Alibaba Cloud and Moonshot AI Launch Next-Generation Reasoning Models

In late January 2026, the artificial intelligence landscape saw the simultaneous arrival of two high-performance flagship models from China: Alibaba Cloud’s Qwen3-Max-Thinking and Moonshot AI’s Kimi K2.5. Both releases represent a significant shift toward "reasoning-first" architectures designed for complex logic and autonomous task execution.

Alibaba Cloud: Qwen3-Max-Thinking

Released on January 26, 2026, Qwen3-Max-Thinking is a large-scale reasoning model featuring an architecture that exceeds one trillion parameters. It is engineered to handle multi-step logical synthesis and advanced technical problem-solving.

• Adaptive Tool Use: A core feature that enables the model to autonomously select between Search, Memory, or Code Interpreter functions during a conversation. The AI independently determines which external tool is required to verify facts or perform calculations based on the user's query.
• Test-Time Scaling (TTS): The model utilizes inference-time compute scaling, allowing it to devote more processing power to "thinking" through difficult problems. This technique has resulted in a score of 90.2 on the Arena-Hard v2 benchmark.
• Benchmark Performance: Qwen3-Max-Thinking has demonstrated high efficiency across multiple reasoning benchmarks, emphasizing its capability in scientific computing, mathematical logic, and complex coding tasks.

Moonshot AI: Kimi K2.5

On January 27, 2026, Moonshot AI (supported by Alibaba Group) introduced Kimi K2.5, an open-source, natively multimodal agentic model. This version is optimized for coordination and large-scale data processing.

• Mixture-of-Experts (MoE) Architecture: While the model’s total capacity reaches one trillion parameters, the MoE design ensures that only 32 billion parameters are activated during operation. It was pre-trained on a massive dataset of 15 trillion mixed visual and text tokens.
• Agent Swarm Mode: K2.5 introduces a sophisticated "Agent Cluster" capability, allowing it to coordinate up to 100 specialized sub-agents for a single project. In "Swarm" mode, the system can self-direct these agents to solve complex problems without requiring pre-defined workflows or human intervention.
• Agentic Efficiency: The model is specifically designed for enterprise-level automation, scoring high on agentic benchmarks like HLE and BrowseComp by focusing on multi-step planning and browser-based research.

While both models utilize trillion-parameter foundations, their focus differs slightly: Alibaba's Qwen3-Max-Thinking prioritizes deep, iterative reasoning and autonomous tool selection, while Moonshot’s Kimi K2.5 focuses on multimodal agentic coordination and large-scale autonomous workflows.