GPT-5.2

I feel there is a point when all these benchmarks are meaningless. What I care about beyond decent performance is the user experience. There I have grudges with every single platform and the one thing keeping me as a paid ChatGPT subscriber is the ability to sort chats in "projects" with associated files (hello Google, please wake up to basic user-friendly organisation!)

But all of them * Lie far too often with confidence * Refuse to stick to prompts (e.g. ChatGPT to the request to number each reply for easy cross-referencing; Gemini to basic request to respond in a specific language) * Refuse to express uncertainty or nuance (i asked ChatGPT to give me certainty %s which it did for a while but then just forgot...?) * Refuse to give me short answers without fluff or follow up questions * Refuse to stop complimenting my questions or disagreements with wrong/incomplete answers * Don't quote sources consistently so I can check facts, even when I ask for it * Refuse to make clear whether they rely on original documents or an internal summary of the document, until I point out errors * ...

I also have substance gripes, but for me such basic usability points are really something all of the chatbots fail on abysmally. Stick to instructions! Stop creating walls of text for simple queries! Tell me when something is uncertain! Tell me if there's no data or info rather than making something up!

<< I feel there is a point when all these benchmarks are meaningless.

I am relatively certain you are not alone in this sentiment. The issue is that the moment we move past seemingly objective measurements, it is harder to convince people that what we measure is appropriate, but the measurable stuff can be somewhat gamed, which adds a fascinating layer of cat and mouse game to this.

Once a metric becomes optimization target, it ceases to become good metric.

There's a leaderboard that measures user experience, the "lmsys" Chatbot Arena Leaderboard ( https://huggingface.co/spaces/lmarena-ai/lmarena-leaderboard ). Main issue with it these days are that it kinda measures sycophancy and user preferred tone more than substance.

Some issues you mentioned like length of response might be user preference. Other issues like "hallucination" are areas of active research (and there are benchmarks for these).

The latest of the big three... OpenAI, Claude, and Google, none of their models are good. I've spent too much time monitoring them than just enjoying them. I've found it easier to run my own local LLM. The latest Gemini release, I gave it another go but only for it to misspell words and drift off into a fantasy world after a few chats with help restructuring guides. ChatGPT has become lazy for some reason and changes things I told it to ignore, randomly too. Claude was doing great until the latest release, then it started getting lazy after 20+k tokens. I tried making sure to keep a guide to refresh it if it started forgetting, but that didn't help.

Locals are better; I can script and have them script for me to build a guide creation process. They don't forget because that is all they're trained on. I'm done paying for 'AI'.

What's to stop you from using the APIs the way you'd like?

I'm not an expert but my understanding is transformers based models simply can't do some of those things, it isn't really how they work.

Especially something like expressing a certainty %, you might be able to get it to output one but it's just making it up. LLMs are incredibly useful (I use them every day) but you'll always have to check important output

Consider using structured output. You can define a JSON with specific fields, and LLMs are only used to fill in the values.

https://ai.google.dev/gemini-api/docs/structured-output

What are your best local models, and what hardware do you run them on?

I'm always impressed how fast people get used to new things. couple of years ago something like chatgpt was completely impossible, and now people complain it something's does mit do what you told it to and sometimes lies. (not saying your points are not valid or you should not raise them) Some of the points are just not fixable at this point due to tech limitations. A language model currently simply has no way to give an estimate of its confidence. Also there is no way to completely do away with hallucinations (lies). there need to be some more fundamental improvements for this to work reliably.

The API is a way to access a model, he is criticizing the model not the access the method (at least until the last sentence where he incorrectly implied you can only script a local model, but I don’t think thats a silver bullet, in my experience that is even more challenging than starting with a working agent)