The user interface and features of the coding environment (the 'harness'), like Cursor or the Codex desktop app, significantly impact AI model performance. A poor experience may stem from an immature application wrapper rather than a flaw in the underlying language model, shifting focus from model-vs-model to the entire toolchain.
The latest AI coding assistants facilitate a massive leap in developer productivity. The host demonstrated this by merging 44 pull requests and adding nearly 93,000 lines of code in just five days, a workload that would typically take an entire team months to complete, making the scale of the impact concrete.
Treat Anthropic's Opus 4.6 as a productive product engineer, excellent for generative, greenfield work. Then, use OpenAI's GPT-5.3 Codex as a principal engineer to review architecture, find edge cases, and harden the code. This mimics a real-world engineering team dynamic for optimal results.
While precise instruction-following is often a feature, the GPT-5.x Codex family can be too literal for creative work. It blindly implements prompts without nuance, overfitting to the most recent instruction. For example, when asked to add a section on integrations, it can make the entire page about integrations.
Standard prompts for creative tasks often yield generic, 'AI slop' results. To achieve exceptional design or copy, use hyperbolic, aspirational language like 'make it look like I spent a million dollars on design.' This 'desperate prompting' pushes the model beyond its default, mediocre state to produce higher-quality, unique work.
While faster model versions like Opus 4.6 Fast offer significant speed improvements, they come at a steep cost—six times the price of the standard model. This creates a new strategic layer for developers, who must now consciously decide which tasks justify the high expense to avoid unexpectedly large bills.