InclusionAI (Ant Group)

Ling 3.1 Flash

InclusionAI (Ant Group)

Ling 3.1 Flash is InclusionAI’s hosted hybrid reasoning language model for coding, multi-step analysis, and agents that call tools. Its mixture-of-experts network has 560B total parameters and activates 25B per token. The verified September 30 availability announcement provides a 262K context through Vercel AI Gateway, suitable for long documents, repository analysis, and extended task histories. Standard and temporary free API identifiers represent the same model. Public weights were not verified at the audit date, so hosted availability is recorded without assuming a downloadable license or checkpoint.

LLaDA-UI

InclusionAI (Ant Group)

LLaDA-UI brings block-wise diffusion generation to a vision-language GUI model. The approximately 16.7B MoE model preserves screenshot aspect ratios and visual detail, reasons about natural-language tasks, and produces grounded coordinates or tagged reasoning and structured actions for mobile, desktop, and web interfaces. The September 9 technical report precedes the September 15 weight publication, independently of the repository creation timestamp. Public weights and serving examples are available; the exact weight license is not declared in the current card and is not inferred from neighboring LLaDA models.

Ling-3.0-Flash-VL

InclusionAI (Ant Group)

Ling-3.0-Flash-VL extends the Ling3.0-Flash language backbone with native image and video understanding for visual reasoning and agent execution. A ViT encoder, MLP projector, VideoRoPE, and hybrid KDA/gated MLA backbone combine visual evidence with planning, action, and verification. The 124B model activates 5.5B parameters and supports up to 256K context through the documented deployment extension. The September 4 official changelog establishes public chat/API access before the subsequent open-weight promotion. MIT weights enable local visual assistants and document or video analysis.

UI-Venus-2

InclusionAI (Ant Group)

UI-Venus-2 is a distinct vision-language model specialization for GUI interaction, combining screenshot understanding with natural-language task reasoning and structured actions. The second generation adds multilingual mobile, browser, and desktop environments, keypoint-grounded verification, and reflection-based recovery. The released 9B checkpoint is initialized from Qwen3.5-9B; a 27B tier is described in research evaluations, but its weights are not verified. The current model card explicitly leaves its weight license unresolved. Local inference requires an external action parser and execution framework; the model itself emits language reasoning and actions.

Ling-3.0-Tiny

InclusionAI (Ant Group)

Ling-3.0-Tiny is a native hybrid reasoning model using Kimi Delta Attention and gated Multi-head Latent Attention in a 3:1 layer pattern. Sparse experts support coding, knowledge work, mathematical reasoning, and tool-based agents, with thinking configurable per request. The Tiny tier targets local and edge use with 7.9B total parameters; the card documents Apple Silicon and DGX Spark deployment. Public API availability precedes the later August weight publication, as established by the official changelog. MIT weights, routine base checkpoints, and quantized distributions are grouped under this tier.

Ling-3.0-Flash-Fin

InclusionAI (Ant Group)

Ling-3.0-Flash-Fin is Ant Group’s finance-enhanced version of Ling3.0-Flash, developed through continued training with financial institutions and domain experts. It combines source-grounded search, multi-document reasoning, valuation modeling, spreadsheet workflows, and report preparation in long-horizon agents. The 124B model activates 5.1B parameters and supports 256K context. The August announcement predates the September weight publication; the exact first public serving day is not asserted here. MIT weights are now downloadable. Model outputs require review of financial assumptions, calculations, sources, and conclusions.

Ling-3.0-Flash

InclusionAI (Ant Group)

Ling-3.0-Flash is a native hybrid reasoning model using Kimi Delta Attention and gated Multi-head Latent Attention in a 5:1 layer pattern. Sparse experts support coding, knowledge work, mathematical reasoning, and tool-based agents, with thinking configurable per request. The Flash tier targets production agent efficiency with 124B total parameters. Public API availability precedes the later August weight publication, as established by the official changelog. MIT weights, routine base checkpoints, and quantized distributions are grouped under this tier.

LLaDA2.2

InclusionAI (Ant Group)

LLaDA2.2 is Ant Group’s MoE diffusion-language model family, generating text through block-parallel refinement rather than exclusively left-to-right decoding. Levenshtein editing introduces DELETE and INSERT tokens for structural correction, while block routing and agentic reinforcement learning support 128K tool use and multi-turn interaction. The 100B Flash checkpoint was followed by the 16B Mini release in September. Public Apache-2.0 weights and custom inference code support research and self-hosting. Performance and latency depend on diffusion settings and the serving implementation.

Ring-2.6-1T

InclusionAI (Ant Group)

Ring-2.6-1T is Ant Group’s thinking flagship for demanding reasoning and sustained agent workflows. The trillion-parameter hybrid-attention MoE model supports adjustable high and xhigh reasoning effort, allowing developers to select between routine multi-turn agents and more expensive mathematical or scientific reasoning. Asynchronous reinforcement learning and IcePop improve training stability and long-horizon execution. The official May 9 launch precedes the May 15 weight/API announcement. MIT weights are public; native 128K context extends to 256K with YaRN, and tool execution requires an external agent environment.

Ling-2.6-1T

InclusionAI (Ant Group)

Ling-2.6-1T is Ant Group’s general flagship for coding, long-document tasks, and multi-step agents. A hybrid MLA/linear-attention trillion-parameter MoE model targets efficient inference and concise fast thinking. Process-redundancy suppression during post-training lowers unnecessary output tokens, while agentic training improves tool calling, coding, and instruction execution. The April 29 official changelog establishes API and chat availability before the April 30 open-weight announcement. Public MIT weights support self-hosting; the model card demonstrates 256K deployment, while hosted services can expose different limits.

LLaDA2.0-Uni

InclusionAI (Ant Group)

LLaDA2.0-Uni unifies visual understanding, image generation, and instruction-based editing within a diffusion language model. Built from LLaDA2.0-Mini, its MoE backbone predicts masked semantic tokens, while SigLIP-VQ encodes images and a diffusion decoder reconstructs visual outputs. It supports visual question answering, image captioning, document understanding, and preliminary interleaved reasoning and generation. Apache-2.0 weights and code were released April 23, with FP8 and additional serving integrations arriving later. Its language-based visual understanding places it within the multimodal language-model scope.

Ling-2.6-Flash

InclusionAI (Ant Group)

Ling-2.6-Flash is Ant Group’s efficient instruction-following tier, designed for faster responses and lower token overhead in everyday agents. The 104B MoE network activates 7.4B parameters, combining MLA and Lightning Linear Attention in a 1:7 ratio. Post-training focuses on concise execution, coding, planning, and tool use rather than long visible reasoning. The first-party April 22 changelog documents public API and chat availability, preceding the later weight upload. MIT weights enable local serving, with a native 128K context and demonstrated YaRN extension to 256K.

Ling-2.5-1T

InclusionAI (Ant Group)

Ling-2.5-1T updates Ant Group’s trillion-parameter language family with hybrid linear attention for efficient long contexts. It combines Multi-head Latent Attention and Lightning Linear Attention in a 1:7 ratio with sparse experts. This instant-model tier targets concise reasoning, preference alignment, creative writing, and native agent interaction, with a native 256K context extendable to one million tokens. Both downloadable MIT weights and historical hosted access are documented. The first-party 2.5 APIs were retired September 30; this page preserves the released model identity.

Ring-2.5-1T

InclusionAI (Ant Group)

Ring-2.5-1T updates Ant Group’s trillion-parameter language family with hybrid linear attention for efficient long contexts. It combines Multi-head Latent Attention and Lightning Linear Attention in a 1:7 ratio with sparse experts. This thinking tier strengthens deep reasoning and sustained agent execution through fully asynchronous agentic reinforcement learning, with a native 128K context extendable to 256K. Both downloadable MIT weights and historical hosted access are documented. The first-party 2.5 APIs were retired September 30; this page preserves the released model identity.

Ming-Flash-Omni 2.0

InclusionAI (Ant Group)

Ming-Flash-Omni2.0 is Ant Group’s language-capable any-to-any multimodal model, combining text, image, audio, and video understanding with text, image, and audio generation. Its Ling2.0-based MoE backbone has 100B total and 6B active parameters, with modality-specific encoders and generation components. The February 11 official release improves visual encyclopedic knowledge, natural speech, and image synthesis and editing. MIT weights and inference code are downloadable. An earlier Flash-Omni preview is grouped as a historical variant, while distinct Lite models have different sizes and specifications.

LLaDA2.1

InclusionAI (Ant Group)

LLaDA2.1 is Ant Group’s MoE diffusion-language model family, generating text through block-parallel refinement rather than exclusively left-to-right decoding. Token editing adds error-correcting generation and Speed/Quality modes, balancing parallel decoding speed against answer quality in 100B Flash and 16B Mini checkpoints. Public Apache-2.0 weights and custom inference code support research and self-hosting. Performance and latency depend on diffusion settings and the serving implementation.

UI-Venus-1.5

InclusionAI (Ant Group)

UI-Venus-1.5 is a distinct vision-language model specialization for GUI interaction, combining screenshot understanding with natural-language task reasoning and structured actions. The 1.5 release offers 2B, 8B, and 30B-A3B checkpoints for grounding and end-to-end mobile agents, with Apache-2.0 weights. Local inference requires an external action parser and execution framework; the model itself emits language reasoning and actions.

LLaDA2.0

InclusionAI (Ant Group)

LLaDA2.0 is Ant Group’s MoE diffusion-language model family, generating text through block-parallel refinement rather than exclusively left-to-right decoding. The release scales diffusion language modeling to a 100B Flash tier and a 16B Mini tier, with instruction-following and code-generation checkpoints and confidence-aware parallel variants. Public Apache-2.0 weights and custom inference code support research and self-hosting. Performance and latency depend on diffusion settings and the serving implementation.

Ring-1T

InclusionAI (Ant Group)

Ring-1T is Ant Group’s trillion-parameter thinking model, trained from Ling-1T-base through verifiable-reward reinforcement learning and human-feedback alignment. The IcePop stabilization method supports large-scale reinforcement learning, while extended natural-language reasoning targets mathematics, coding, and scientific tasks. Public MIT weights are available with a native 64K context extendable to 128K through YaRN. The original first-party API was retired in June 2026. Earlier preview work is distinct from the October 14 balanced final release recorded in the official announcement.

Ling-1T

InclusionAI (Ant Group)

Ling-1T is the non-thinking flagship of Ant Group’s Ling 2.0 language-model series. A trillion-parameter MoE model activates approximately 50B parameters and uses evolutionary chain-of-thought training to improve efficient reasoning without long visible thinking traces. The family combines code generation, writing, instruction following, and tool calling with public MIT weights. Its original first-party API was retired in June 2026 in favor of Ling2.6; downloadable named checkpoints preserve historical access. The October 9 product changelog establishes public availability independently of earlier private repository uploads.

Ming-UniVision

InclusionAI (Ant Group)

Ming-UniVision is a unified vision-language model that answers visual questions, describes images, generates images, and performs iterative image editing. A continuous MingTok vision tokenizer connects image understanding and generation with a single autoregressive next-token-prediction framework. The released 16B-A3B MoE checkpoint supports multi-round conversations that alternate between questions and editing requests. Public Apache-2.0 weights and code enable research and local inference. The October technical report establishes publication; the precise first public weight-release day is not asserted.

Ming-Lite-Omni

InclusionAI (Ant Group)

Ming-Lite-Omni is Ant Group’s compact omnimodal language-model family, combining image, text, video, and speech understanding with text, speech, and image generation. The public preview was released May 4, followed by the May 28 official checkpoint and July 15 v1.5 update. The v1.5 model has 20.3B total parameters with 3B active in its MoE backbone, adding stronger video comprehension, refined image editing, and improved speech synthesis. MIT weights and model-specific inference code enable self-hosting. This page groups the original Lite sequence; the substantially larger Flash-Omni2.0 tier has a separate page.