A German tech outlet benchmarked four Chinese model families — Kimi, Qwen, GLM and DeepSeek — against established Western offerings such as ChatGPT and Claude. The review examines whether their strong benchmark scores translate into comparable quality in everyday practical use. It concludes that these alternatives offer notable capability at a lower price, though with trade-offs.
Chinese lab AllSpark has published two open-source search agents, Iris-mini and Iris-pro, both built on Qwen base models. The company claims the pair lead benchmarks in their respective size classes. A notable side effect emerged during the agents' search training, according to the report.
Researchers post-trained a 27B Qwen model to adopt a Korean response style covering verbosity, list and markdown formatting, discourse structure and register. They then measured two behaviors the training objective never targeted, including abstention on ambiguous social questions in the KoBBQ benchmark. The study reports that style-focused alignment produced measurable side effects on these untrained behaviors.
A new arXiv paper examines how much a language model relies on query-routing signals versus stored knowledge as it produces an answer. The authors apply layerwise interventions to the hidden state at the end of the question and test the approach across several model families, including Qwen and Llama. The work aims to clarify where and when internal knowledge is retrieved during generation.