Xiaomi MiMo-V2.5: The Next Generation of Open Agentic Models

Xiaomi announces the MiMo-V2.5 series, featuring flagship agentic performance and native omnimodality to rival frontier models like Claude 4.6 and GPT-5.4.

by HowAIWorks Team
aixiaomimimoagentsllmmultimodalbenchmarksopen-weightsautomation

Introduction

Xiaomi has rapidly advanced its AI roadmap with the announcement of the MiMo-V2.5 series, the next generation of their open agentic models. This release follows the success of the MiMo-V2 line, pushing the boundaries of what open-weights models can achieve in autonomous task execution, multimodal reasoning, and software engineering.

The MiMo-V2.5 lineup is designed to compete directly with the world's most advanced frontier models, providing developers and enterprises with powerful tools for complex, long-duration agentic workflows.

MiMo-V2.5-Pro: The New Agentic Flagship

The MiMo-V2.5-Pro stands as the flagship of the new series. It represents a significant technological leap over its predecessor, particularly in its ability to handle general agentic tasks, complex software development, and long-term multi-step objectives.

Autonomous Professional Capabilities

One of the most impressive claims by the Xiaomi team is the model's ability to autonomously complete professional tasks that require over 1,000 tool calls. This level of reliability and complexity enables the model to handle workloads that would typically take human experts several days to finish.

  • Complex Software Engineering: Enhanced capabilities for architectural planning and code implementation.
  • Long-term Task Management: Improved state retention and reasoning across extended workflows.
  • Tool Use Excellence: Highly efficient and accurate interaction with external APIs and systems.

MiMo-V2.5: High-Performance Omnimodal Intelligence

For those seeking a balance between performance and cost, the standard MiMo-V2.5 offers "Pro-level" capabilities at approximately half the inference cost. Unlike many models that add multimodal layers later, MiMo-V2.5 is natively omnimodal, allowing for more fluid and accurate processing of diverse data types.

Key Improvements

  • Enhanced Perception: Significant upgrades to image and video understanding.
  • 1M Context Window: A native 1-million-token context window for processing massive datasets.
  • Inference Efficiency: Optimized for significantly faster and more resource-efficient performance compared to previous generations.
  • Agentic Native: Built from the ground up to support agentic workflows, even at a lower cost point.

Benchmarks and Comparisons

The performance of the MiMo-V2.5-Pro has been validated against some of the most rigorous benchmarks in the industry. According to Xiaomi's internal data, the model is now catching up to frontier models such as Claude 4.6 and GPT-5.4.

Benchmark Scores

  • SWE-bench Pro: 57.2
  • Claw-Eval: 63.8
  • τ3-Bench: 72.9

These scores highlight the model's proficiency in real-world software engineering (SWE-bench) and complex reasoning tasks, positioning Xiaomi as a top-tier provider in the open AI ecosystem.

Conclusion

The announcement of the MiMo-V2.5 series marks a pivotal moment for Xiaomi's AI division. By focusing on deep agentic capabilities and native omnimodality, they have created a suite of models that are not just conversational assistants, but capable autonomous workers. As these models become available via API and open-weights releases, we can expect a surge in sophisticated AI-driven automation across various industries.

Sources


Explore our AI models catalog for more detailed technical specifications, or check out our AI glossary to learn more about agentic models and omnimodality.

Frequently Asked Questions

MiMo-V2.5-Pro is the flagship of the lineup, offering a major leap in general agentic capabilities, complex software development, and long-term tasks. It can autonomously perform professional tasks requiring over 1,000 tool calls.
MiMo-V2.5 is a natively omnimodal model that provides Pro-level performance at roughly half the cost. It features improved image and video perception, a 1-million-token context window, and more efficient inference.
The models achieved top-tier scores: SWE-bench Pro — 57.2, Claw-Eval — 63.8, and τ3-Bench — 72.9, placing them in competition with frontier models like Claude 4.6 and GPT-5.4.
Technical blogs and API access with various tariff plans are available on the Xiaomi MiMo platform at platform.xiaomimimo.com.

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