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For the first time, Qwen3.8 brings a Qwen-Max-class model to open release. Built on the architectural foundation of Qwen3.5, Qwen3.8 delivers substantial gains across coding, professional work, research, and long-horizon agentic tasks. Beyond answering harder questions, Qwen3.8 is designed to carry complex, multi-step tasks through to completion with greater reliability. Qwen3.8 features the following enhancements: Core Capabilities: Comprehensive improvements across coding, professional work, research, and long-horizon agentic tasks. Agent Execution: Stronger autonomous planning and better ha

ZenDNN 5.2.1 builds on the modular multi-backend architecture introduced in 5.2, extending it with deeper  quantization paths and kernel-level optimizations across the ZenDNN runtime.

MiniMax M3 is a new open-weight model for coding, agentic, and multimodal workloads. MiniMax describes M3 as combining three frontier capabilities in one model: strong coding and agentic task performance, long-context MiniMax Sparse Attention (MSA), and native multimodality for text, image, and video understanding. The 1M-token context window of MiniMax M3 enables sophisticated, long-horizon application workflows, including autonomous software engineering agents, repository-wide reasoning, and native multimodal document analysis alongside tool-driven automation. The AMD day-zero enablement foc