evaluating these models. Differences in training data, post-training methods, and architectural nuances play critical roles in their performance and capabilities. Reinforcement learning is a common element in post-training across all three models, often requiring surprisingly small datasets to enhance reasoning and alignment. The discussion highlights the complexity and innovation in the open-source LLM landscape, encouraging viewers to explore these models with an understanding of their unique engineering approaches rather than focusing solely on raw metrics.