Daily Technology
·24/04/2026
OpenAI has announced the release of GPT-5.5, the latest iteration of its flagship language model. While described as its "most intuitive to use" and a "next step toward a new way of getting work done on a computer," the upgrade from GPT-5.4 appears to be incremental. The company highlights faster performance and "gains" in areas like agentic coding, computer use, knowledge work, and scientific research, though concrete evidence beyond company benchmarks remains to be seen.
OpenAI states that GPT-5.5 runs faster than previous versions and demonstrates progress in agentic coding, computer use, knowledge work, and early scientific research. These advancements are attributed to the model's ability to reason across context and take action over time. OpenAI CEO Sam Altman shared his personal approval of the model on X, emphasizing the "excellent work by the inference team to serve this model so efficiently." He also suggested that OpenAI is increasingly becoming an "AI inference company."
OpenAI has released benchmarks indicating that GPT-5.5 outperforms Anthropic's Claude Opus 4.7 in several cybersecurity and autonomous computer-use standards. However, the company acknowledges that it still trails Anthropic in coding tests. The effectiveness and relevance of these benchmarks are increasingly being questioned, as AI models can sometimes be trained to perform well on specific tests but falter under unexpected conditions. Furthermore, Anthropic itself claims its even more advanced, limited-access Claude Mythos Preview model significantly surpasses all alternatives, including Opus.
Despite the debate over benchmark utility, OpenAI continues its rapid pace of development. In addition to GPT-5.5, the company has recently introduced a new image generation model, "workspace agents" capable of autonomous task completion, a model for detecting and redacting personally identifiable information, and an update to its coding agent, Codex. This consistent stream of updates underscores OpenAI's commitment to iterative improvement across various AI domains.









