Ming
Ming is an open-source full-modal large language model from Ant Group, based on a unified multimodal architecture with “modal unity + task unity” as its core design philosophy, achieving cross-modal understanding and generation capabilities for text, images, audio, and video. As the industry’s first open-source full-modal model at the 100B parameter scale, Ming-Flash-Omni achieves open-source SOTA performance across multiple benchmarks including image-text understanding, video analysis, speech synthesis, and image generation/editing.
Why Choose Ming?
Ming achieves breakthroughs in multiple dimensions:
- Full-Modal Unified Architecture: A single model end-to-end supports all four modalities — text, images, audio, and video — replacing multiple specialized models and significantly reducing system complexity
- Scaling Effect Validation: As the industry’s first open-source full-modal model at the hundred-billion parameter scale, it pioneers the validation of Model Scaling and Data Scaling effectiveness in the full-modal domain
- Seamless Integration of Generation and Understanding: Meta Query and Thinker-Talker architectures enable seamless transitions from understanding to generation, without either capability interfering with the other
Core Capabilities
Ming-Flash-Omni integrates four core capability modules, truly achieving “can see, can hear, can speak, can draw” full-modal intelligence:
Image-Text Understanding
- Knowledge Graph Enhancement: Introduces structured knowledge graphs for fine-grained visual perception and background knowledge fusion
- Multi-image Joint Understanding: Supports associative reasoning and comprehensive analysis across multiple images
- Subject Reasoning: Excellent performance on math, physics, and other specialized subject problems
Video Analysis
- Long Video Understanding: Effectively processes complex temporal information and understands video content semantics
- Video Grounding: Supports precise temporal event localization and segment retrieval in videos
- Dynamic Scene Perception: Understands action sequences, event logic, and entity relationships within videos
Speech Synthesis
- Thinker-Talker Architecture: Two-stage reasoning-based speech generation, significantly improving speech naturalness and expression accuracy
- Free-form Speech Editing: Supports addition, deletion, and modification of audio segments, emotion and style replacement, and dialect conversion
- 100+ Premium Voices: High-quality, copyright-protected voice library with multiple emotion variants
- Professional Content Reading: Accurately synthesizes speech for complex professional symbols such as chemical formulas and mathematical expressions
Image Generation and Editing
- Meta Query: Drives image generation through multimodal context feature extraction, integrating understanding and generation into a unified pipeline
- Fine-grained Image Editing: Supports local modification, style transfer, and content optimization of existing images
- Text-to-Image: Generates high-fidelity, high-consistency images from natural language descriptions
Technology Evolution
Ming-Flash-Omni has gone through multiple key milestones, evolving from architectural unification and scale expansion to data improvement — each version represents a major breakthrough in full-modal technology:
| Time | Version | Key Breakthrough |
|---|---|---|
| 2025.05 | Ming-Light Omni | Proposed the industry’s first full-modal unified architecture, validating the feasibility of unified modeling |
| 2025.10 | Ming-Flash Omni Preview | Reached hundred-billion parameter scale, first validation of Model Scaling effect in full-modal models |
| 2026.01 | Ming-Flash Omni 2.0 | Achieved open-source SOTA on multiple benchmarks through Data Scaling strategy |
This evolution reflects not only an expansion in supported modalities, but also continuous innovation in architectural philosophy: Unified architecture validation established the foundation for a single model handling multiple modalities; Model Scaling and Data Scaling together drive performance leaps; Future work will explore unified representation spaces, moving toward deeper cross-modal understanding and generation unification.
Use Cases
Ming-Flash-Omni is suitable for the following typical Use Cases:
| Scenario Category | Typical Applications |
|---|---|
| Multimodal Content Creation | Image-text mixed content generation, video script creation, intelligent illustration and asset production |
| Intelligent Video Analysis | Video content summarization, temporal event detection, video Q&A and retrieval |
| Voice Interaction Applications | Intelligent customer service, audio content production, personalized voice assistant |
| Cross-modal Retrieval and Generation | Image-to-text search, text-to-image generation, multimodal knowledge base Q&A |
| Professional Knowledge Processing | Subject formula recognition and parsing, multimodal understanding of professional documents |
Community Recognition
Ming-Flash-Omni has received widespread attention from academia and industry after open-sourcing:
- Reached #1 on Hugging Face Trending within one week of release
- Community evaluations show that the unified architecture design does not negatively impact individual modality performance, validating the “unified without compromise” design philosophy
- Has become an important reference benchmark for full-modal models in the open-source community
Technical Specifications
| Attribute | Specification |
|---|---|
| Model Name | Ming-Flash-Omni |
| Parameter Scale | Hundred-billion level (100B+) |
| Architecture | Unified multimodal MoE (Mixture of Experts) architecture |
| Supported Modalities | Text, Images, Audio, Video |
| Core Capabilities | Image-text understanding, video analysis, speech synthesis, image generation/editing |
| Training Strategy | Dynamic balanced training + Multi-router expert differentiation |
| Open Source License | See official repository |
Quick Start
Visit the Ling Studio to experience the multimodal capabilities of Ming-Flash-Omni.