Molmo 2 - Ai2 open source multimodal video image understanding model series

What's Molmo 2?

Molmo 2 is an open source multimodal model released by Allen Institute for AI (Ai2) to improve video and multi-image understanding. Three variants are included, Molmo 2 (8B), Molmo 2 (4B), and Molmo 2-O (7B), which are suitable for different scenarios and requirements. Among them, Molmo 2 (8B) performs best in video localization and Q&A, Molmo 2 (4B) optimizes efficiency, and Molmo 2-O (7B) provides a fully open end-to-end model flow.Molmo 2 outperforms its predecessor model in several key benchmarks, and outperforms strong rivals such as Gemini 3 Pro in video tracking. Molmo 2 also excels in the amount of training data, using only 9.19 million videos, far fewer than other models, demonstrating its ability to utilize data efficiently.Molmo 2 supports single-image and multi-image inputs, as well as video clips of different lengths, and is capable of performing a wide range of tasks such as video localization, tracking, and Q&A.

Features of Molmo 2

• Powerful video comprehension: Outperforms its predecessor model as well as several industry-leading models on tasks such as video localization, tracking, and Q&A, such as Gemini 3 Pro.
• Multi-image and single-image support: It not only supports single image input, but also handles multiple image inputs and video clips of different lengths, making it suitable for a wide range of complex scenarios.
• Efficient data utilization: The amount of training data is only 9.19 million videos, which is much less than other models such as Meta's PerceptionLM (72.5 million videos), demonstrating an efficient training efficiency.
• Flexible model variants: Includes Molmo 2 (8B), Molmo 2 (4B) and Molmo 2-O (7B) variants, each meeting different performance and efficiency needs.
• Openness and scalability: Provides a fully open end-to-end modeling process suitable for researchers who need full control of their modeling stack, and in the future will also be available via an API.
• Rich application scenarios: It can be used in a variety of fields such as video analytics, robot vision, and assistive technology, and supports video summarization, object tracking, and dense caption generation.
• easy-to-use: Users can find out more about Ai2 in the Ai2 Playground to get a quick taste of the model's capabilities, upload videos or images and run multiple tasks to see the model's reasoning process.

Core Benefits of Molmo 2

• Excellent video comprehension: excels at tasks such as video localization, tracking, and Q&A, outperforming several industry-leading models, such as the Gemini 3 Pro, to become the leader in video understanding.
• Efficient Training and Data Utilization: Only 9.19 million videos were used for training, far fewer than other models (e.g., Meta's PerceptionLM uses 72.5 million), demonstrating efficient training efficiency and data utilization.
• Multi-modal input supportIt supports single-image, multi-image and video clip inputs of different lengths, and is able to flexibly handle a variety of complex scenarios to meet diversified needs.
• Flexible model variantsMolmo 2 (8B), Molmo 2 (4B) and Molmo 2-O (7B) variants are available to meet the different needs for high performance, high efficiency and fully open control, respectively.
• Openness and scalability: Built on Qwen 3 and Olmo, it provides a fully open end-to-end modeling process for easy customization and extension by researchers.

What is the official website for Molmo 2

• Project website:: https://allenai.org/blog/molmo2
• GitHub repository:: https://github.com/allenai/molmo2
• HuggingFace Model Library:: https://huggingface.co/collections/allenai/molmo2
• Technical Papers:: https://www.datocms-assets.com/64837/1765901660-molmo_v2_2026-techreport-3.pdf

Who Molmo 2 is for

• research worker: Scholars and researchers in multimodal AI research can conduct experiments and explorations in video comprehension, image analysis and multimodal reasoning with Molmo 2, advancing research progress in related fields.
• developers: Software developers looking to integrate advanced video and image processing capabilities into their projects can use Molmo 2's API and open source code to quickly implement video analysis, object tracking, and more.
• educator: In the field of AI education, Molmo 2 can be used as a teaching tool to help students understand and practice the application of multimodal modeling to enhance teaching effectiveness.
• industry expert: Professionals in the fields of traffic monitoring, industrial automation, medical imaging, etc. can use the powerful features of Molmo 2 to improve the efficiency and quality of their work and decision-making.
• technology enthusiast: Individuals interested in artificial intelligence and multimodal technologies can learn and practice with Molmo 2's open source resources to explore the possibilities of the technology.