In my opinion and that of the colleagues I've spoken with, working with Opus 5 feels like a downgrade compared to Opus 4.7, Opus 4.8, and Fable.
I'm not claiming a step backwards in capabilities – it is a more capable model than Opus 4.7 and Opus 4.8 and even rivals Fable in benchmarks, yet these other models feel better to work with. I believe this is because they:
- stop and ask questions if my intent was unclear,
- don't make assumptions without checking,
- and don't reinterpret or update my plans without asking.
Because of this, they don't require the careful babysitting that Opus 5 does.
I suspect this is the result of two compounding forces at Anthropic, and in current frontier labs in general.
First, the desire to create a self-improving AI that is capable of recursively bootstrapping itself to AGI/ASI.
Second, the pressure to score highly on benchmarks. Although it's an open secret that many benchmark tasks are ill-defined, unfair, hackable, or otherwise broken, a good benchmark task is self-contained. It can be solved. It doesn't require hints, reading the task creator's mind, or outside information to pass.
That doesn't mean a good task can only have one correct answer, just that it should score all unambiguously correct answers equally.
Selecting for models that do well on benchmarks (and indeed training for them or on RLVR tasks in general) inherently selects for models that make bold, usually-correct assumptions in the face of ambiguity. It penalizes models with a tendency to stop and ask for clarification or direction.
Unfortunately, that's exactly what most of us want from a coding agent.
Try as you might, it's nearly impossible to get the entirety of the context, intentions, business implications, budget constraints, and what-have-you written down and accessible to a coding agent. There will invariably be ambiguity and choices to be made, and it is nice to know that an agent will stop and ask when needed.
Real life just isn't a benchmark. There isn't a guaranteed right answer to every question, nor even a set of right answers, and with real-life consequences on the line, I do not want an agent taking its best guess!