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Introduction

Introduction

At Ant Ling, we build frontier models and make advanced intelligence publicly accessible. We believe AGI should move toward openness, sharing, and scalability—grounded in small, iterative steps that support steady evolution and real-world deployment.

Try Online

Explore our model’s core capabilities, from multi-turn reasoning and long-context processing to multimodal generation. Try it now at Ling Studio .

API Access

Prerequisites

Register an account and get an API Key to complete the environment setup before your first API call.

Go to Prerequisites →

Make Your First Call

Use OpenAI-compatible SDK or HTTP requests to complete your first conversation within 5 minutes.

Go to Quickstart →

Model Family

MoE architecture general-purpose LLM series, balancing high parameter scale with low activation cost. Suitable for conversation, text generation, and content creation.

Ling (General)

Specializes in deep reasoning and cognitive capabilities, achieving open-source leading performance in math, programming, and comprehensive reasoning benchmarks.

Ring (Reasoning)

Unified multimodal architecture supporting cross-modal understanding and generation of text, images, audio, and video.

Ming (Multimodal)

View all models and pricing →

Explore Documentation

Ling / Ring / Ming three series, including model selection guide, parameter comparison, and pricing.

Model Family

API usage guide, including OpenAI and Anthropic API compatible request formats.

API Reference

Guides for advanced features including streaming, structured outputs, vision, and voice.

Tutorials

Integration guides for 4 tools including Claude Code, OpenClaw, Kilo Code and Hermes Agent.

Integrations

Use Ant Ling models through aggregation platforms like OpenRouter and Zenmux.

Third-Party Platforms

Why Choose Ant Ling

  • Open Source: Research results open-sourced to the Inclusion AI  community
  • Professional: Deep optimization for financial, medical, and other professional domains
  • Full Stack: From lightweight models to trillion-parameter flagship models, covering all scenarios
  • Evolving: Listening to user feedback to drive continuous model capability improvement
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Prerequisites