OpenAI Unveils GPT 5.1 Pro and Codex Max: Breakthrough Reasoning Meets Practical Hurdles

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OpenAI has introduced two new large language models, GPT 5.1 Pro and GPT 5.1 Codex Max, signaling a new phase in the ongoing generative AI advancements. GPT 5.1 Pro, currently available exclusively through the ChatGPT website, is highlighted as a ‘slow, heavyweight reasoning model’ with exceptional instruction-following capabilities. Demonstrations reveal its capacity to tackle highly complex, multi-stage problems; notably, it successfully deciphered a Defcon Gold Bug puzzle – a task typically requiring days for human experts – in approximately 40 minutes, identifying intricate ciphers, subtle clues, and deriving the correct 12-character solution. Despite its ‘disgustingly smart’ problem-solving prowess, its utility is significantly constrained by slow response times (often exceeding 30 minutes), a suboptimal user interface, and critically, the absence of API access, preventing seamless integration into developer workflows.

Simultaneously, OpenAI released GPT 5.1 Codex Max, an agentic coding model targeting long-running software engineering tasks, accessible via the Codex CLI and website, with API access anticipated soon. This model is natively trained for multi-context window operations through a process called ‘compaction,’ aiming for coherence over millions of tokens and promising enhanced speed, intelligence, and token efficiency compared to its predecessors. However, initial developer experiences using the Codex CLI revealed practical difficulties, including frequent errors, context bloat from inefficient web content retrieval, and a surprising inability to maintain TypeScript type safety, often introducing errors and neglecting tsc validation. Developers report that Codex Max demands ‘absurd’ levels of explicit, detailed instructions to perform correctly, indicating a significant gap between its benchmarked performance and real-world usability. Both models, while pushing the boundaries of AI reasoning, present a mixed bag of capabilities and frustrations, with slow execution and limited API availability emerging as primary inhibitors to broader adoption, particularly when compared to more responsive alternatives like Gemini 3 for day-to-day tasks.