What superpower does Kimi-K2.5 bring to the table?

Kimi-K2.5 has arrived, and it has immediately claimed its place among the elite tier of large language models. Its performance is on par with or even surpasses top-tier large models such as GPT-5.2 (xhigh), Claude Opus 4.5, and Gemini 3 Pro in key benchmark tests.The official technical documentation highlights its robust capabilities across agents, vision, and code generation.

This release marks not merely an incremental update, but Moonshot AI's decisive entry into the top echelon of multimodal AI systems—delivering a versatile, high-performance assistant that rivals the best offerings from OpenAI, Google, and Anthropic.

So, what's Kimi-K2.5's superpower? Let's take a look👇

1. Starting From a Simple Task

K2.5 emphasizes the joint optimization of text and vision, enabling the two modalities to mutually enhance each other. Here, we first present a task of understanding image and text.

1.1 Example: Medicine Counting Task

● Prompt: Find out how many medicines there are in total.

● Input:

● Output:

1.2 Analysis

K2.5 was designed as a model that can both see images and understand text. Its training process consists of three stages.

● Stage 1: Native Multimodal Pre-Training

You can think of the stage as raising a newborn baby. The goal here is to help the model develop an initial understanding of the world—what images are, what text is, and how they relate to each other. It first learns the most basic concepts, just like a child learning to recognize "cats", "people", or the color "red".

● Stage 2: Zero-Vision SFT (Zero-Vision Supervised Fine-Tuning)

SFT is like a teacher guiding a student step by step through practicing problems. Normally, to train a model that can understand images, you need a large amount of paired data in the format: image + question → correct answer. However, such data is difficult and expensive to collect, and its limited diversity can restrict the model's capabilities.

K2.5 introduces a new idea. At the stage, no images are provided. The model is trained using only pure text data. This may sound strange — how can a model improve its visual ability without seeing images?

The key lies in how it interacts with images internally. Instead of directly consuming image-answer pairs, the model learns to operate on images programmatically with Python—for example, reading pixel values, counting objects, performing binarization, and other code-based image manipulations. The model learns how to write correct code and achieves stronger generalization, rather than memorizing visual answers.

● Stage 3: Joint Multimodal Reinforcement Learning

The final stage further optimizes the model, making it more stable and reliable when incorporating visual information into its reasoning process. It is like a student who already knows how to solve problems participating in real exams—gaining practical experience and becoming more mature and robust. Force the model to 'truly look at the picture' and transform visual understanding from 'optional' to 'mandatory'.

During the inferring process, Kimi generates Python code and executes it internally to obtain the result.

2. Understanding Image-Text and Generating Slides

Building upon its strong foundation in multimodal understanding, K2.5 extends its capabilities to practical content creation scenarios. The model excels at interpreting complex visual information from images while simultaneously processing textual context, enabling it to generate well-structured, visually coherent presentation slides. This demonstrates K2.5's ability to bridge comprehension and generation—transforming raw image-text inputs into organized, presentation-ready outputs that maintain logical flow and visual consistency.

2.1 Example: Urban Renewal Project (Comparison with top LLM)

● Prompt: Based on this picture, think about how to transform it into a modern public facility that meets the needs of the Z-generation, and generate a design sketch and a brief slide outline for the product plan.

● Input:

● Output:

- ChatGPT

Only generate this picture.

- Gemini

Only generate this picture.

- Claude

Claude first analyzes the specific content, and then calls the tool to generate slides.

- Kimi

Kimi has a preliminary understanding of the images and creates a reasonable overall logic for the design of the slides, providing rich creative information and reference images and the website of "kimi slides" to generate slides.

2.2 Analysis

● General semantic understanding

Kimi doesn't just "look and label". It matches image information with text information, so it can understand the image while referencing language knowledge and generate more suitable responses. Next, it transforms combined information into higher-level concepts, enabling the model to go from "seeing the surface" to "grasping the deeper meaning".

● Chain-of-Thought reasoning

Kimi also has Chain-of-Thought reasoning ability, meaning it processes multimodal information step by step. Instead of giving a conclusion all at once, it first analyzes of the image, then gradually integrates language context to reach the final reasoning. This makes the process more transparent and interpretable and better at handling complex and multi-step tasks.

3. Generating Web Prototypes by Understanding Web Design

Extending its multimodal capabilities to the domain of interface development, K2.5 leverages agentic execution to transform visual design comprehension into functional web implementations. Acting as an autonomous interface developer, the model analyzes web design references—whether screenshots, wireframes,or mockups—then iteratively plans, codes, and refines responsive prototypes. This agent-driven workflow enables K2.5 to bridge design intent and technical execution independently, orchestrating the full pipeline from visual parsing to interactive deployment while preserving aesthetic coherence and usability constraints.

3.1 Example: Going Places Travel (Comparison with top LLM)

● Prompt:

Generate the same webpage html based on this image.

● Input:

● Output:

- ChatGPT

- Gemini

- Claude

- Kimi

3.2 Analysis

When it comes to generating webpages, different AI models have very different styles.

● ChatGPT can sketch out the overall structure and content of a page, giving users a draft of what goes where, but the HTML details and icons often need manual tweaking.

● Gemini, using the same prompt, generates a static image of the page, more like a designer's sketch you can look at but can't use directly.

● Claude can set up a basic HTML framework like a junior builder setting up the frame.

● The freshly released Kimi-K2.5 reconstructs webpage code with high accuracy, handling icons, buttons, styles, and text correctly. It's like a skilled web craftsman placing every piece in the right spot, producing code that's ready to go live.

4. Summary

Kimi-K2.5 is inherently a vision-language model (VLM). Typically, when companies release a VLM, they append "-VL" to the model name to indicate its multimodal capability. However, Kimi does not follow this convention. This suggests that Chinese companies are beginning to seriously develop multimodal models without explicitly labeling them with a "-VL" suffix, making visual understanding a native capability rather than an add-on feature.