GPT-5 models have been the most useless models out of any model released this year despite being SOTA, and it because it slow as fuck.
Ideally I would have both fast and SOTA; if I would have to pick one I’d go with SOTA.
There a report by OpenRouter on what folks tend to pay for it; it generally is SOTA in the coding domain. Folks are still paying a premium for them today.
There is a question if there is a bar where coding models are “good enough”; for myself I always want smarter / SOTA.
I think the bar for when coding models are "good enough" will be a tradeoff between performance and price. I could be using Cerebras Code and saving $50 a month, but Opus 4.5 is fast enough and I value the piece-of-mind I have knowing it's quality is higher than Cerebras' open source models to spend the extra money. It might take a while for this gap to close, and what is considered "good enough" will be different for every developer, but certainly this gap cannot exist forever.
Hard disagree. There are very few scenarios where I'd pick speed (quantity) over intelligence (quality) for anything remotely to do with building systems.
Implicit in your claim are specific assumptions about how expensive/untenable it is to build systemic guardrails and human feedback, and specific cost/benefit ratio of approximate goal attainment instead of perfect goal attainment. Rest assured that there is a whole portfolio of situations where different design points make most sense.
1. law of diminishing returns - AI is already much, much faster at many tasks than humans, especially at spitting out text, so becoming even faster doesn’t always make that much of a difference. 2. theory of constraints - throughput of a system is mostly limited by the „weakest link“ or slowest part, which might not be the LLM, but some human-in-the-loop, which might be reduced only by smarter AI, not by faster AI. 3. Intelligence is an emergent property of a system, not a property of its parts - with other words: intelligent behaviour is created through interactions. More powerful LLMs enable new levels of interaction that are just not available with less capable models. You don’t want to bring a knife, not even the quickest one in town, to a massive war of nukes.
The current SOTA models are impressive but still far from what I’d consider good enough to not be a constant exercise in frustration. When the SOTA models still have a long way to go, the open weights models have an even further gap distance to catch up.
I'd like more speed but prefer more quality than more speed.
We should be glad that the foundation model companies are stuck running on treadmills. Runaway success would be bad for everyone else in the market.
Let them sweat.
Reason is: while these models look promising in benchmarks and seem very capable at an affordable price, I *strongly* felt that OpenAI models perform better most of the times. I had to cleanup Gemini mess or Claude mess after vibe coding too much. OpenAI models are just much more reliable with large scale tasks, organizing, chomping tasks one by one etc. That takes its time but the results are 100% worth it.
