Baichuan-M2-Plus

Baichuan-M2 is the flagship visual understanding model launched by Baichuan Intelligence. Its core positioning is as a medical reasoning engine based on the MOE architecture, leveraging a dynamic validation system to achieve deep adaptation to real-world medical scenarios and redefine the boundaries of AI-driven clinical decision-making.

• World-Leading Medical Performance: In the HealthBench benchmark, Baichuan-M2 scored 60.1 points, surpassing all open-source models (such as gpt-oss-120b) and most closed-source models (such as Grok3), becoming the first open-source model to exceed 32 points on the HealthBench Hard task.
• Dynamic Validation System: Pioneering a “Virtual Clinical World” reinforcement learning environment, which uses patient simulators and multi-dimensional evaluation scales to enable real-time optimization of diagnostic logic and communication skills.
• High-Resolution Visual Analysis: Exclusively supports an input resolution of 1120×1120, precisely capturing fine details in medical images (such as bronchial nodules) and improving parsing accuracy by 50%.
• Enterprise-Grade Private Deployment: After 4-bit quantization, it can run on just a single RTX 4090 GPU. Huawei Ascend 910B compatibility has been completed, significantly reducing deployment costs for medical institutions.
• Structured Output Support: Generates diagnostic and treatment recommendations in native JSON format, directly interfacing with hospital information systems and minimizing the need for secondary development.

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Core Capabilities

🧠 MOE Architecture Powered: With a total parameter count of 106 billion and 12 billion activated parameters, Baichuan-M2 achieves the highest performance among comparable open-source models, boosting visual reasoning efficiency by 40%.

⚡ Deep Thinking Mode: Dynamically activates complex reasoning chains to tackle advanced tasks such as interdisciplinary problem-solving and logical deduction, with an accuracy rate exceeding 92%.

🔍 Multi-Modal Parsing: Exclusively supports three modalities—video, image, and file—as input, enabling multi-source information fusion analysis in a single call.

🌐 Long-Term Sequence Understanding: 32K Context Window + Intelligent Caching Mechanism: This allows for more coherent tracking of long-video events and causal chain analysis of documents.

🛠️ Automated Execution: A GUI Agent precisely identifies interface elements and automatically performs office tasks such as PPT editing and data entry.

📊 Structured Output: Native JSON support plus coordinate-based localization (such as Grounding) enables direct generation of interactive code or structured data.