Ling AI Model Family

Overview

The Ling AI Model Family, developed by Ant Group (inclusionAI), reached a new milestone on April 22, 2026, with the official launch of Ling-2.6-flash. This model, previously tested under the codename "Elephant Alpha", represents the state-of-the-art in efficient, high-intelligence AI for the global development community. The family is divided into three specialized series:

• Ling (Efficiency): Sparse MoE language models optimized for speed and accuracy.
• Ring (Reasoning): Advanced "thinking" models with explicit chain-of-thought pathways.
• Ming (Multimodal): Native omnimodal systems for text, image, audio, and video.

All models in the family are released under the MIT license, reinforcing Ant Group's commitment to the open-source AI ecosystem. Ling-2.6-flash, in particular, sets a new benchmark for cost-efficiency, offering GPT-5 class intelligence at a fraction of the cost.

Model Family

Ling Series (Language & Efficiency)

• Ling-2.6-flash: The latest high-efficiency flagship (released April 2026). 104B total / 7.4B active parameters.
• Ling-1T: Original trillion-parameter sparse MoE (released October 2025).
• Ling-mini/lite: Optimized compact versions for edge and high-throughput use.

Ring Series (Reasoning & Thinking)

• Ring-1T: World's first open-source trillion-parameter reasoning model.
• Ring-flash-2.6: Reasoning-optimized variant with linear complexity support.

Ming Series (Multimodal & Perception)

• Ming-flash-omni: Native multimodal processing for text, image, audio, and video.
• Ming-UniVision: Specialized for advanced visual understanding and spatial reasoning.

Experimental Models

• LLaDA-MoE: Experimental architecture exploring novel MoE designs

Capabilities

The Ling AI Model Family provides comprehensive capabilities across all model series:

Language Processing (Ling Series)

• Flagship-Level Efficient Reasoning: Ling-1T extends the Pareto frontier of reasoning accuracy vs. length
• Advanced Code Generation: State-of-the-art performance in code generation and software development
• Aesthetic Understanding: Excels in visual reasoning and front-end code generation (#1 on ArtifactsBench)
• High Throughput: Flash variants optimized for rapid, efficient processing
• Resource Flexibility: Models from trillion-scale to edge deployment

Advanced Reasoning (Ring Series)

• Explicit Thinking Mode: Ring models feature advanced reasoning with visible thought processes
• World's First Open-Source Trillion-Parameter Reasoning Model: Ring-1T-preview (September 2025)
• Linear Complexity: Ring-linear variants for extended context reasoning
• Multi-Step Problem Solving: Strong performance on complex logical and mathematical tasks
• Efficient Reasoning Pathways: Optimized thinking modes across model sizes

Multimodal Processing (Ming Series)

• Omnimodal Understanding: Process text, images, audio, and video simultaneously
• Specialized Modalities: Dedicated audio (UniAudio) and vision (UniVision) models
• Cross-Modal Reasoning: Advanced understanding across different input types
• Flexible Deployment: From high-performance omni models to lightweight variants

Shared Capabilities

• Emergent Intelligence: Strong transfer capabilities at trillion-scale (approximately 70% tool-call accuracy with minimal training)
• Natural Language Understanding: Interprets complex instructions across all model types
• Cross-Platform Compatibility: Generates compatible code for multiple platforms
• Multilingual Support: Stylistically controlled content in multiple languages
• Open Source: All models available under MIT license

Ling-1T: Flagship Model

Ling-1T is the flagship non-thinking model featuring 1 trillion total parameters with approximately 50 billion active parameters per token. Pre-trained on over 20 trillion high-quality, reasoning-dense tokens, it demonstrates state-of-the-art performance on complex reasoning benchmarks while maintaining exceptional efficiency through its innovative MoE architecture and evolutionary chain-of-thought optimization.

Technical Specifications (Ling-2.6-flash)

• Architecture: Sparse Mixture-of-Experts (MoE) based on Ling 2.0.
• Parameters: 104 billion total; 7.4 billion active per token.
• Context Window: 256K tokens (262,144).
• Max Output: 32,768 tokens.
• Training: Largest known foundation model trained entirely with FP8 mixed-precision for 15% end-to-end speedup.
• Reasoning: Evolutionary Chain-of-Thought (Evo-CoT) for progressive logic enhancement.
• Knowledge Cutoff: January 2026.

Use Cases

Ling-1T's efficient architecture makes it ideal for diverse applications:

• Software Development: High-quality code generation, debugging, and software architecture design
• Front-End Design: Creating aesthetically pleasing, functional user interfaces with visual reasoning
• Visual Reasoning: Complex visual understanding and design system implementation
• Mathematical Problem-Solving: Competition-level mathematics and complex calculations
• Tool Usage & Agents: Building AI agents with strong tool-call capabilities (70% accuracy on BFCL V3)
• Marketing & Content: Generating stylistically controlled, aesthetically refined marketing materials
• Cross-Platform Development: Creating compatible code for multiple platforms and frameworks
• Research & Analysis: Efficient processing of complex reasoning tasks with optimal token usage

Performance Metrics

Ling-1T demonstrates exceptional performance across multiple benchmarks:

• AIME 2025: Extends Pareto frontier of reasoning accuracy vs. reasoning length, showcasing efficient thinking
• ArtifactsBench: #1 ranking among open-source models for front-end generation and aesthetic understanding
• BFCL V3: Approximately 70% tool-call accuracy with minimal instruction tuning (no large-scale trajectory data)
• Code Generation: State-of-the-art performance in software development tasks
• Knowledge Benchmarks: Comprehensive evaluation across knowledge, code, math, reasoning, agent, and alignment tasks
• Efficiency: 15%+ end-to-end speedup with FP8 training, 40%+ utilization improvement with heterogeneous pipeline

Limitations

While the Ling AI Model Family has made strong progress, several considerations apply:

Ling Series Limitations

• Attention Mechanism: GQA-based attention is stable for long-context but relatively costly; future versions will adopt hybrid attention
• Agentic Capabilities: Room to grow in multi-turn interaction, long-term memory, and advanced tool use
• Instruction Alignment: Occasional deviations or role confusion may occur; ongoing improvements in alignment
• Non-Thinking Model: Designed for efficient reasoning without explicit thinking mode; for deep reasoning chains, use Ring series

Model Selection Guidance

• For explicit reasoning: Use Ring series (thinking models) instead of Ling series
• For multimodal tasks: Use Ming series for text, image, audio, or video processing
• For resource constraints: Consider lite variants across all series
• For maximum performance: Flagship models (Ling-1T, Ring-1T) require substantial compute resources

Safety & Alignment

Ling-1T incorporates comprehensive safety measures:

• Open Source Safety: Full transparency through MIT license and open model weights
• Alignment Optimization: LPO (Linguistics-Unit Policy Optimization) for precise reward-behavior alignment
• Training Stability: Superior training stability and generalization across reasoning tasks
• Evo-CoT Safety: Progressive reasoning enhancement under controllable cost constraints
• Community Oversight: Open-source model enabling community review and safety improvements

Innovation Highlights

Ling Scaling Law

The Ling 2.0 architecture was designed from the ground up using the Ling Scaling Law (arXiv:2507.17702), ensuring architectural and hyperparameter scalability even under 1e25–1e26 FLOPs of compute.

FP8 Mixed-Precision Training

Ling-1T is the largest known FP8-trained foundation model, achieving:

• 15%+ end-to-end speedup
• Improved memory efficiency
• Less than or equal to 0.1% loss deviation from BF16 across 1T tokens

Evolutionary Chain-of-Thought (Evo-CoT)

Built upon mid-training reasoning activation, Evo-CoT provides:

• Progressive reasoning enhancement under controllable cost
• Continual expansion of Pareto frontier (accuracy vs. efficiency)
• Ideal optimization for reflexive non-thinking models

Hybrid Reward Mechanism

The Syntax–Function–Aesthetics reward system enables:

• Correct and functional code generation
• Refined visual aesthetic understanding
• Superior performance on front-end generation tasks

Pricing & Access

Ling-2.6-flash offers industry-leading cost-efficiency for global developers:

API Pricing (per 1M Tokens)

• Input: $0.10
• Output: $0.30

Access Options

• Developer API: Available through the Alipay Tbox and major AI marketplaces.
• Open Source: Full model weights are available on Hugging Face and ModelScope under the MIT license.
• Self-Hosting: Optimized for deployment with vLLM and SGLang on standard workstation hardware.

Model Variants

• Ling Series: Access via inclusionai/ling-* model names
• Ring Series: Access via inclusionai/ring-* model names
• Ming Series: Access via inclusionai/ming-* model names

Ecosystem & Tools

The Ling AI Model Family is well-supported across deployment platforms:

• Hugging Face: Primary hub for all model weights and documentation
• ModelScope: Chinese platform for faster downloads
• vLLM: High-performance inference engine support for all series
• SGLang: Structured generation language support
• Custom Deployment: Full support for local and private cloud deployment across all models

Community & Resources

• Ant Group Press Release - Official announcement
• inclusionAI on Hugging Face - Complete model family and downloads
• Ling-1T on Hugging Face - Flagship model page
• Ling Scaling Law Paper - Architecture design principles
• WSM Scheduler Paper - Training methodology