China is currently experiencing rapid development in AI models, ranging from food delivery services to giants like Alibaba and Baidu. Xiaomi is also contributing to this field by introducing new versions of its MiMo family of models. The latest development is the V2.6 Pro model, based on reinforcement learning (RL) and offering various configurations.

Architecture and Capabilities of MiMo-V2.6-Pro

MiMo-V2.6-Pro is positioned as a flagship model with 1.02 trillion parameters in a "Mixture of Experts" (MoE) architecture, with only 42 billion parameters actively utilized. The primary goal of the model is to scale reinforcement learning for self-improvement. It integrates coding tasks, agent operations, visual tasks, and cybersecurity within a single reinforcement learning loop, rather than separate training for each domain. This enables skill transfer across different domains.

The model is capable of processing text, images, video, and audio within a single architecture, boasting a context window of 1 million tokens, which supports extended agent sessions.

Testing with a Real-World Application Example

To demonstrate the model's capabilities, the task was to fix an error in the live application "Nusantara Ferry Lines," an interactive dashboard for monitoring ferry occupancy between Indonesian islands. The dashboard displayed incorrect data regarding ferry fullness. For instance, the Merak — Bakauheni route showed 5% occupancy with 114 available seats, while in reality, 92 seats were booked on the ferry, leaving only 28. The cause of the error was that the system was counting the number of bookings rather than the number of passengers within each booking. This led to an underestimation of actual occupancy and a potential risk of overbooking and safety violations.

After integration with the Hermes agent, the model was tasked with correcting the error without being explicitly told its specific nature.

Comparative Characteristics and Benchmarks

According to the presented data, MiMo-V2.6-Pro demonstrates results comparable to the Claude Opus 5 and GPT-6 Astra models. The model surpasses both on real-world coding tasks, such as Deep Sue, and holds leading positions in the Job Bench benchmark. It is noted that the model does not win in all categories, and Opus 5 is an outdated model. Nevertheless, for a model of this size, competition with GPT-6 Astra is considered a significant achievement. Results on other benchmarks are not as impressive but are comparable to the Fable 5.1 model.

Error Correction Process in Nusantara Ferry Lines

During the task of correcting an error in the "Nusantara Ferry Lines" application, the MiMo-V2.6-Pro model demonstrated its ability to find and resolve issues. Despite the process taking over 25 minutes and consuming a significant number of tokens, the model successfully completed the assigned objective. After refreshing the page, the booked seats indicator changed from 6 to 92, which corresponds to the actual situation and eliminates the risk of overbooking. It is noted that the error was subtle and difficult to detect.

Testing Web Page Generation for a Coffee Shop

In the next test, the model was tasked with creating a landing page design for a Melbourne coffee shop, following a detailed brief: use bold colors, an editorial layout, and a strong personality, while avoiding cliché solutions. The model was expected to demonstrate a "flair" for front-end development, as stated in the company's blog.

The model successfully completed the task, generating an HTML file and launching a local Python server. The created page, named "Duck Board," included "Menu," "Story," and "Find Us" sections, which functioned correctly. The page's design was assessed as stylish and modern, with creative text and detailed information for visitors.

Testing Visual Capabilities and Game Creation

Next, the model's visual capabilities were tested by providing an image and a prompt to create a concept for an interactive 3D game based on it. The model accurately identified all objects in the image, including balls, mice, bedsheet towers, and donuts, and mapped them to specific interactive mechanics.

The model was then asked to create a simple game prototype in a single HTML file. The result was a working game where the character could jump (using the spacebar). However, the WASD keys for movement control did not work. The model was informed of the error and provided with the error code. After re-correction, the movement control keys still did not function, although the jump worked correctly. Testing in another browser (Microsoft Edge) also did not reveal a fix for the control issue. Nevertheless, the model's ability to understand images and create simulations or 3D animations based on them was positively assessed.

Conclusion

The Xiaomi MiMo-V2.6-Pro demonstrates significant capabilities in multimodal data processing, solving complex encoding tasks, and content generation. Despite some remarks regarding speed and token efficiency in certain scenarios, the model shows high potential, especially in tasks requiring creativity and contextual understanding. The distilled version of the Coin model is planned for review in the near future.