A potent new front has opened in the fiercely competitive race for artificial intelligence supremacy, centering on the ability of large language models to reliably execute long, multi-step, and autonomous tasks. The developer community, specifically users of the AI-native code editor Cursor, has weighed in with a strong sentiment: OpenAI’s latest flagship model, GPT-5.2, is demonstrating a superior capacity for sustained agentic work compared to its chief rival, Anthropic’s Claude Opus 4.5. This comparison is not based on traditional single-prompt benchmarks but on real-world, end-to-end workflow performance, marking a critical shift in how frontier models are being evaluated.

The central finding highlights a fundamental difference in model behavior during complex, lengthy operations, such as refactoring large codebases or executing multi-stage project plans. According to the developer reports circulating within the Cursor community, Anthropic's Opus 4.5, while exceptionally intelligent and efficient, tends to "stop earlier and take shortcuts when convenient" during sustained autonomous sessions. This perceived lack of persistence can cause the model to prematurely halt a complex task or produce an incomplete result, forcing a developer to intervene and redirect the AI agent. In contrast, OpenAI's GPT-5.2, particularly when utilizing its deepest reasoning modes, reportedly exhibits greater tenacity, "holding context" and demonstrating a more robust ability to adhere to a long-term plan, even when encountering unforeseen complications mid-task. For one user performing an "insane large-scale refactor," Opus reportedly "kept falling apart mid-way," while the rival model "actually held context and landed it clean."[1] This suggests that GPT-5.2’s advanced architecture, potentially its more thorough "Thinking" or "Pro" variants, invests greater internal compute resources into planning and self-correction, enabling it to better navigate the gnarly bugs and ambiguous instructions inherent in complex software development.

The ability of an AI model to maintain focus across numerous files and multiple reasoning steps is a crucial metric for the burgeoning field of autonomous agents. For professional knowledge workers, this capability transcends simple code generation and speaks directly to the model's capacity for true agentic behavior—acting on a user’s behalf over an extended period without continuous human oversight. OpenAI has explicitly marketed GPT-5.2’s enhanced capabilities in "multi-step project execution" and "spreadsheet creation, financial modeling, presentations," underscoring an intense focus on professional, long-horizon applications.[2][3] Its release, which some reports suggest was accelerated following competitive pressure from other models, focused heavily on strengthening core capabilities like reasoning and stability, rather than introducing only flashy new features.[4][5] The model’s nearly perfect accuracy on long-context retrieval tasks, such as the 4-needle MRCR variant at 256k tokens, provides a technical basis for its superior long-term performance, demonstrating an unparalleled capacity to retain vital details across massive documents or code repositories.[5]

However, the comparison is far from a complete rout, with Claude Opus 4.5 retaining distinct advantages in specific areas, largely centered on efficiency and speed. On a key industry evaluation, the SWE-bench Verified benchmark, which tests the critical ability to understand real-world GitHub issues and implement fixes in complex codebases, Opus 4.5 holds a marginal lead with a score of 80.9%, compared to GPT-5.2's 80.0%, essentially tying on this measure of real-world coding capability.[6][7] Furthermore, in terms of speed and cost-effectiveness, Opus 4.5 often proves the more pragmatic choice. The model features a new "effort" parameter, allowing developers to trade off depth for nimbleness, and anecdotal evidence suggests that in tasks where the developer has an implementation plan or requires rapid iteration, Opus 4.5's superior speed and token efficiency make it preferable.[8][9][10] This trade-off is often summarized by developers who suggest that while Opus is excellent for implementing known tasks, GPT-5.2 is better for initial planning and tackling the most challenging, unpredictable edge cases, owing to its superior abstract reasoning on benchmarks like ARC-AGI-2.[6]

The ongoing rivalry, framed by the developer feedback from environments like Cursor, underscores the industry's shift away from generic performance metrics toward task-specific utility. Both companies have responded to the need for tailored performance by introducing mechanisms to control the model's computational "effort" or "thinking" time. GPT-5.2 offers a range of thinking modes, including "instant" and "Pro," while Opus 4.5 has an "effort" parameter, allowing users to select the right balance of speed, depth, and cost for a given task.[2][11] This feature parity signals an evolving competition where the winner will be the platform that can most reliably, cost-effectively, and intelligently automate the most valuable professional workflows. The current sentiment suggests that for the most demanding, open-ended, and self-directed tasks, where the risk of the model giving up is the primary concern, OpenAI's latest offering has set a new, high bar for persistence in agentic AI. This ongoing battle for autonomous task superiority will not only dictate the market share of these tech giants but also accelerate the timeline for a new generation of fully independent AI agents across all sectors of the economy.[5]