GLM-5 Shows Regression in International Language Writing Despite Massive Scaling

Despite its groundbreaking scale and advanced agentic architecture, GLM-5, the latest large language model from Zhipu AI, has revealed a troubling regression in international language writing capabilities, according to independent benchmarking data from NCBench. While the model demonstrates substantial improvements in long-context reasoning and complex system engineering — as detailed in a technical blog by z.ai — it underperforms its predecessor, GLM-4.5, in generating coherent, fluent text across European languages and Hindi. This discrepancy has sparked concern among developers and multilingual AI users who rely on consistent performance across linguistic domains.

The anomaly was first highlighted by a user on the r/LocalLLaMA subreddit, who noted that GLM-5’s performance in language writing tasks was comparable to older, smaller models like Llama 3.70B or even the aging Gemini 2.5 Flash. The user’s personal experience using GLM-5 for low-resource language generation aligned with NCBench’s quantitative results, which show GLM-5 scoring significantly lower than GLM-4.5 on writing-specific metrics for languages including French, German, Spanish, Portuguese, and Hindi. Notably, GLM-5’s language comprehension scores remained stable, suggesting the regression is isolated to generation quality rather than understanding.

According to z.ai’s official technical release, GLM-5 represents a massive architectural leap: its total parameter count has expanded from 355B in GLM-4.5 to 744B, with active parameters rising from 32B to 40B. Pre-training data has also grown from 23 trillion to 28.5 trillion tokens. The model integrates DeepSeek Sparse Attention (DSA) to maintain long-context efficiency and employs a novel asynchronous reinforcement learning infrastructure called "slime" to enhance reasoning and task execution. These enhancements are explicitly designed for complex agentic workflows — such as multi-step software development, autonomous system orchestration, and long-horizon planning — not necessarily for nuanced linguistic expression.

The disconnect between scaling and linguistic performance suggests a potential misalignment in training objectives. While GLM-4.5 was likely fine-tuned with a broader corpus of multilingual human-written text, GLM-5’s training data may have prioritized technical, code-related, or English-dominant corpora. This could explain why comprehension — which relies on pattern recognition and contextual inference — remains robust, while generation — which demands stylistic fluency, grammatical precision, and cultural nuance — deteriorates. The phenomenon mirrors past cases in AI development, such as GPT-4’s initial struggles with non-English poetry, where scaling alone did not guarantee balanced linguistic proficiency.

For enterprise users deploying GLM-5 in global content generation, customer service localization, or multilingual publishing, this regression poses a tangible risk. Organizations relying on GLM-5 for international outreach may inadvertently produce awkward, inaccurate, or culturally insensitive outputs in non-English contexts. Developers are advised to validate outputs in target languages using domain-specific benchmarks before deployment. Meanwhile, the open-source community has begun sharing fine-tuning datasets to restore GLM-5’s multilingual writing capabilities, with early results showing promise when combined with LoRA adapters trained on NCBench-aligned corpora.

Zhipu AI has not yet issued a public response to the NCBench findings. However, given the company’s history of rapid iteration — including the release of GLM-4.5 just months before GLM-5 — a patch or GLM-5.1 update focused on linguistic fidelity may be imminent. Until then, users requiring high-quality multilingual writing should consider retaining GLM-4.5 for content generation tasks while reserving GLM-5 for agentic and reasoning-intensive applications.