Tag: multimodal

Fotol AI is a website that provides AGI technology and services, dedicated to providing users with powerful artificial intelligence solutions. Its main advantages include advanced technical support, rich functional modules and wide range of application fields. Fotol AI is positioned to become the first choice platform for users to explore AGI and provide users with flexible and diverse AI solutions.

Grok 4 is the latest version of the large-scale language model launched by xAI, which will be officially released in July 2025. It has leading natural language, mathematics and reasoning capabilities and is a top model AI. Grok 4 represents a huge step forward, skipping the expected Grok 3.5 version to speed up progress in the fierce AI competition.

OmniGen2 is an efficient multi-modal generation model that combines visual language models and diffusion models to achieve functions such as visual understanding, image generation and editing. Its open source nature provides researchers and developers with a strong foundation to explore personalized and controllable generative AI.

OneReach.ai is a platform designed to help organizations orchestrate advanced multi-modal AI agents that enhance employee and customer experiences. It allows users to easily create intelligent digital workers (IDWs) and provides powerful workflow automation capabilities.

FLUX.1 Kontext is a revolutionary multi-modal AI model that combines text instructions with image editing and generation to achieve precise localized editing and maintain character consistency and style coherence. The product is suitable for professional workflows such as marketing content creation, film production and design.

BAGEL is a scalable unified multimodal model that is revolutionizing the way AI interacts with complex systems. The model has functions such as conversational reasoning, image generation, editing, style transfer, navigation, composition, and thinking. It is pre-trained through deep learning video and network data, providing a foundation for generating high-fidelity, realistic images.

MNN-LLM is an efficient inference framework designed to optimize and accelerate the deployment of large language models on mobile devices and local PCs. It solves the problem of high memory consumption and computational cost through model quantization, hybrid storage and hardware-specific optimization. MNN-LLM performs well in CPU benchmarks and is significantly faster, making it suitable for users who require privacy protection and efficient inference.

HunyuanCustom is a multi-modal custom video generation framework designed to generate topic-specific videos based on user-defined conditions. This technology performs well in identity consistency and supports multiple input modes. It can handle text, image, audio and video input, and is suitable for a variety of application scenarios such as virtual human advertising and video editing.

InternVL3 is a multimodal large language model (MLLM) released by OpenGVLab as an open source, with excellent multimodal perception and reasoning capabilities. This model series includes a total of 7 sizes from 1B to 78B, which can process text, pictures, videos and other information at the same time, showing excellent overall performance. InternVL3 performs well in fields such as industrial image analysis and 3D visual perception, and its overall text performance is even better than the Qwen2.5 series. The open source of this model provides strong support for multi-modal application development and helps promote the application of multi-modal technology in more fields.

DreamActor-M1 is a Diffusion Transformer (DiT)-based human animation framework designed to achieve fine-grained global controllability, multi-scale adaptability, and long-term temporal consistency. Through hybrid guidance, the model is able to generate highly expressive and photorealistic human videos, suitable for a variety of scenarios from portraits to full-body animations. Its main advantages are high fidelity and identity preservation, bringing new possibilities for animation of human behavior.

Gemini 2.5 is the most advanced AI model launched by Google. It has efficient inference and coding performance, can handle complex problems, and performs well in multiple benchmark tests. The model introduces new thinking capabilities, combines enhanced basic models and post-training to support more complex tasks, aiming to provide powerful support for developers and enterprises. Gemini 2.5 Pro is available in Google AI Studio and the Gemini app for users who require advanced inference and coding capabilities.

Mistral-Small-3.1-24B-Base-2503 is an advanced open source model with 24 billion parameters, supports multi-language and long context processing, and is suitable for text and vision tasks. It is the basic model of Mistral Small 3.1, has strong multi-modal capabilities and is suitable for enterprise needs.

Mistral OCR is an advanced optical character recognition API developed by Mistral AI, designed to extract and structure document content with unparalleled accuracy. It can process complex documents containing text, images, tables and equations, and output results in Markdown format for easy integration with AI systems and Retrieval Enhanced Generation (RAG) systems. Its high precision, high speed and multi-modal processing capabilities make it excellent in large-scale document processing scenarios, and is especially suitable for fields such as scientific research, law, customer service and historical document protection. Mistral OCR is priced at 1,000 pages per dollar for standard usage, with batch processing up to 2,000 pages per dollar, and also offers enterprise self-hosted options for specific privacy needs.

Gemini Robotics is an advanced artificial intelligence model launched by Google DeepMind, specially designed for robotic applications. It is based on the Gemini 2.0 architecture and enables robots to perform complex real-world tasks through the fusion of vision, language and action (VLA). The importance of this technology is that it promotes the advancement of robots from laboratories to daily life and industrial applications, laying the foundation for the development of future intelligent robots. The main advantages of Gemini Robotics include strong generalization capabilities, interactivity and dexterity, allowing it to adapt to different tasks and environments. Currently, the technology is in the research and development stage, and the specific price and market positioning have not yet been determined.

R1-Omni is an innovative multi-modal emotion recognition model that improves the model's reasoning and generalization capabilities through reinforcement learning. This model is developed based on HumanOmni-0.5B, focuses on emotion recognition tasks, and can perform emotion analysis through visual and audio modal information. Its main advantages include powerful inference capabilities, significantly improved emotion recognition performance, and excellent performance on out-of-distribution data. This model is suitable for scenarios that require multi-modal understanding, such as sentiment analysis, intelligent customer service and other fields, and has important research and application value.

The GO-1 universal embodied base large model of Zhiyuan is a revolutionary artificial intelligence model launched by Zhiyuan. The model is based on the innovative Vision-Language-Latent-Action (ViLLA) architecture and achieves efficient conversion from visual and language input to robot action execution through a multi-modal large model (VLM) and a hybrid expert (MoE) system. GO-1 can learn using human videos and real robot data, has strong generalization capabilities, and can quickly adapt to new tasks and environments with very little data or even zero samples. Its main advantages include efficient learning capabilities, strong generalization performance, and adaptability to a variety of robot ontologies. The launch of this model marks an important step in the direction of generalization, openness and intelligence of embodied intelligence, and is expected to play an important role in many fields such as business, industry and home.

OpenAI Agents SDK is a development toolkit for building autonomous agents. It builds on OpenAI's advanced model capabilities, such as advanced reasoning, multi-modal interaction, and new security technologies, to provide developers with a simplified way to build, deploy, and scale reliable agent applications. The toolkit not only supports the orchestration of single-agent and multi-agent workflows, but also integrates observability tools to help developers track and optimize the execution process of agents. Its main advantages include easy-to-configure LLM models, intelligent agent handover mechanisms, configurable safety checks, and powerful debugging and performance optimization capabilities. This toolkit is suitable for businesses and developers who need to automate complex tasks and is designed to improve productivity and efficiency through agent technology.

SmolVLM2 is a lightweight video language model designed to generate relevant text descriptions or video highlights by analyzing video content. This model is efficient, has low resource consumption, and is suitable for running on a variety of devices, including mobile devices and desktop clients. Its main advantage is that it can quickly process video data and generate high-quality text output, providing powerful technical support for video content creation, video analysis, education and other fields. This model was developed by the Hugging Face team and is positioned as an efficient and lightweight video processing tool. It is currently in the experimental stage and users can try it for free.

Inception Labs is a company focused on developing diffusion large language models (dLLMs). Its technology is inspired by advanced image and video generation systems such as Midjourney and Sora. With diffusion models, Inception Labs offers 5-10 times faster speeds, greater efficiency, and greater control over generation than traditional autoregressive models. Its model supports parallel text generation, is able to correct errors and illusions, is suitable for multi-modal tasks, and performs well in inference and structured data generation. The company, comprised of researchers and engineers from Stanford, UCLA and Cornell University, is a pioneer in the field of diffusion modeling.

Aya Vision is an advanced vision model developed by the Cohere For AI team, focusing on multi-language and multi-modal tasks, supporting 23 languages. The model significantly improves the performance of visual and text tasks through innovative algorithm breakthroughs such as synthetic annotation, multilingual data expansion, and multimodal model fusion. Its main advantages include efficiency (it performs well even with limited computing resources) and extensive multi-language support. Aya Vision is launched to advance the cutting edge of multilingual and multimodal research and provide technical support to the global research community.

EgoLife is an AI assistant project for long-term, multi-modal and multi-perspective daily life. By recording the shared life experiences of six volunteers for a week, the project generated approximately 50 hours of video data, covering daily activities, social interactions and other scenes. Its multi-modal data (including video, line of sight, IMU data) and multi-view camera system provide rich contextual information for AI research. In addition, the project proposed the EgoRAG framework to solve long-term context understanding tasks and promote the application capabilities of AI in complex environments.

UniTok is an innovative visual word segmentation technology designed to bridge the gap between visual generation and comprehension. It significantly improves the representation capabilities of discrete word segmenters through multi-codebook quantization technology, enabling it to capture richer visual details and semantic information. This technology breaks through the bottleneck of traditional word segmenters in the training process and provides an efficient and unified solution for visual generation and understanding tasks. UniTok performs well in image generation and understanding tasks, such as achieving significant zero-shot accuracy improvements on ImageNet. The main advantages of this technology include efficiency, flexibility, and strong support for multi-modal tasks, bringing new possibilities to the field of visual generation and understanding.

ViDoRAG is a new multi-modal retrieval-enhanced generation framework developed by Alibaba's natural language processing team, specifically designed for complex reasoning tasks in processing visually rich documents. This framework significantly improves the robustness and accuracy of the generative model through dynamic iterative inference agents and a Gaussian Mixture Model (GMM)-driven multi-modal retrieval strategy. The main advantages of ViDoRAG include efficient processing of visual and textual information, support for multi-hop reasoning, and high scalability. The framework is suitable for scenarios where information needs to be retrieved and generated from large-scale documents, such as intelligent question answering, document analysis and content creation. Its open source nature and flexible modular design make it an important tool for researchers and developers in the field of multimodal generation.

Migician is a multi-modal large language model developed by the Natural Language Processing Laboratory of Tsinghua University, focusing on multi-image localization tasks. By introducing an innovative training framework and the large-scale data set MGrounding-630k, this model significantly improves the precise positioning capabilities in multi-image scenarios. It not only surpasses existing multi-modal large language models, but even surpasses the larger 70B model in performance. The main advantage of Migician is its ability to handle complex multi-image tasks and provide free-form localization instructions, making it an important application prospect in the field of multi-image understanding. The model is currently open source on Hugging Face for use by researchers and developers.

Mochii AI is designed to power human-AI collaboration through adaptive memory, custom personalities, and seamless multi-platform integration. It supports multiple advanced AI models such as OpenAI, Claude, Gemini, DALL-E and Stable Diffusion, enabling functions such as intelligent dialogue, content creation, data analysis and image generation. The product offers a free tier that requires no credit card and is suitable for professionals who want to increase their productivity and creativity.

M2RAG is a benchmark code library for retrieval augmentation generation in multimodal contexts. It answers questions by retrieving documents across multiple modalities and evaluates the ability of multimodal large language models (MLLMs) in leveraging multimodal contextual knowledge. The model was evaluated on tasks such as image description, multimodal question answering, fact verification, and image rearrangement, aiming to improve the effectiveness of the model in multimodal context learning. M2RAG provides researchers with a standardized testing platform that helps advance the development of multimodal language models.

TheoremExplainAgent is an AI-based model focused on generating detailed multi-modal explanation videos for mathematical and scientific theorems. It helps users understand complex concepts more deeply by combining text and visual animations. This product uses Manim animation technology to generate long videos of more than 5 minutes, which fills the shortcomings of traditional text explanations and is particularly good at revealing reasoning errors. It is mainly aimed at the education field and aims to improve learners' understanding of theorems in STEM fields. Its price and commercialization positioning have not yet been determined.

Gemini 2.0 Flash-Lite is an efficient language model launched by Google, optimized for long text processing and complex tasks. It performs well on inference, multimodal, mathematical, and factual benchmarks, and has a simplified pricing strategy that makes million-level context windows more affordable. Gemini 2.0 Flash-Lite is fully open in Google AI Studio and Vertex AI, and is suitable for enterprise-level production use.

Phi-4-multimodal-instruct is a multimodal basic model developed by Microsoft that supports text, image and audio input and generates text output. The model is built based on the research and data sets of Phi-3.5 and Phi-4.0, and undergoes processes such as supervised fine-tuning, direct preference optimization, and human feedback reinforcement learning to improve instruction compliance and safety. It supports text, image and audio input in multiple languages, has a context length of 128K, and is suitable for a variety of multi-modal tasks, such as speech recognition, speech translation, visual question answering, etc. The model has achieved significant improvements in multi-modal capabilities, especially on speech and visual tasks. It provides developers with powerful multi-modal processing capabilities that can be used to build various multi-modal applications.

Magma-8B is a multi-modal AI basic model developed by Microsoft and designed specifically for studying multi-modal AI agents. It combines text and image inputs, is able to generate text output, and has visual planning and agent capabilities. This model uses Meta LLaMA-3 as the backbone of the language model, combined with the CLIP-ConvNeXt-XXLarge visual encoder, to support learning spatiotemporal relationships from unlabeled video data, and has strong generalization capabilities and multi-task adaptability. Magma-8B performs well in multi-modal tasks, especially in spatial understanding and reasoning. It provides powerful tools for multimodal AI research and advances the study of complex interactions in virtual and real environments.

DeepSeek is an advanced language model developed by China AI Lab supported by the High-Flyer Fund, focusing on open source models and innovative training methods. Its R1 series of models excel in logical reasoning and problem solving, using reinforcement learning and a hybrid expert framework to optimize performance and achieve efficient training at low cost. DeepSeek’s open source strategy drives community innovation while igniting industry discussions about AI competition and the impact of open source models. Its free and registration-free usage further lowers the user threshold and is suitable for a wide range of application scenarios.

ZeroBench is a benchmark designed for evaluating the visual understanding capabilities of large multimodal models (LMMs). It challenges the limits of current models with 100 carefully crafted and rigorously vetted complex questions, with 334 sub-questions. This benchmark aims to fill the gaps in existing visual benchmarks and provide a more challenging and high-quality evaluation tool. The main advantages of ZeroBench are its high difficulty, lightweight, diverse and high-quality characteristics, which allow it to effectively differentiate the performance of models. In addition, it provides detailed sub-problem evaluations to help researchers better understand the model's inference capabilities.

Magma is a multi-modal basic model launched by the Microsoft research team, aiming to achieve the planning and execution of complex tasks through the combination of vision, language and movement. Through large-scale visual language data pre-training, it has the capabilities of language understanding, spatial intelligence and action planning, and can perform well in tasks such as UI navigation and robot operation. The emergence of this model provides a powerful basic framework for multi-modal AI agent tasks and has broad application prospects.

Grok 3 is the latest flagship AI model developed by Elon Musk’s AI company xAI. It has significantly improved computing power and data set size, can handle complex mathematical and scientific problems, and supports multi-modal input. Its main advantage is its powerful inference capabilities, the ability to provide more accurate answers, and surpassing existing top models in some benchmarks. The launch of Grok 3 marks the further development of xAI in the field of AI, aiming to provide users with smarter and more efficient AI services. This model currently mainly provides services through Grok APP and X platform, and will also launch voice mode and enterprise API interface in the future. It is positioned as a high-end AI solution, mainly for users who require deep reasoning and multi-modal interaction.

CLaMP 3 is an advanced music information retrieval model that supports cross-modal and cross-language music retrieval through comparative learning to align features of scores, performance signals, audio recordings, and multilingual texts. It is able to handle misaligned modalities and unseen languages, exhibiting strong generalization capabilities. The model is trained on the large-scale data set M4-RAG, which covers various music traditions around the world and supports a variety of music retrieval tasks, such as text-to-music, image-to-music, etc.

VideoRAG is an innovative retrieval-enhanced generative framework specifically designed to understand and process extremely long contextual videos. It enables understanding of videos of unlimited length by combining graph-driven text knowledge anchoring and hierarchical multi-modal context encoding. The framework can dynamically construct knowledge graphs, maintain semantic coherence of multiple video contexts, and optimize retrieval efficiency through an adaptive multi-modal fusion mechanism. VideoRAG's key benefits include efficient processing of extremely long-context videos, structured video knowledge indexing, and multi-modal retrieval capabilities, enabling it to provide comprehensive answers to complex queries. This framework has important technical value and application prospects in the field of long video understanding.

MedRAX is an innovative AI framework designed for intelligent analysis of chest X-rays (CXR). It is capable of dynamically processing complex medical queries by integrating state-of-the-art CXR analysis tools and multi-modal large-scale language models. MedRAX can run without additional training, supports real-time CXR interpretation, and is suitable for a variety of clinical scenarios. Its main advantages include high flexibility, powerful reasoning capabilities, and transparent workflows. This product is aimed at medical professionals and aims to improve diagnostic efficiency and accuracy and promote the practical use of medical AI.

Qwen2.5-VL is the latest flagship visual language model launched by the Qwen team and is an important advancement in the field of visual language models. It can not only identify common objects, but also analyze complex content such as text, charts, and icons in images, and support the understanding and event location of long videos. The model performs well in multiple benchmarks, especially in document understanding and visual agent tasks, demonstrating strong visual understanding and reasoning capabilities. Its main advantages include efficient multi-modal understanding, powerful long video processing capabilities and flexible tool calling capabilities, which are suitable for a variety of application scenarios.

Gemini 2.0 is Google’s important progress in the field of generative AI and represents the latest artificial intelligence technology. It provides developers with efficient and flexible solutions through its powerful language generation capabilities, suitable for a variety of complex scenarios. Key benefits of Gemini 2.0 include high performance, low latency and a simplified pricing strategy designed to reduce development costs and increase productivity. The model is provided through Google AI Studio and Vertex AI, supports multiple modal inputs, and has a wide range of application prospects.

Gemini Pro is one of the most advanced AI models launched by Google DeepMind, designed for complex tasks and programming scenarios. It excels at code generation, complex instruction understanding, and multi-modal interaction, supporting text, image, video, and audio input. Gemini Pro provides powerful tool calling capabilities, such as Google search and code execution, and can handle up to 2 million words of contextual information, making it suitable for professional users and developers who require high-performance AI support.

OmniHuman-1 is an end-to-end multi-modal conditional human video generation framework capable of generating human videos based on a single human image and motion signals (such as audio, video, or a combination thereof). This technology overcomes the problem of scarcity of high-quality data through a hybrid training strategy, supports image input with any aspect ratio, and generates realistic human videos. It performs well in weak signal input (especially audio) and is suitable for a variety of scenarios, such as virtual anchors, video production, etc.

MILS is an open source project released by Facebook Research that aims to demonstrate the ability of large language models (LLMs) to handle visual and auditory tasks without any training. This technology enables automatic description generation of images, audio and video by utilizing pre-trained models and optimization algorithms. This technological breakthrough provides new ideas for the development of multi-modal artificial intelligence and demonstrates the potential of LLMs in cross-modal tasks. This model is primarily intended for researchers and developers, providing them with a powerful tool to explore multimodal applications. The project is currently free and open source and aims to promote academic research and technology development.

MNN Large Model Android App is an Android application developed by Alibaba based on large language model (LLM). It supports multiple modal inputs and outputs, including text generation, image recognition, audio transcription, and more. The application optimizes inference performance to ensure efficient operation on mobile devices while protecting user data privacy, with all processing done locally. It supports a variety of leading model providers, such as Qwen, Gemma, Llama, etc., and is suitable for a variety of scenarios.

Janus-Pro-7B is a powerful multimodal model capable of processing both text and image data. It solves the conflict between traditional models in understanding and generation tasks by separating the visual encoding path, improving the flexibility and performance of the model. The model is based on the DeepSeek-LLM architecture, uses SigLIP-L as the visual encoder, supports 384x384 image input, and performs well in multi-modal tasks. Its main advantages include efficiency, flexibility and powerful multi-modal processing capabilities. This model is suitable for scenarios requiring multi-modal interaction, such as image generation and text understanding.

Janus-Pro-1B is an innovative multimodal model focused on unifying multimodal understanding and generation. It solves the conflicting problem of traditional methods in understanding and generation tasks by separating the visual encoding path, while maintaining a single unified Transformer architecture. This design not only improves the model's flexibility but also enables it to perform well in multi-modal tasks, even surpassing task-specific models. The model is built on DeepSeek-LLM-1.5b-base/DeepSeek-LLM-7b-base, uses SigLIP-L as the visual encoder, supports 384x384 image input, and uses a specific image generation tokenizer. Its open source nature and flexibility make it a strong candidate for the next generation of multimodal models.

Humanity's Last Exam is a multi-modal benchmark developed by a global collaboration of experts to measure the performance of large language models in academic settings. It contains 3,000 questions contributed by nearly 1,000 experts from more than 500 institutions in 50 countries, covering more than 100 disciplines. The test is intended to be the ultimate closed academic benchmark, pushing the boundaries of artificial intelligence technology by pushing the limits of models. Its main advantage is that it is highly difficult and can effectively evaluate the performance of models on complex academic problems.

Computer-Using Agent (CUA) is an advanced artificial intelligence model developed by OpenAI that combines the vision capabilities of GPT-4o with advanced reasoning capabilities through reinforcement learning. It is able to interact with a graphical user interface (GUI) like a human, without relying on operating system-specific APIs or network interfaces. The flexibility of CUA enables it to perform tasks in a variety of digital environments, such as filling out forms, browsing the web, etc. The emergence of this technology marks the next step in the development of AI, opening up new possibilities for the application of AI in everyday tools. CUA is currently in research preview and available to Pro users in the United States through the Operator.

SmolVLM-256M is a multi-modal model developed by Hugging Face, based on the Idefics3 architecture and designed for efficient processing of image and text input. It can answer questions about images, describe visual content, or transcribe text, and requires less than 1GB of GPU memory to run inference. The model performs well on multi-modal tasks while maintaining a lightweight architecture suitable for on-device applications. Its training data comes from The Cauldron and Docmatix data sets, covering document understanding, image description and other fields, giving it a wide range of application potential. The model is currently available for free on the Hugging Face platform and is designed to provide developers and researchers with powerful multi-modal processing capabilities.

SmolVLM-500M is a lightweight multi-modal model developed by Hugging Face and belongs to the SmolVLM series. The model is based on the Idefics3 architecture and focuses on efficient image and text processing tasks. It can accept image and text input in any order and generate text output, which is suitable for tasks such as image description and visual question answering. Its lightweight architecture enables it to run on resource-constrained devices while maintaining strong multi-modal task performance. The model is licensed under the Apache 2.0 license, enabling open source and flexible usage scenarios.

VideoLLaMA3 is a cutting-edge multi-modal basic model developed by the DAMO-NLP-SG team, focusing on image and video understanding. The model is based on the Qwen2.5 architecture and combines advanced visual encoders (such as SigLip) and powerful language generation capabilities to handle complex visual and language tasks. Its main advantages include efficient spatiotemporal modeling capabilities, powerful multi-modal fusion capabilities, and optimized training on large-scale data. This model is suitable for application scenarios that require deep video understanding, such as video content analysis, visual question answering, etc., and has extensive research and commercial application potential.

UI-TARS is a new GUI agent model developed by ByteDance that focuses on seamless interaction with graphical user interfaces through human-like perception, reasoning, and action capabilities. The model integrates key components such as perception, reasoning, localization, and memory into a single visual language model, enabling end-to-end task automation without the need for predefined workflows or manual rules. Its main advantages include powerful cross-platform interaction capabilities, multi-step task execution capabilities, and the ability to learn from synthetic and real data, making it suitable for a variety of automation scenarios, such as desktop, mobile, and web environments.

Doubao-1.5-pro is a high-performance sparse MoE (Mixture of Experts) large language model developed by the Doubao team. This model achieves the ultimate balance between model performance and inference performance through integrated training-inference design. It performs well on multiple public evaluation benchmarks, especially in reasoning efficiency and multi-modal capabilities. This model is suitable for scenarios that require efficient reasoning and multi-modal interaction, such as natural language processing, image recognition, and voice interaction. Its technical background is based on the sparse activation MoE architecture, which achieves higher performance leverage than traditional dense models by optimizing the activation parameter ratio and training algorithm. In addition, the model also supports dynamic adjustment of parameters to adapt to different application scenarios and cost requirements.

Gemini Flash Thinking is the latest AI model launched by Google DeepMind, designed for complex tasks. It can display the reasoning process and help users better understand the decision-making logic of the model. The model excels in mathematics and science, supporting long text analysis and code execution capabilities. It aims to provide developers with powerful tools to advance the application of artificial intelligence in complex tasks.

Kimi k1.5 is a multi-modal language model developed by MoonshotAI. Through reinforcement learning and long context expansion technology, it significantly improves the model's performance in complex reasoning tasks. The model has reached industry-leading levels on multiple benchmarks, surpassing GPT-4o and Claude Sonnet 3.5 in mathematical reasoning tasks such as AIME and MATH-500. Its main advantages include an efficient training framework, powerful multi-modal reasoning capabilities, and support for long contexts. Kimi k1.5 is mainly targeted at application scenarios that require complex reasoning and logical analysis, such as programming assistance, mathematical problem solving, and code generation.

OmAgent is a multi-modal native agent framework for smart devices and more. It uses a divide-and-conquer algorithm to efficiently solve complex tasks, can pre-process long videos and conduct questions and answers with human-like accuracy, and can also provide personalized clothing suggestions based on user requests and optional weather conditions. At present, the price is not clearly displayed on the official website, but from a functional point of view, it is mainly targeted at user groups that require efficient task processing and intelligent interaction, such as developers, enterprises, etc.

InternVL2.5-MPO is a series of multi-modal large-scale language models based on InternVL2.5 and Mixed Preference Optimization (MPO). It performs well on multi-modal tasks by integrating the newly incrementally pretrained InternViT with multiple pretrained large language models (LLMs), such as InternLM 2.5 and Qwen 2.5, using randomly initialized MLP projectors. This model series was trained on the multi-modal reasoning preference data set MMPR, which contains approximately 3 million samples. Through effective data construction processes and hybrid preference optimization technology, the model's reasoning capabilities and answer quality are improved.

MiniCPM-o 2.6 is the latest and most powerful model in the MiniCPM-o series. The model is built based on SigLip-400M, Whisper-medium-300M, ChatTTS-200M and Qwen2.5-7B and has 8B parameters. It performs well in visual understanding, voice interaction and multi-modal live broadcast, supporting real-time voice dialogue and multi-modal live broadcast functions. This model has performed well in the open source community, surpassing several well-known models. Its advantages lie in efficient inference speed, low latency, low memory and power consumption, and it can efficiently support multi-modal live broadcast on terminal devices such as iPad. In addition, MiniCPM-o 2.6 is easy to use and supports multiple usage methods, including CPU inference of llama.cpp, quantization models in int4 and GGUF formats, high-throughput inference of vLLM, etc.

Moondream AI is an open source visual language model with powerful multi-modal processing capabilities. It supports multiple quantization formats, such as fp16, int8, and int4, and can perform GPU and CPU optimized inference on a variety of target devices such as servers, PCs, and mobile devices. Its main advantages include being fast, efficient, easy to deploy, and using the Apache 2.0 license, allowing users to use and modify it freely. Moondream AI is positioned to provide developers with a flexible and efficient artificial intelligence solution that is suitable for various application scenarios that require visual and language processing capabilities.

InternVL2.5-MPO is an advanced multi-modal large-scale language model series built on InternVL2.5 and Mixed Preference Optimization (MPO). This series of models performs well in multi-modal tasks, capable of processing image, text and video data and generating high-quality text responses. The model adopts the 'ViT-MLP-LLM' paradigm to optimize visual processing capabilities through pixel unshuffle operations and dynamic resolution strategies. In addition, the model also introduces support for multiple image and video data, further expanding its application scenarios. InternVL2.5-MPO surpassed multiple benchmark models in multi-modal capability evaluation, proving its leading position in the multi-modal field.

InternVL2_5-26B-MPO-AWQ is a multi-modal large-scale language model developed by OpenGVLab, aiming to improve the model's reasoning capabilities through mixed preference optimization. The model performs well in multi-modal tasks and is able to handle complex relationships between images and text. It adopts advanced model architecture and optimization technology, giving it significant advantages in multi-modal data processing. This model is suitable for scenarios that require efficient processing and understanding of multi-modal data, such as image description generation, multi-modal question answering, etc. Its main advantages include powerful inference capabilities and efficient model architecture.

CreatiLayout is an innovative layout-to-image generation technology that utilizes the Siamese Multimodal Diffusion Transformer to achieve high-quality and fine-grained controllable image generation. This technology can accurately render complex attributes such as color, texture, shape, quantity and text, making it suitable for application scenarios that require precise layout and image generation. Its main advantages include efficient layout guidance integration, powerful image generation capabilities and support for large-scale data sets. CreatiLayout was jointly developed by Fudan University and ByteDance to promote the application of image generation technology in the field of creative design.

VITA-1.5 is an open source multi-modal large language model designed to achieve near real-time visual and voice interaction. It provides users with a smoother interactive experience by significantly reducing interaction latency and improving multi-modal performance. The model supports English and Chinese and is suitable for a variety of application scenarios, such as image recognition, speech recognition, and natural language processing. Its main advantages include efficient speech processing capabilities and powerful multi-modal understanding capabilities.

FlexRAG is a flexible and high-performance framework for retrieval augmentation generation (RAG) tasks. It supports multi-modal data, seamless configuration management, and out-of-the-box performance for research and prototyping. Written in Python, the framework is lightweight and high-performance, significantly increasing the speed and reducing latency of RAG workflows. Its main advantages include support for multiple data types, unified configuration management, and easy integration and expansion.

InternVL2_5-26B-MPO is a multimodal large language model (MLLM). Based on InternVL2.5, it further improves the model performance through Mixed Preference Optimization (MPO). This model can process multi-modal data including images and text, and is widely used in scenarios such as image description and visual question answering. Its importance lies in its ability to understand and generate text that is closely related to the content of the image, pushing the boundaries of multi-modal artificial intelligence. Product background information includes its superior performance in multi-modal tasks and evaluation results in OpenCompass Leaderboard. This model provides researchers and developers with powerful tools to explore and realize the potential of multimodal artificial intelligence.

InternVL2_5-8B-MPO-AWQ is a multi-modal large-scale language model launched by OpenGVLab. It is based on the InternVL2.5 series and uses Mixed Preference Optimization (MPO) technology. The model demonstrates excellent performance in visual and language understanding and generation, especially in multi-modal tasks. It achieves in-depth understanding and interaction of images and text by combining the visual part InternViT and the language part InternLM or Qwen, using randomly initialized MLP projectors for incremental pre-training. The importance of this technology lies in its ability to process multiple data types including single images, multiple images, and video data, providing new solutions in the field of multi-modal artificial intelligence.

InternVL2.5-MPO is an advanced multi-modal large-scale language model series built on InternVL2.5 and hybrid preference optimization. The model integrates the newly incrementally pretrained InternViT with various pretrained large language models, including InternLM 2.5 and Qwen 2.5, using randomly initialized MLP projectors. InternVL2.5-MPO retains the same model architecture as InternVL 2.5 and its predecessor in the new version, following the "ViT-MLP-LLM" paradigm. The model supports multiple image and video data, and further improves model performance through Mixed Preference Optimization (MPO), making it perform better in multi-modal tasks.

DiffSensei is a customized comic generation model that combines multimodal large language models (LLMs) and diffusion models. It can generate controllable black and white comic panels based on user-provided text prompts and character images, with flexible character adaptability. The importance of this technology is that it combines natural language processing with image generation, providing new possibilities for comic creation and personalized content generation. The DiffSensei model has attracted attention for its high-quality image generation, diverse application scenarios, and efficient use of resources. Currently, the model is public on GitHub and can be downloaded and used for free, but specific use may require certain computing resources.

InternVL2_5-4B-MPO-AWQ is a multimodal large language model (MLLM) focused on improving the model's performance in image and text interaction tasks. The model is based on the InternVL2.5 series and further improves performance through Mixed Preference Optimization (MPO). It can handle a variety of inputs including single and multi-image and video data, and is suitable for complex tasks that require interactive understanding of images and text. InternVL2_5-4B-MPO-AWQ provides a powerful solution for image-to-text tasks with its excellent multi-modal capabilities.

OpenEMMA is an open source project that reproduces Waymo's EMMA model and provides an end-to-end framework for motion planning of autonomous vehicles. The model leverages pre-trained visual language models (VLMs) such as GPT-4 and LLaVA to integrate text and forward-looking camera input to achieve accurate predictions of its own future waypoints and provide reasons for decision-making. The goal of OpenEMMA is to provide researchers and developers with easily accessible tools to advance autonomous driving research and applications.

InternVL2.5-MPO is an advanced multi-modal large-scale language model series built based on InternVL2.5 and hybrid preference optimization. The model integrates the new incremental pre-trained InternViT and various pre-trained large language models, such as InternLM 2.5 and Qwen 2.5, using randomly initialized MLP projectors. It supports multiple image and video data and performs well in multi-modal tasks, capable of understanding and generating image-related text content.

Valley is a multi-modal large-scale model (MLLM) developed by ByteDance and is designed to handle a variety of tasks involving text, image and video data. The model achieved the best results in internal e-commerce and short video benchmarks, far outperforming other open source models, and demonstrated excellent performance on the OpenCompass multimodal model evaluation rankings, with an average score of 67.40, ranking among the top two among known open source MLLMs (<10B).

Valley-Eagle-7B is a multi-modal large-scale model developed by Bytedance and is designed to handle a variety of tasks involving text, image and video data. The model achieved best results in internal e-commerce and short video benchmarks, and demonstrated superior performance compared to models of the same size in OpenCompass tests. Valley-Eagle-7B combines LargeMLP and ConvAdapter to build the projector, and introduces VisionEncoder to enhance the model's performance in extreme scenes.

Valley is a cutting-edge multi-modal large-scale model developed by ByteDance that is capable of handling a variety of tasks involving text, image and video data. The model achieved the best results in internal e-commerce and short video benchmarks, outperforming other open source models. In the OpenCompass test, compared with models of the same scale, the average score was greater than or equal to 67.40, ranking second among models smaller than 10B. The Valley-Eagle version refers to Eagle and introduces a visual encoder that can flexibly adjust the number of tokens and parallel the original visual tokens, enhancing the performance of the model in extreme scenarios.

InternVL2_5-2B-MPO is a family of multi-modal large-scale language models that demonstrates excellent overall performance. The series is built on InternVL2.5 and hybrid preference optimization. It integrates the newly incrementally pretrained InternViT with various pretrained large language models, including InternLM 2.5 and Qwen 2.5, using randomly initialized MLP projectors. The model performs well in multi-modal tasks and is able to handle a variety of data types including images and text, making it suitable for scenarios that require understanding and generating multi-modal content.

InternVL2_5-1B-MPO is a multimodal large language model (MLLM) built on InternVL2.5 and Mixed Preference Optimization (MPO), demonstrating superior overall performance. The model integrates the newly incrementally pretrained InternViT with various pretrained large language models (LLMs), including InternLM 2.5 and Qwen 2.5, using randomly initialized MLP projectors. InternVL2.5-MPO retains the same "ViT-MLP-LLM" paradigm as InternVL 2.5 and its predecessors in model architecture, and introduces support for multiple image and video data. This model performs well in multi-modal tasks and can handle a variety of visual language tasks including image description, visual question answering, etc.

InternVL2-8B-MPO is a multimodal large language model (MLLM) that enhances the model's multimodal reasoning capabilities by introducing a mixed preference optimization (MPO) process. This model designed an automated preference data construction pipeline in terms of data, and built MMPR, a large-scale multi-modal reasoning preference data set. In terms of models, InternVL2-8B-MPO is initialized based on InternVL2-8B and fine-tuned using the MMPR data set, showing stronger multi-modal reasoning capabilities and fewer hallucinations. The model achieved an accuracy of 67.0% on MathVista, surpassing InternVL2-8B by 8.7 points, and its performance was close to InternVL2-76B, which is 10 times larger.

Gemini Multimodal Live + WebRTC is a sample project showing how to build a simple speech AI application, using Gemini Multimodal Live API and WebRTC technology. The main advantages of this product include low latency, better robustness, ease of implementation of core functions, and compatibility with SDKs for multiple platforms and languages. Product background information shows that this is an open source project designed to improve the performance of real-time media connections and simplify the development process through WebRTC technology.

This is a multi-modal language model framework developed by a Stanford University research team that aims to unify verbal and non-verbal language in 3D human movements. The model is capable of understanding and generating multimodal data including text, speech, and motion, which is critical for creating virtual characters that can communicate naturally and is widely used in games, movies, and virtual reality. Key advantages of this model include high flexibility, low training data requirements, and the ability to unlock new tasks such as editable gesture generation and predicting emotions from actions.

Infini-Megrez is a terminal-side full-modal understanding model developed by Wuwen Core. It is based on the Megrez-3B-Instruct extension and has the ability to understand and analyze three modal data: images, text, and audio, and achieves optimal accuracy in image understanding, language understanding, and speech understanding. Through collaborative optimization of software and hardware, this model ensures that each structural parameter is highly compatible with mainstream hardware, and its inference speed is up to 300% ahead of models with the same accuracy. It is simple and easy to use, using the original LLaMA structure. Developers can deploy the model on various platforms without any modification, minimizing the complexity of secondary development. In addition, Infini-Megrez also provides a complete WebSearch solution, allowing the model to automatically decide when to call search, automatically switch between search and dialogue, and provide better summary effects.

POINTS-Yi-1.5-9B-Chat is a visual language model that integrates the latest visual language model technology and new technologies proposed by WeChat AI. This model has significant innovations in pre-training data set filtering and model soup (Model Soup) technology, which can significantly reduce the size of pre-training data sets and improve model performance. It performs well on multiple benchmarks and is an important advance in the field of visual language models.

POINTS-Qwen-2-5-7B-Chat is a model that integrates the latest advances and new techniques in visual language models, proposed by WeChat AI researchers. It significantly improves model performance through pre-training data set screening, model soup and other technologies. This model performs well on multiple benchmarks and is an important advancement in the field of visual language models.

WePOINTS is a series of multi-modal models developed by the WeChat AI team, aiming to create a unified framework that accommodates various modalities. These models leverage the latest multimodal modeling advances and technologies to drive seamless unification of content understanding and generation. The WePOINTS project not only provides models, but also includes pre-training data sets, evaluation tools and usage tutorials, which is an important contribution to the field of multi-modal artificial intelligence.

InternVL 2.5 is a family of advanced multimodal large language models based on InternVL 2.0, which introduces significant enhancements in training and testing strategies and data quality while maintaining the core model architecture. This model provides an in-depth look at the relationship between model scaling and performance, systematically exploring performance trends for visual encoders, language models, dataset sizes, and test-time configurations. Through extensive evaluation on a wide range of benchmarks including multi-disciplinary reasoning, document understanding, multi-image/video understanding, real-world understanding, multi-modal hallucination detection, visual localization, multi-language capabilities and pure language processing, InternVL 2.5 has demonstrated competitiveness on par with leading commercial models such as GPT-4o and Claude-3.5-Sonnet. In particular, the model is the first open source MLLM to exceed 70% on the MMMU benchmark, achieve a 3.7 percentage point improvement via chain-of-thinking (CoT) inference, and demonstrate strong potential for test-time scaling.

InternVL2_5-4B is an advanced multi-modal large language model (MLLM) that maintains the core model architecture based on InternVL 2.0 and has significant enhancements in training and testing strategies and data quality. The model performs well in processing image, text-to-text tasks, especially in multi-modal reasoning, mathematical problem solving, OCR, diagrams, and document understanding. As an open source model, it provides researchers and developers with powerful tools to explore and build vision- and language-based intelligent applications.

InternVL 2.5 is an advanced multi-modal large language model series that builds on InternVL 2.0 by introducing significant training and testing strategy enhancements and data quality improvements while maintaining its core model architecture. The model integrates the newly incrementally pretrained InternViT with various pretrained large language models, such as InternLM 2.5 and Qwen 2.5, using randomly initialized MLP projectors. InternVL 2.5 supports multiple image and video data, with dynamic high-resolution training methods that provide better performance when processing multi-modal data.

InternVL 2.5 is a series of advanced multimodal large language models (MLLM) that builds on InternVL 2.0 by introducing significant training and testing strategy enhancements and data quality improvements, while maintaining its core model architecture. The model integrates the newly incrementally pretrained InternViT with various pretrained large language models (LLMs), such as InternLM 2.5 and Qwen 2.5, using randomly initialized MLP projectors. InternVL 2.5 supports multiple image and video data, and enhances the model's ability to handle multi-modal data through dynamic high-resolution training methods.

Gemini 2.0 Flash is the next generation AI model launched by Google, aiming to empower developers to build future AI applications. Since the release of Gemini 1.0 in December, millions of developers have used Google AI Studio and Vertex AI to build Gemini apps in 109 languages. Gemini 2.0 Flash is twice as powerful as 1.5 Pro while enabling enhanced performance, including new multi-modal output and native tool usage. It offers experimental access in Google AI Studio and Vertex AI via the Gemini API, with general availability planned for early next year. The main advantages of Gemini 2.0 Flash include better performance, new output modes, native tool usage and multi-modal real-time API. These features will further improve developer productivity and application interactivity.

Gemini 2.0 is the latest AI model launched by Google DeepMind, aiming to provide support for the "intelligent assistant era". The model has been upgraded in multi-modal capabilities, including native image and audio output and tool usage capabilities, making building a new AI smart assistant closer to the vision of a universal assistant. The release of Gemini 2.0 marks Google's in-depth exploration and continuous innovation in the field of AI. By providing more powerful information processing and output capabilities, it makes information more useful and brings users a more efficient and convenient experience.

MAmmoTH-VL is a large-scale multi-modal reasoning platform that significantly improves the performance of multi-modal large language models (MLLMs) in multi-modal tasks through instruction tuning technology. The platform uses open models to create a dataset of 12 million command-response pairs, covering diverse, inference-intensive tasks with detailed and faithful justification. MAmmoTH-VL achieved state-of-the-art performance on benchmarks such as MathVerse, MMMU-Pro and MuirBench, demonstrating its importance in education and research.

InternViT-6B-448px-V2_5 is a visual model based on InternViT-6B-448px-V1-5. By using ViT incremental learning with NTP loss (stage 1.5), it improves the visual encoder's ability to extract visual features, especially in areas that are underrepresented in large-scale network datasets, such as multi-language OCR data and mathematical charts. This model is part of the InternVL 2.5 series, retaining the same "ViT-MLP-LLM" model architecture as the previous generation, and integrating the new incremental pre-trained InternViT with various pre-trained LLMs, including InternLM 2.5 and Qwen 2.5, using randomly initialized MLP projectors.

InternVL2_5-8B is a multi-modal large language model (MLLM) developed by OpenGVLab. It has significant training and testing strategy enhancements and data quality improvements based on InternVL 2.0. The model adopts the 'ViT-MLP-LLM' architecture, which integrates the new incremental pre-trained InternViT with multiple pre-trained language models, such as InternLM 2.5 and Qwen 2.5, using a randomly initialized MLP projector. InternVL 2.5 series models demonstrate excellent performance on multi-modal tasks, including image and video understanding, multi-language understanding, etc.

InternVL2_5-26B is an advanced multimodal large language model (MLLM) that is further developed based on InternVL 2.0 by introducing significant training and testing strategy enhancements and data quality improvements. The model maintains the "ViT-MLP-LLM" core model architecture of its predecessor and integrates the newly incrementally pretrained InternViT with various pretrained large language models (LLMs), such as InternLM 2.5 and Qwen 2.5, using randomly initialized MLP projectors. InternVL 2.5 series models demonstrate excellent performance in multi-modal tasks, especially in visual perception and multi-modal capabilities.

InternVL 2.5 is a series of multi-modal large-scale language models launched by OpenGVLab. It has significant training and testing strategy enhancements and data quality improvements based on InternVL 2.0. This model series can process image, text and video data, and has the ability to understand and generate multi-modal data. It is a cutting-edge product in the current field of multi-modal artificial intelligence. The InternVL 2.5 series models provide powerful support for multi-modal tasks with their high performance and open source features.

InternVL 2.5 is a series of advanced multimodal large language models (MLLM) that builds on InternVL 2.0 by introducing significant training and testing strategy enhancements and data quality improvements. This model series is optimized in terms of visual perception and multi-modal capabilities, supporting a variety of functions including image and text-to-text conversion, and is suitable for complex tasks that require processing of visual and language information.

The Qwen2-VL-7B is the latest iteration of the Qwen-VL model and represents nearly a year of innovation. The model achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, and others. It can understand videos longer than 20 minutes and provide high-quality support for video-based question answering, dialogue, content creation, etc. In addition, Qwen2-VL also supports multi-language, in addition to English and Chinese, it also includes most European languages, Japanese, Korean, Arabic, Vietnamese, etc. Model architecture updates include Naive Dynamic Resolution and Multimodal Rotary Position Embedding (M-ROPE), which enhance its multi-modal processing capabilities.

Pi-intelligent presentation document is a presentation document production platform that uses AI technology to provide rich design elements and multi-modal model construction design. It can integrate users' notes, PDFs, web pages, pictures, videos and data to create content in any format. Product background information shows that Pi aims to provide users with elegantly structured content generation and design inspiration through AI intelligence and knowledge engines, suitable for users who need to create presentation documents. The product is positioned to improve the efficiency and quality of presentation documents, and the price information is not clearly provided on the page.

Amazon Nova is a new generation of basic models launched by Amazon that can process text, images, and video cues, enabling customers to use Amazon Nova-powered generative AI applications to understand videos, charts, and documents, or to generate videos and other multimedia content. The Amazon Nova model, which has approximately 1,000 generative AI applications running within Amazon, is designed to help internal and external builders address challenges and make meaningful progress in latency, cost-effectiveness, customization, information grounding, and agent capabilities.

Aria-Base-64K is one of the base models of the Aria series, designed for research purposes and continued training. The model is formed after the long text pre-training stage and is trained on 33B tokens (21B multimodal, 12B language, 69% long text). It is suitable for continued pre-training or fine-tuning of long video Q&A data sets or long document Q&A data sets. Even when resources are limited, the data set can be tuned with short instructions for post-training and transferred to long text Q&A scenarios. The model is able to understand up to 250 high-resolution images or up to 500 medium-resolution images and maintains strong base performance in language and multi-modal scenes.

Qwen2vl-Flux is an advanced multi-modal image generation model that combines the FLUX framework with the visual language understanding capabilities of Qwen2VL. The model excels at generating high-quality images based on textual cues and visual references, providing superior multi-modal understanding and control. Product background information shows that Qwen2vl-Flux integrates Qwen2VL’s visual language capabilities, enhancing FLUX’s image generation accuracy and context awareness capabilities. Its main advantages include enhanced visual language understanding, multiple generation modes, structural control, flexible attention mechanism and high-resolution output.

jina-clip-v2 is a multi-lingual multi-modal embedding model developed by Jina AI. It supports image retrieval in 89 languages, is capable of processing 512x512 resolution images, and provides output in different dimensions from 64 to 1024 to adapt to different storage and processing needs. The model combines the powerful text encoder Jina-XLM-RoBERTa and the visual encoder EVA02-L14 to create aligned image and text representations through joint training. jina-clip-v2 provides more accurate and easier-to-use capabilities in multi-modal search and retrieval, especially in breaking language barriers and providing cross-modal understanding and retrieval.