InclusionAI API

Ling 3.0 Tiny is a mixture-of-experts model from InclusionAI, Ant Group's AI initiative, the smallest member of the Ling 3.0 family, with 7.9 billion total parameters and 1.3 billion active per token. It supports switchable Thinking and Instant modes, letting a caller trade reasoning depth against response speed, and a 262,144-token context window. InclusionAI positions it for lightweight, resource-sensitive workloads such as task agents, mobile and browser UI automation, code assistance, and other high-frequency, low-latency requests. This differs from its larger sibling Ling 3.0 Flash (5.1B active parameters, general-purpose use) and Ling 2.6 1T (63B active parameters, complex tasks with a 1M-token context). On Artificial Analysis's Intelligence Index, Ling 3.0 Tiny scores 25, above the median of 8 for models in its price tier, and ranks 6th out of 56 models in that class. It supports native function calling. This free-tier listing is offered through OpenRouter.

Ling 3.0 Flash is InclusionAI's (Ant Group) successor to Ling 2.6 Flash, a hybrid-reasoning Mixture-of-Experts model with 124B total parameters and about 5.1B active per token. It stacks five Kimi Delta Attention (KDA) layers per one Multi-Head Latent Attention (MLA) layer, combining efficient long-range memory with precise attention, and supports both thinking and non-thinking modes. According to InclusionAI, with roughly 1/8 of the total parameters and 1/12 of the active parameters of its 1T-parameter flagship model, Ling 3.0 Flash matches or beats that flagship on most of the benchmarks the company reported. It natively supports a 262K-token context window, with InclusionAI designing it to scale toward 1M context; the paid tier here is served at 131K context. It targets high-frequency agentic workflows such as coding agents, tool use, document processing, and long multi-turn conversations, where per-token cost and latency matter.

Ring 2.6 1T is a trillion-parameter open-weights reasoning model from InclusionAI (Ant Group), released under the MIT license. It uses a Mixture-of-Experts architecture with approximately 63B active parameters per token and supports a 262K context window with up to 66K output tokens. The model offers adaptive reasoning effort through "high" and "xhigh" modes, letting developers tune thinking depth against token cost based on task complexity. It is purpose-built for agentic workflows, coding agents, tool use, and long-horizon multi-step task execution. Ring 2.6 1T scores 95.83 on AIME 2026, 88.27 on GPQA Diamond, and 87.60 on PinchBench in agent mode — surpassing GPT-5.4 and Gemini 3.1 Pro on that benchmark. A strong pick for developers building autonomous agent systems or complex reasoning pipelines.

Ling 2.6 1T is InclusionAI's trillion-parameter flagship non-reasoning model, built by Ant Group's AGI initiative. It uses a Mixture-of-Experts architecture with approximately 50 billion active parameters per token, employing a "fast thinking" approach that reduces token costs to roughly a quarter of comparable models while maintaining top-tier output quality. The model targets advanced coding, complex reasoning, and large-scale agent workflows. It achieves state-of-the-art results on benchmarks like AIME 2025 and SWE-bench Verified, and ranks first among open-source models on ArtifactsBench for front-end code generation. On the Artificial Analysis Intelligence Index, it scores 34 — far above the median of 13 for comparable open-weight non-reasoning models. With a 262K context window and strong tool-use capabilities out of the box, Ling 2.6 1T is a strong fit for developers building autonomous agents or cost-sensitive pipelines that need flagship-level reasoning without a dedicated thinking model.

Ling 2.6 Flash is a high-efficiency open-weights instruct model from InclusionAI (Ant Group), featuring 104B total parameters with only 7.4B active via a Mixture-of-Experts architecture. It supports a 262K-token context window and is purpose-built for agentic workflows, coding, and document processing. The model scores 26 on the Artificial Analysis Intelligence Index — nearly double the median of 13 among comparable open-weight non-reasoning models, and a 10-point jump over its predecessor Ling-flash-2.0. It also achieves 59.3% on GPQA Diamond. Trained with Agentic Reinforcement Learning, Ling 2.6 Flash is optimized for tool use, terminal operations, and multi-step agent tasks while keeping token consumption notably low. A strong choice for developers building cost-sensitive agent pipelines or high-throughput automation that still demands capable reasoning and code generation.