Multi-Reference Input
Accepts between 1 and 10 reference images simultaneously via image URL arrays, extracting outlines, color tones, and lighting from each to inform generation.
Kling Image O1, formally known as Kling Omni Image O1, is an image generation model developed by Kuaishou Technology, the company behind the Kling AI ecosystem. It is built on a Multimodal Visual Language (MVL) framework that combines natural language understanding with multi-reference image processing, allowing it to accept between 1 and 10 reference images simultaneously and extract consistent visual features across all outputs. The model was trained through December 2025 and supports a context window of 10,000 tokens. The model is designed to address a common challenge in AI image generation: maintaining consistent character identity, style, and visual detail across multiple generated images. It is particularly suited for workflows such as IP character design, comic and manga creation, brand merchandise imagery, and serialized visual content where cross-image consistency is a requirement. Inputs include image URL arrays alongside select and toggle controls, giving users structured options for guiding generation behavior.
High-signal model metadata in a structured two-column overview table.
The entity that provides this model.
The number of tokens supported by the input context window.
The number of tokens that can be generated by the model in a single request.
Whether the model's code is available for public use.
When the model was first released.
When the model's knowledge was last updated.
The providers that offer this model. This is not an exhaustive list.
Types of data this model can process.
A fuller summary of positioning, capabilities, and source-specific details for Kling Image O1.
Kling Image O1, formally known as Kling Omni Image O1, is an image generation model developed by Kuaishou Technology, the company behind the Kling AI ecosystem. It is built on a Multimodal Visual Language (MVL) framework that combines natural language understanding with multi-reference image processing, allowing it to accept between 1 and 10 reference images simultaneously and extract consistent visual features across all outputs. The model was trained through December 2025 and supports a context window of 10,000 tokens.
The model is designed to address a common challenge in AI image generation: maintaining consistent character identity, style, and visual detail across multiple generated images. It is particularly suited for workflows such as IP character design, comic and manga creation, brand merchandise imagery, and serialized visual content where cross-image consistency is a requirement. Inputs include image URL arrays alongside select and toggle controls, giving users structured options for guiding generation behavior.
Accepts between 1 and 10 reference images simultaneously via image URL arrays, extracting outlines, color tones, and lighting from each to inform generation.
Preserves subject identity across multiple generated images, maintaining recognizable features of characters or objects from one output to the next.
Sustains a coherent visual aesthetic and tone across an entire project, suitable for brand systems, comic series, and marketing campaigns.
Allows specific elements to be added, removed, or modified through natural language instructions without disrupting the surrounding style or texture.
Exposes select and toggle group inputs so users can control generation parameters such as aspect ratio or output mode directly at the API level.
Uses a Multimodal Visual Language framework to interpret complex creative text prompts alongside visual references within a 10,000-token context window.
Primary API pricing shown in the same “quick compare” spirit as the reference page.
Places where this model is available, based on the synced detail-page metadata.
The configurable options currently documented for this model.
Provide up to 10 references images of the scene, subject, objects, or anything else in the image.
Parameters currently listed by OpenRouter or the local catalog for this model.
Official model cards, release notes, docs, and other references synced from the source page.
Kling Image O1 discussions are most active in r/lmarena. The strongest match in this snapshot has 12 upvotes and 16 comments.
There are currently 481 models listed on the [arena.ai](http://arena.ai) website.
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Unfortunately, the list of models available for selection in direct and side-by-side mode is much smaller :(
The model supports between 1 and 10 reference images simultaneously, supplied as an array of image URLs.
The model has a context window of 10,000 tokens, which covers both the text prompt and associated image reference metadata.
According to the model metadata, the training date is listed as December 2025.
The model accepts image URL arrays, select inputs, and toggle group inputs, allowing structured control over generation behavior alongside visual references.
Kling Image O1 was developed by Kuaishou Technology, the company behind the broader Kling AI ecosystem.
Continue browsing adjacent models from the same provider.