Apple has introduced SlowFast-LLaVA-1.5, a new family of video large language models (Video-LLMs) designed to efficiently understand long-form video. In its research paper, Apple explains that most existing video LLMs struggle with high computational costs and excessive token usage when analyzing extended video content, which limits their ability to scale. SlowFast-LLaVA-1.5 addresses this by introducing a token-efficient framework that reduces the number of tokens needed to represent video while maintaining accuracy.
Token efficiency is critical because every frame in a video must be converted into tokens before an LLM can process it. With long-form video, the number of tokens quickly becomes unmanageable, driving up costs and slowing performance. Apple’s approach compresses video data so that fewer tokens are used without losing important context. By combining this with a dual-pathway architecture, where a “slow” pathway captures long-term patterns and a “fast” pathway focuses on short-term details, the model can balance comprehension with efficiency. This allows it to track both overarching storylines and fine-grained actions across extended sequences.
The system is also highly scalable, meaning it can expand to handle much longer videos and larger datasets without overwhelming compute resources. Traditional models become impractical as input length increases, but Apple’s design ensures that scaling from short clips to multi-hour footage remains feasible. This makes SlowFast-LLaVA-1.5 suitable for tasks such as video question answering, temporal reasoning, summarization, and content retrieval across long video archives.
In benchmark tests, Apple reports that the model achieves strong results on datasets such as Video-MME and LongVideoBench, showing both improved efficiency and comprehension compared to prior approaches. The research also introduces multiple model sizes, including 1.5B, 7B, and 13B parameter versions, which are instruction-tuned to follow natural language prompts. This allows the system to generate detailed responses about complex video content, making it applicable for educational video analysis, meeting summarization, and accessibility tools that create captions or searchable transcripts.
Apple emphasizes that the token-efficient and scalable design is not just about research novelty but about practicality. By lowering computational requirements while expanding capability, the model paves the way for integrating long-form video understanding into real-world products. As video continues to dominate in entertainment, education, and professional communication, Apple’s long-form video LLM represents a significant step toward making advanced multimodal AI both usable and accessible.
Check out the full paper here.
