Xiaomi MiMo-V2.6 – Xiaomi's Open-Source Multimodal Model Series

1. What is Xiaomi MiMo-V2.6

Xiaomi MiMo-V2.6 is a series of fully multimodal models released and open-sourced by Xiaomi, comprising two native full-modal models: Pro and Flash. It is centered on large-scale Agentic reinforcement learning that enables verifiable complex tasks. Through systematic training involving 6 days of online reinforcement learning and approximately 750,000 interaction trajectories, this series significantly enhances capabilities across multiple dimensions, including software engineering, code generation, visual understanding, and network security. It also extends its application boundaries to cutting-edge domains such as 3D game scene construction, Blender 3D modeling, robotic arm embodiment control, Computer Use automated operations, research assistance, and video and music creation.

Technical Positioning and Domain: Belongs to the intersection of multimodal large language models and Agentic AI, focusing on achieving a leap in the model's closed-loop capabilities from perception to action through reinforcement learning. The series employs a MoE (Mixture of Experts) design in its architecture, supporting unified processing of multimodal inputs such as native text, images, videos, GUI interfaces, and 3D spaces, as well as multimodal action outputs.

Development Background: Developed by Xiaomi's AI Lab, leveraging its technical expertise in large-scale pre-training, multimodal alignment, and reinforcement learning. The goal is to build an open-source full-modal Agent base model that is reproducible, modifiable, and capable of self-evolution. The motivation for development stems from the industry's pain points regarding closed-source models, which excel in Agent tasks but suffer from poor customizability and high costs.

Core Value: Addresses three major core issues faced by open-source models in complex Agent tasks: insufficient reasoning capabilities, lack of multimodal action capabilities, and instability in verifiable reinforcement learning training. By opening up weights, technical reports, RL code, and task environments, this series provides the research community and industry with a reproducible and scalable training paradigm for Agent intelligence, significantly lowering the technical barriers to applying full-modal Agent systems.

Technical Features: Utilizes large-scale Agentic reinforcement learning (with a single-step update of approximately 2.7–3.7B tokens), combined with engineering stability mechanisms such as Reward Hacking defenses and MoE Router freezing, to achieve reliable generalization on out-of-sample long-horizon tasks. Its unified trajectory representation and decoupled system architecture support high-concurrency interactions among multiple agents, offering a complete action pipeline from language understanding to physical-world operations.

2. Key Features

• Large-scale Agentic Reinforcement Learning Training: The model continuously learns through trial and error in multi-task environments such as Code, General, Visual, and Cyber, using verifiable results as reward signals. It completes approximately 750,000 interaction trajectories in less than 6 days. Single-step updates reach 2.7–3.7B tokens, significantly improving sample efficiency and out-of-sample generalization. This is the core technical differentiator of this series from traditional supervised fine-tuning models.

• Full-stack Software Engineering Capabilities: In real-world software engineering scenarios, the model demonstrates a notable improvement on the out-of-sample long-range engineering benchmark DeepSWE v1.1. It can automatically identify code defects, generate patches, execute tests, and iteratively refine based on failure logs. The emphasis is on real-world repair capabilities and cross-project generalization, rather than being limited to known issues within the training set.

• 3D Interactive World Construction: The model can automatically decompose textual, image, or video-based requirement descriptions into collaborative multi-agent tasks, generating executable 3D game scenes, interactive logic, and dynamic effects. This capability compresses weeks of scene-building work in traditional game development into minutes, supporting end-to-end generation from concept design to playable demos.

• Blender 3D Modeling: Based on natural language descriptions or reference images, the model generates high-quality 3D assets directly usable in animation production, 3D printing, and game development. It understands geometric structures, material properties, and topological requirements, producing assets with industry-standard precision and completeness.

• Embodied Intelligence Closed-loop Control: Using multi-view camera inputs, the model performs closed-loop control of robotic arms to complete physical operation tasks such as grasping, coloring, and placing. It deeply integrates visual perception with motion planning, adjusting operational strategies based on real-time visual feedback. This represents a critical extension of capabilities from the digital world to the physical world.

• Computer Use Agent: The model understands graphical user interfaces and interacts with office and productivity tools to perform tasks such as data retrieval, table processing, document editing, and cross-tool workflow orchestration. It possesses a complete action chain including screen understanding, interface element localization, click operations, and content verification, capable of replacing human labor in repetitive digital office tasks.

• Research Assistance Capabilities: Supports the "dry lab" process in materials screening, enabling the selection of candidate materials in a virtual environment through computational simulation and data-driven methods, thereby reducing the cost of physical experiments. It also supports Lean 4 formal mathematical proofs, assisting researchers in automated reasoning and verification for theorem proving.

• Multimodal Content Creation: Supports the unified generation of front-end pages, PPT presentations, SVG vector graphics, videos, and music. It emphasizes aesthetic consistency, animation synchronization, music rhythm, and voiceover alignment. The model maintains thematic coherence and stylistic harmony across multiple modalities based on user intent.

3. How to Use

1. Environment Requirements: When using the MiMo Desktop client, it requires support for mainstream operating systems such as Windows or macOS. It is recommended to have at least 8GB of memory. For API usage, you need to register an account on the MiMo Open Platform and obtain an API Key. The network must be able to access the Xiaomi Cloud service endpoint.

2. Client Deployment Process: Visit the official website of Xiaomi MiMo Desktop to download the installation package. After installation, log in with your account and choose a subscription plan or configure your own API Key in the settings panel. Subscription users can directly access the model, while the API Key method is suitable for developers who already have access to Xiaomi Cloud services.

3. Model Selection and Parameter Configuration: Switch between MiMo-V2.6-Pro or Flash versions in the model list. For real-time interaction scenarios, enable the UltraSpeed mode to reduce latency. Choose the Pro version (high precision, strong reasoning) for complex tasks or the Flash version (low cost, fast response) for simpler tasks. For content creation and Agent tasks, it is recommended to use the Pro version.

4. Task Initiation and Material Upload: Describe the task goal directly using natural language. Support uploading multi-modal materials such as images, videos, and files as context input. The model supports mixed input for various task types, ranging from programming development, PPT/web/SVG/video/music creation to research assistance and computer operations.

5. Iterative Correction Loop: Use follow-up questions, screenshot feedback, request retries, or phased confirmation to allow the model to iteratively refine its output based on visual or log results. In Agent tasks, integrate environment logs, test pass rates, screenshots, or validator feedback to form a "execute-check-correct" closed-loop workflow.

6. API Integration and Gray Launch: Developers can create an application on the MiMo Open Platform and copy the API Key. Then, according to the API documentation, input the model name, message, tool calling, and multi-modal input parameters. For Agent integration, it is recommended to first validate cost, latency, success rate, and stability metrics with low traffic, and then gradually replace it into the production pipeline.

4. Pros and Cons Analysis

5. Comparative Analysis with Similar Tools

Selection Recommendations: For research institutions and open-source communities, MiMo-V2.6-Pro stands out as a comprehensive choice among open-source full multimodal Agent models. Its open weights, RL training code, and task environments provide a rare reproducible foundation for academic research. For enterprises requiring deep customization of Agent behavior (such as domain-specific RL or private environment deployment), the open-source nature of MiMo-V2.6 makes it a viable alternative to closed-source API solutions, with a clear cost advantage. However, for production environments that prioritize top-tier intelligence performance and have sufficient budget, closed-source flagship models (such as Claude Fable 5.1) still offer reference value in terms of upper limits for complex tasks. A hybrid architecture can be adopted based on specific scenarios.

6. Editor's Summary

Xiaomi MiMo-V2.6 demonstrates a technically deep layout in the field of open-source, full-modal Agent models. Its core contribution lies in transforming the concept of large-scale Agentic reinforcement learning into practical engineering implementation—training on approximately 750,000 trajectories over 6 days, a single-step update of 2.7–3.7B tokens, and a complete Reward Hacking defense system. The disclosure of these engineering details provides the community with a reference model for scalable RL training. Particularly notable are the introduction of the MoE Router freezing strategy and the unified trajectory representation mechanism, which offer practical solutions to the stability challenges inherent in large-scale RL training.

In terms of practical value, this series of models extends the capability boundaries from traditional text generation to action-oriented tasks such as software engineering, 3D modeling, robotic arm control, computer usage, and research assistance, forming a complete closed-loop from perception to action. Its "verifiable complex tasks" RL paradigm design enables the model to continuously self-improve in scenarios requiring clear feedback signals, a feature that is relatively rare in open-source models.

In terms of target users, this series holds significant value for three categories: first, large model laboratories that require reproducible foundations for research; second, engineering teams in need of a low-cost, customizable Agent solution; and third, enterprise users with strict requirements for data privacy and local deployment. The release of MiMo-V2.6 marks a substantial challenge to closed-source flagship models in the Agent task dimension. The "open weights + reproducible RL + full-modal action" technical stack it has built provides a noteworthy reference point for the future development of open-source Agent models.

7. Application Scenarios

• Agentization of Software Engineering: R&D teams integrate MiMo-V2.6 into code repositories and CI/CD pipelines, allowing the model to automatically detect bugs, modify code, run tests, and iteratively fix issues based on failure logs. This is suitable for internet product development with high-frequency iterations, reducing typical bug fix cycles from hours to minutes. Combined with a code review mechanism, it significantly enhances development efficiency.

• Rapid Prototyping for 3D Interactive Worlds: Game and VR/AR teams use the model to convert planning documents or concept art into executable 3D game scenes and interactive logic, or to generate Blender 3D assets for animation production and 3D printing. This greatly shortens the cycle from creative idea to interactive prototype, making it ideal for Game Jam events, creative validation, and time-sensitive scenarios such as architectural visualization.

• Computer Use for Office Automation: Enterprises and individual users leverage MiMo-V2.6 to control GUIs for tasks such as document retrieval, spreadsheet organization, document editing, and cross-tool workflow orchestration. It is well-suited for repetitive digital labor scenarios like financial data processing, administrative process automation, and market research information consolidation, replacing manual clicks and copy-paste operations with natural language commands.

• Research Assistance and Formal Verification: Researchers use the model to automatically organize literature and patents, perform high-throughput "dry experiments" for screening of porous materials such as MOFs, and assist with formal mathematical proofs in Lean 4. This scenario extends AI capabilities from document processing to hypothesis generation and logical proof, making it particularly suitable for research teams in interdisciplinary fields such as materials science, mathematics, and computer science.

• Large-scale Multimodal Content Production: Marketing teams, educational institutions, and independent content creators use the model to generate front-end pages, PowerPoint presentations, SVG graphics, educational videos, and background narration in bulk, while maintaining consistency in visual style, animation pacing, and narration content. This significantly reduces the marginal cost of content production, making it ideal for regular social media updates, online course creation, and preparation of product promotion materials.

8. FAQ

Q: What are the differences between the Pro and Flash versions of MiMo-V2.6, and how should one choose between them?
A: The Pro version is designed for high-precision inference and excels in complex Agent tasks, long-range engineering benchmarks, and multimodal creation, making it suitable for professional scenarios requiring top-tier output quality. The Flash version focuses on low cost and fast response, offering faster inference speeds and lower token consumption, which is ideal for high-frequency calls, real-time interaction, or production environments sensitive to cost. It is recommended to choose based on task complexity and budget, or to use both versions in combination.

Q: Is MiMo-V2.6 truly open source, and what is included in the open source release?
A: It is open in terms of weights. Xiaomi has released the model weights, along with a technical report, RL training code, and task environments, enabling research institutions and enterprises to perform secondary development and domain customization under the premise of legal compliance. For specific license terms, please refer to the model card information in the official HuggingFace repository.

Q: How does the Agent task capability of MiMo-V2.6 compare to professional Agent products (such as coding assistants or RPA tools)?
A: MiMo-V2.6 has a strong advantage in cross-task generalization. A single model can cover Agent tasks across multiple domains, including programming, web browsing, GUI control, and 3D modeling, without requiring separate model training for each scenario. Professional Agent products have more refined engineering and greater stability in specific task chains. The two can be used complementarily, with professional tools taking the lead in specific scenarios and MiMo handling complex reasoning and orchestration.

Q: What hardware configuration is required for local deployment of MiMo-V2.6?
A: Due to its use of a MoE architecture and its full-modal nature, the model consumes a significant amount of GPU memory during inference. It is recommended to have multiple high-performance GPUs (such as NVIDIA A100/H100 level), along with ample CPU memory and high-speed storage. Specific parameter counts and memory requirements will be confirmed after the official release of the detailed technical report. Developers can first use the API to test the performance before deciding whether to self-host the model.

Q: What languages and multimodal input types does MiMo-V2.6 support?
A: The model supports input in multiple languages, including Chinese and English. It can use text, images, videos, and files as context, and is capable of processing input types such as GUI screenshots, 3D scene information, and multi-perspective camera feeds. On the output side, it supports generating results in various modalities, including text, code, images, 3D assets, music, and video.

9. Project Links

• HuggingFace Model Library: https://huggingface.co/collections/XiaomiMiMo/mimo-v26