inclusionAI: Ling-3.1-flash

inclusionAI: Ming Image 0.1 Design

Ming Image 0.1 Design is a text-to-image model from inclusionAI aimed at graphic-design output, with an emphasis on legible text rendering inside the generated image. It generates from a prompt only and does not accept reference images. Output format can be requested as PNG, JPEG, or WebP. Image dimensions are chosen by the model rather than by the request, so explicit sizes and aspect ratios are rejected instead of silently reshaped.

inclusionAI: Ming Image 0.1 Design Layer

Ming Image 0.1 Design Layer is an image-to-image model from inclusionAI that decomposes a flattened design image into separate RGBA layers, such as a background layer and foreground elements, and returns one image per layer. It requires exactly one reference image and a prompt describing the layer plan. Output format can be requested as PNG or WebP. Output dimensions follow the input image, so explicit sizes and aspect ratios are rejected instead of silently reshaped.

inclusionAI: Ling-3.0-flash-VL

Ling 3.0 Flash VL builds on Ling 3.0 Flash (124B total / 5.5B active MoE from InclusionAI), further strengthening its language capabilities while adding native visual perception and advanced visual agent capabilities. Hybrid instant/reasoning model with tool calling.

inclusionAI: Ling-3.0-flash-Sante

Ling-3.0-Flash-Sante is a Mixture-of-Experts (MoE) model with enhanced capabilities across health and medicine. Built on Ling-3.0-Flash, it has 124 billion total parameters and activates approximately 5.1 billion parameters per token. The model excels in medical knowledge reasoning, clinical safety, evidence-based retrieval, and long-horizon medical tasks, while retaining strong general capabilities in reasoning, coding, and agentic tasks.

inclusionAI: Ling-3.0-flash-Fin

Ling-3.0-flash-Fin is a finance-enhanced MoE model built on Ling-3.0-flash, with 124 billion total parameters and approximately 5.1 billion activated parameters. Designed for real-world investment workflows, it is optimized for complex multi-step tasks and long-horizon planning and execution. With a relatively small active parameter footprint, it delivers competitive financial performance while maintaining strong general capabilities in reasoning, coding, and mathematics.

inclusionAI: Ling-3.0-flash

Ling-3.0-flash is the latest-generation cost-effective model in the Ling series, with 124B total parameters, 5.1B activated parameters, a native 256K context window extendable up to 1M. Compared to the previous flash model, Ling-3.0-flash delivers significantly enhanced stability in long-horizon tasks, markedly higher tool calling accuracy, and substantially improved compatibility with common Harness environments. In coding tasks, Ling-3.0-flash features greatly optimized spatial understanding, capable of constructing physical scene grids and relative positions. Additionally, thanks to its hybrid Reasoning mode, task success rates across varying difficulty levels have all improved.