The Best 68 Video-to-Text Tools in 2025

The LLaVA-Video model is a 7B-parameter multimodal model based on the Qwen2 language model, specializing in video understanding tasks and supporting 64-frame video input.

LLaVA-NeXT-Video is an open-source multimodal chatbot optimized through mixed training on video and image data, possessing excellent video understanding capabilities.

InternVideo2.5 is a video multimodal large language model enhanced by Long and Rich Context (LRC) modeling, built upon InternVL2.5. It significantly improves existing MLLM models by enhancing the ability to perceive fine-grained details and capture long-term temporal structures.

CogVLM2-Caption is a video caption generation model used to generate training data for the CogVideoX model.

SpaceTime GPT is a video description generation model capable of spatial and temporal reasoning, analyzing video frames and generating sentences describing video events.

Video-R1-7B is a multimodal large language model optimized based on Qwen2.5-VL-7B-Instruct, focusing on video reasoning tasks, capable of understanding video content and answering related questions.

InternVideo2.5 is a video multimodal large language model (MLLM) built upon InternVL2.5, enhanced with Long and Rich Context (LRC) modeling, capable of perceiving fine-grained details and capturing long-term temporal structures.

VideoLLM-online is a multimodal large language model based on Llama-3-8B-Instruct, focusing on online video understanding and video-text generation tasks.

VideoChat-R1_7B is a multimodal video understanding model based on Qwen2.5-VL-7B-Instruct, capable of processing video and text inputs and generating text outputs.

A camera motion analysis model fine-tuned based on Qwen2.5-VL-7B-Instruct, focusing on camera motion classification in videos and video-text retrieval tasks

PAVE is a model focused on repairing and adapting video large language models, aiming to enhance the conversion capability between video and text.

LongVU is a spatio-temporal adaptive compression technology for long video language understanding, designed to efficiently process long video content.

VideoChat-Flash-2B is a multimodal model built upon UMT-L (300M) and Qwen2.5-1.5B, supporting video-to-text tasks with only 16 tokens per frame and extending the context window to 128k.

Vamba is a hybrid Mamba-Transformer architecture that achieves efficient long video understanding through cross-attention layers and Mamba-2 modules.

VideoChat-R1-thinking_7B is a multimodal model based on Qwen2.5-VL-7B-Instruct, focusing on video-text-to-text tasks.

VideoChat-Flash-7B is a multimodal model built upon UMT-L (300M) and Qwen2-7B, using only 16 tokens per frame and supporting input sequences of up to approximately 10,000 frames.

Tarsier-7b is an open-source large-scale video-language model from the Tarsier series, specializing in generating high-quality video descriptions with excellent general video understanding capabilities.

InternVideo2 is a multimodal video understanding model with 6B parameters, focusing on video content analysis and comprehension tasks.

InternVideo2-Chat-8B is a video understanding model that combines a large language model (LLM) with video BLIP, built through a progressive learning scheme, capable of video semantic understanding and human-computer interaction.

LLaVA-Video-7B-Qwen2-TPO is a video understanding model based on LLaVA-Video-7B-Qwen2 with temporal preference optimization, demonstrating excellent performance across multiple benchmarks.

LongVU is a spatio-temporal adaptive compression technology designed for long video language understanding, aiming to efficiently process long video content and enhance language comprehension.

VideoBLIP is an enhanced version of BLIP-2 capable of processing video data, using OPT-2.7b as the language model backbone.

xGen-MM-Vid (BLIP-3-Video) is an efficient compact vision-language model equipped with an explicit temporal encoder, specifically designed for video content understanding.

VideoChat2-HD-hf is a multimodal video understanding model based on Mistral-7B, focusing on video-to-text conversion tasks.

SkyCaptioner-V1 is a model specifically designed for generating high-quality structured descriptions of video data. By integrating specialized sub-expert models, multimodal large language models, and manual annotations, it addresses the limitations of general description models in capturing professional film details.

An open-source video caption generator fine-tuned on GPT4V-annotated data, supporting videos of various durations, aspect ratios, and resolutions

A video multimodal large language model enhanced by Long and Rich Context (LRC) modeling, improving existing MLLMs by enhancing the perception of fine-grained details and capturing long-term temporal structures

LongVU is a multimodal model based on Qwen2-7B, focusing on long video language understanding tasks and employing spatio-temporal adaptive compression technology.

LongVA-7B-TPO is a video-text model derived from LongVA-7B through temporal preference optimization, excelling in long video understanding tasks.

LLaVAction is a multimodal large language model for action recognition, based on the Qwen2 language model, trained on the EPIC-KITCHENS-100-MQA dataset.

Llama 2 is a series of open-source large language models developed by Meta, supporting various natural language processing tasks.

VideoMind is a multimodal agent framework that enhances video reasoning capabilities by simulating human thought processes (such as task decomposition, moment localization & verification, and answer synthesis).

InternVideo2-Chat-8B-HD is a video understanding model that combines a large language model and VideoBLIP. It is constructed through a progressive learning scheme and can handle high-definition video input.

A video multimodal large language model using a slow-fast architecture, balancing temporal resolution and spatial details, supporting 64-frame video understanding

TimeZero is a reasoning-guided large vision-language model (LVLM) specifically designed for temporal video grounding (TVG) tasks. It identifies temporal segments in videos corresponding to natural language queries through reinforcement learning methods.

VideoLLaMA2.1 is an upgraded version of VideoLLaMA2, focusing on enhancing spatiotemporal modeling and audio understanding capabilities in large video-language models.

Kangaroo is a powerful multimodal large language model specifically designed for long video understanding, supporting bilingual dialogue (Chinese-English) and long video inputs.

LLaVAction is a multimodal large language model evaluation and training framework for action recognition, based on the Qwen2 language model architecture, supporting first-person perspective video understanding.

TimeZero is a reasoning-guided large-scale vision-language model (LVLM) specifically designed for temporal video grounding (TVG) tasks, achieving dynamic video-language relationship analysis through reinforcement learning methods.

TinyLLaVA-Video-R1 is a small-scale video reasoning model based on the traceable training model TinyLLaVA-Video. It significantly enhances reasoning and thinking abilities through reinforcement learning and exhibits the emergent property of 'epiphany moments'.

Tarsier-34b is an open-source large-scale video-language model focused on generating high-quality video captions and achieving leading results in multiple public benchmarks.

TEMPURA is a vision-language model capable of reasoning causal event relationships and generating fine-grained timestamp descriptions for unedited videos.