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GLM-5.2 supports a larger context window and output length, with up to 1M context and 128K output tokens. It adds streaming output for tool calls via `tool_stream=true`, enabling real-time access to tool call arguments. Like the GLM-4.7 series, it supports deep thinking with `thinking={ type: "enabled" }`, where thinking is enforced once enabled, and introduces `reasoning_effort` to control reasoning intensity under chain-of-thought mode. It also delivers stronger coding performance and more advanced reasoning capabilities.

Doubao-Seed-2.1 is a next-generation large model built for the era of coding and agents. It offers two versions, Pro and Turbo, respectively designed for exploring highly complex tasks and for large-scale production scenarios. Compared with the previous generation, Seed 2.1 delivers comprehensive upgrades across three key areas: coding engineering delivery, long-horizon agent task execution, and multimodal understanding. With stronger autonomous planning and dynamic self-repair capabilities, it is well suited to high-value production tasks in real enterprise R&D as well as broader economic and social contexts. For developers, it also offers an Evolving version for coding, office work, and productivity enhancement scenarios, with weekly updates and continuous evolution.

2.4-trillion-parameter MoE flagship delivering a comprehensive leap in coding and professional work. Autonomously codes and delivers complete projects spanning 10+ days. Handles hundreds of specialized tasks across legal, financial, design, and other professional domains, producing production-grade results end-to-end in a single conversation. Native visual understanding runs through the full cycle of planning, execution, and verification, enabling deep semantic analysis of ultra-long documents and extended video content. In long-horizon tasks, plans autonomously, iterates through closed feedback loops, and continuously evolves.

MiniMax M3 is the first open-weights flagship that brings coding, agentic reasoning, million-token context, and native multimodality together in one model. It handles autonomous task decomposition, tool use, and multi-step reasoning with ease — and writes code that's meant to ship, not code that just runs. Built on MiniMax's proprietary Sparse Attention architecture, M3 natively supports million-token context windows for long-horizon agents, large codebases, and long-video understanding. Its multimodality isn't bolted on — it's trained in from day one, with text and vision deeply aligned at the semantic level.

Kimi K3 is Kimi’s most capable model to date, with 2.8 trillion parameters. Built on Kimi Delta Attention, a hybrid linear attention mechanism, and Attention Residuals, it offers native visual understanding and a 1M-token context window for frontier intelligence scenarios such as software engineering, knowledge work, and deep reasoning.

MiMo-V2.5 is a native omnimodal model by Xiaomi. It delivers Pro-level agentic performance at roughly half the inference cost, while surpassing MiMo-V2-Omni in multimodal perception across image and video understanding tasks.