Bagel
Bagel is a pioneering open-source unified multimodal AI model and platform that combines advanced cryptography with machine learning to enable monetizable, privacy-preserving collaborative AI development while offering powerful capabilities across text, image, and video understanding and generation.
https://bagel-ai.org/?ref=aipure
Product Information
Updated:Jul 16, 2025
Bagel Monthly Traffic Trends
Bagel achieved 278.3K visits with a 232.6% growth in June. The significant increase is likely due to the $5.5 million Seed funding round and the release of its open-source multimodal AI model, which has generated substantial interest and expanded its user base.
What is Bagel
Bagel is an innovative AI research platform and model architecture that transforms open-source AI development through two main offerings: 1) A product intelligence platform that converts customer feedback and company data into actionable insights, and 2) A scalable unified multimodal AI model (BAGEL) that can handle both image and text inputs/outputs with capabilities comparable to proprietary systems like GPT-4 and Gemini 2.0. Founded by Bidhan Roy, who has extensive experience in machine learning infrastructure at companies like Amazon Alexa and Cash App, Bagel aims to make open-source AI development more sustainable by ensuring proper attribution and fair revenue distribution to all contributors while maintaining privacy and security.
Key Features of Bagel
Bagel is a cutting-edge AI platform that combines product intelligence and multimodal capabilities. It features a unified architecture that can handle both image and text processing, enabling tasks from product feedback analysis to image generation and editing. The platform uses advanced cryptography for secure collaborative AI development while ensuring fair revenue attribution to contributors. It integrates with existing workflow tools and uses AI to analyze customer feedback, identify product gaps, and quantify business impact.
Multimodal AI Architecture: Uses Mixture-of-Transformer-Experts (MoT) architecture to process both visual and textual data, enabling advanced capabilities in image generation, editing, and understanding
Secure Collaborative Development: Implements cryptographic methods to enable safe collaboration on AI models while protecting proprietary data and ensuring proper revenue attribution
Automated Feedback Analysis: Automatically extracts and analyzes feedback from various sources like transcripts, tickets, and CRM updates to identify product pain points and feature requests
Workflow Integration: Seamlessly integrates with existing tools like Salesforce, Zendesk, Jira, and Gong to provide insights where teams actually work
Use Cases of Bagel
Product Management: Helps product teams analyze customer feedback, prioritize features, and make data-driven decisions about product roadmap
AI Model Development: Enables developers and researchers to collaboratively build and monetize open-source AI models while maintaining privacy
Content Creation: Provides AI-powered tools for generating and editing images, videos, and text content for marketing and creative purposes
Customer Feedback Analysis: Analyzes customer interactions across various channels to identify trends, pain points, and improvement opportunities
Pros
Strong security and privacy features with SOC2 Type 2 compliance
Comprehensive integration with existing business tools
Advanced multimodal capabilities combining text and visual processing
Cons
May require significant setup and integration effort
Complex architecture might have a learning curve for new users
How to Use Bagel
Access BAGEL: Access BAGEL through Hugging Face or install it locally. The model is open-source and free to use
Select Task Type: Choose your desired task: image generation, image editing, or image understanding, as BAGEL can handle all these tasks in a single 7B parameter model
Prepare Input: Prepare your input which can be text, images, or both, depending on your task. BAGEL handles mixed format inputs
Fine-tune (Optional): If needed, further train the model using PEFT or LoRA for efficient adaptation with both visual and textual datasets
Enable Chain-of-Thought: For better results, especially in text-to-image generation, enable the chain-of-thought feature which allows the model to 'think' before generating outputs
Execute Task: Run your task through the model. The cost is approximately $0.091 per run on Replicate
Review Output: Review the generated output, which can include images, edited content, or understanding-based responses depending on your initial task
Bagel FAQs
BAGEL is an open-source Unified Multimodal Model that can handle both image and text inputs/outputs. It's designed to offer comparable functionality to proprietary systems like GPT-4 and Gemini 2.0, with capabilities for generation, understanding, editing, style transfer, and navigation.
Bagel Video
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Analytics of Bagel Website
Bagel Traffic & Rankings
278.3K
Monthly Visits
#158945
Global Rank
-
Category Rank
Traffic Trends: Apr 2025-Jun 2025
Bagel User Insights
00:01:18
Avg. Visit Duration
2.26
Pages Per Visit
49.64%
User Bounce Rate
Top Regions of Bagel
US: 29.12%
IN: 8.75%
KZ: 6.25%
IT: 5.37%
VN: 4.06%
Others: 46.46%
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