API-Bench v2 evaluates how effectively different large language models (LLMs) can build integrations with real-world APIs. It specifically measures capabilities like API specification adherence, authentication handling, pagination, and multi-step workflows. The benchmark found that while LLMs can write code, they often fail to produce reliable, working integrations. For example, in testing, superglue achieved a 93% success rate across 41 tasks, outperforming other models like Claude Opus 4.5 (88%) and Gemini 3 Pro (85%). Key issues identified include LLMs' reliance on outdated documentation, which leads to incorrect implementations, such as failing to use newer API features. Many models struggled with niche or lesser-known systems, resulting in misinterpretations of API structures. The models also fell short in debugging multi-step processes, often unable to correct errors autonomously. Authentication, especially with legacy systems, posed additional challenges, as LLMs typically can't track and manage intermediate states effectively. The findings emphasize that specialized tools like superglue outperform general-purpose LLMs in integration tasks. Superglue’s self-healing capabilities and precise documentation retrieval allowed it to navigate errors and adhere to API specifications more effectively. This highlights the importance of specialized solutions in ensuring reliable API integrations, especially for complex or niche systems.