GLM-5.1
❖ GLM-5.1 is our next-generation flagship model for agentic engineering, with significantly stronger coding capabilities than its predecessor. It achieves state-of-the-art performance on SWE-Bench Pro and leads GLM-5 by a wide margin on NL2Repo (repo generation) and Terminal-Bench 2.0 (real-world terminal tasks).
Chatbot Arena +
❖ This leaderboard is based on the following benchmarks. Chatbot Arena - a crowdsourced, randomized battle platform for large language models (LLMs). We use 6M+ user votes to compute Elo ratings. AAII - Artificial Analysis Intelligence Index v3 aggregating 10 challenging evaluations. ARC-AGI - Artificial General Intelligence benchmark v2 to measure fluid intelligence.
Attention Residuals
❖ This is the introduction of Attention Residuals (AttnRes), a drop-in replacement for standard residual connections in Transformers that enables each layer to selectively aggregate earlier representations via learned, input-dependent attention over depth.
Qwen3.5
❖ Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency.
SWE-bench +
❖ SWE-bench is a benchmark for evaluating large language models on real world software issues collected from GitHub. Given a codebase and an issue, a language model is tasked with generating a patch that resolves the described problem. SWE-bench Verified is a human-validated subset that more reliably evaluates AI models’ ability to solve issues. International Olympiad in Informatics (IOI) competition features standardized and automated grading.
Kimi K2.5
❖ Kimi K2.5 is an open-source, native multimodal agentic model built through continual pretraining on approximately 15 trillion mixed visual and text tokens atop Kimi-K2-Base. It seamlessly integrates vision and language understanding with advanced agentic capabilities, instant and thinking modes, as well as conversational and agentic paradigms.
GLM-4.7 with SGLang
❖ The GLM-4.x series models are foundation models designed for intelligent agents. GLM-4.7 has 355 billion total parameters with 32 billion active parameters, while GLM-4.5-Air adopts a more compact design with 106 billion total parameters and 12 billion active parameters. GLM-4.x models unify reasoning, coding, and intelligent agent capabilities to meet the complex demands of intelligent agent applications.
Qwen3-VL
❖ Meet Qwen3-VL — the most powerful vision-language model in the Qwen series to date. This generation delivers comprehensive upgrades across the board: superior text understanding & generation, deeper visual perception & reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities. Available in Dense and MoE architectures that scale from edge to cloud, with Instruct and reasoning‑enhanced Thinking editions for flexible, on‑demand deployment.