Aria: First Open Multimodal Native MoE Model
Aria processes text, images, video, and code all at once, without needing separate setups for each type, demonstrating the advantages of a multimodal native model.
A multimodal native model refers to a single model with strong understanding capabilities across multiple input modalities (e.g., text, code, image, video) that matches or exceeds the modality-specialized models of similar capacities.
We compared Aria against the best open and closed multimodal native models across established benchmarks, highlighting the following key observations:
Multimodal data is often complex, involving long sequences that combine visuals and text, like videos with subtitles or long documents. For a model to be effective in real-world applications, it must be capable of understanding and processing such data efficiently.
Aria excels in this area, demonstrating superior long multimodal input understanding. It outperforms larger open models, proving its efficiency and effectiveness despite its size. When compared to proprietary models, Aria surpasses GPT-4o mini in long video understanding and outperforms Gemini-1.5-Flash in long document understanding. This makes Aria a preferred choice for processing extensive multimodal data in a compute-and-time-efficient manner, delivering faster and more accurate results in real-world scenarios.
