GLM-5深夜登场，这是国产开源模型首次逼平Claude Opus 4.5。

Core Viewpoint - The article emphasizes the significant advancements of the GLM-5 model in the AI coding landscape, positioning it as a competitive alternative to leading models like GPT-5.3-codex and Claude Opus 4.6, particularly in terms of performance and cost-effectiveness [3][72]. Performance and Capabilities - GLM-5 has expanded its parameters from 355 billion to 744 billion, resulting in a substantial increase in intelligence and capabilities, while keeping costs relatively low [7]. - In benchmark tests, GLM-5 scored 75.9 in the BrowseComp benchmark, surpassing GPT-5.2 by 10 points and approaching the top models like GPT-5.2 Pro and Opus 4.6 [12]. - The model shows strong performance in various tasks, including long-term planning and execution, indicating its capability to handle complex tasks effectively [16][64]. Cost and Accessibility - GLM-5 offers a significantly lower price point compared to its competitors, with input and output costs being much cheaper, making it more accessible for users [17][18]. - The subscription model for GLM-5 is priced at two-thirds of the Claude Max package while offering three times the token limit, indicating a strong value proposition [20]. Development and Use Cases - The article discusses practical applications of GLM-5, including the development of a cross-platform content distribution tool, showcasing its ability to handle real-world coding tasks effectively [27][36]. - Another example includes the creation of a card counting plugin for a game, demonstrating GLM-5's capability to engage in complex problem-solving and iterative development [42][64]. Market Position and Future Outlook - The emergence of GLM-5 signifies a narrowing gap between domestic models and leading international counterparts, suggesting a shift in the competitive landscape of AI coding tools [70][72]. - The open-source nature of GLM-5, combined with its affordability, is expected to democratize access to advanced AI coding capabilities, fostering a more vibrant community and accelerating model iterations [73].