Blog

Explore the latest research results and technical insights from Ant Ling Foundation Model

2026.4.23
Model Release

Ling-2.6-flash Release: Faster Response, Stronger Execution, Higher Token Efficiency

Ant Ling has officially launched Ling-2.6-flash—a instruction model with a total parameter count of 10.4 billion and 7.4 billion activated parameters. Faced with ever-increasing token demands, Ling-2.6-flash has chosen a different technical path: rather than simply relying on longer outputs to achieve higher scores, it adopts a systematic optimization approach centered on inference efficiency, token efficiency, and agent scenario performance. While maintaining a competitive level of intelligence, this model strives to be faster, more resource-efficient, and better suited to real-world business scenarios.

2026.4.1
landscape

Taking the Pulse of Agentic AI from the Developer Community at the End of Q1 2026

2026.3.4
Release

Ming-Omni-TTS: Simple and Efficient Unified Generation of Speech, Music, and Sound with Precise Control

2025.12.17
Insights

The Community Stories of vLLM and SGLang

2025.10.28
Release

Ming-flash-omni-Preview: A Sparse, Unified Architecture for Multimodal Perception and Generation

2025.10.11
Landscape

Open Source LLM Development Landscape 2.0: 2025 Revisited

2025.10.1
Release

Ming-UniAudio: Speech LLM for Joint Understanding, Generation and Editing with Unified Representation

2025.10.1
Release

Ming-UniVision: Joint Image Understanding and Generation via a Unified Continuous Tokenizer

2025.9.13
Insights

Segmentation-as-Editing for Unified Multimodal AI

2025.8.5
Release

Introducing Ring-lite-2507

2025.7.21
Release

Introducing Ming-Lite-Omni V1.5

2025.7.8
Release

ABench: An Evolving Open-Source Benchmark

2025.4.1
Release

Agentic Learning

2025.4.1
Release

AReaL: Ant Reasoning Reinforcement Learning for LLMs

2025.4.1
release

In RL we trust — AReaL v0.2 (Boba) Release

2025.7.7
Release

AWorld: The Agent Runtime for Self-Improvement

2025.5.8
Release

Ling: A MoE LLM Provided and Open-sourced by InclusionAI

2025.6.11
Landscape

Open Source LLM Development 2025: Landscape, Trends and Insights

2025.7.11
Release

M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning

2025.5.5
Release

Ming-Lite-Omni-Preview: A MoE Model Designed to Perceive a Wide Range of Modalities

2025.5.7
Release

Ming-Lite-Uni: Advancements in Unified Architecture for Natural Multimodal Interaction

2025.6.11
Release

Ming-Omni: A Unified Multimodal Model for Perception and Generation

2025.4.1
Release

PromptCoT & PromptCoT-Mamba: Advancing the Frontiers of Reasoning

2025.4.1
Release

Ring: A Reasoning MoE LLM Provided and Open-sourced by InclusionAI

2026.2.14
Tutorials

Using Ring 1T with Claude Code via ZenMux