AI Technology Insights -2026.10.25

1. Kimi 3.1 Pro — Model Capabilities and Emerging Opportunities
Benchmark Performance
SWE Marathon: Ranked No. 1
Terminal Bench: Ranked No. 2
Agent Last Exam: Ranked No. 1
Office Q&A V2: Ranked No. 2
Key Technical Insight
The significant improvement in long-horizon task execution is primarily driven by reinforcement learning (RL), enabling the model to perform more effectively on complex, multi-step tasks.

API Pricing (per 1 Million Tokens)
Input: RMB 13
Output: RMB 68

Industry Trend: AI-Powered 3D Content Generation


3D content generation is becoming an increasingly promising application of AI. Combining large language models (LLMs) with tools such as Blender enables more automated 3D modeling, scene creation, and content production.

Action Items
Research practical AI-powered 3D application scenarios and use cases.
Explore the integration of LLMs with Blender.
Identify opportunities for AI-generated 3D content in enterprise showrooms, immersive LED displays, and interactive exhibitions.
2. Large Language Model Deployment — From Infrastructure to Validation

Infrastructure and Deployment

Develop a practical understanding of how to configure and deploy LLMs on AI computing infrastructure

Key areas include:

Setting up high-speed network switches and physical connections between computing devices.
Integrating hardware infrastructure with AI inference software and algorithms.
Deploying and configuring LLM inference services.
Verifying network connectivity, model loading, and service availability.
Post-Deployment Testing and Validation
Successful deployment is only the first step. Comprehensive testing is essential to ensure the system is reliable, efficient, and ready for production.