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GPT-5.2

openai.com

431–440 of 1001 posts

Re: GPT-5.2

#431

It's actually more expensive than GPT-5.1. I've gotten used to prices going down with each latest model, but this time it's gone up. https://platform.openai.com/docs/pricing

Gemini 3 Pro Preview also got more expensive than 2.5 Pro. 2.5 Pro: $1.25 input, $10 output (million tokens) 3 Pro Preview: $2 input, $12 output (million tokens)

Literally no difference in productivity from a free/ $1.00+ per mm output are literal scams. No added value to the world.

Re: GPT-5.2

#432
>GPT‑5.2 sets a new state of the art across many benchmarks, including GDPval, where it outperforms industry professionals at well-specified knowledge work tasks spanning 44 occupations.

We built a benchmark tool that says our newest model outperforms everyone else. Trust me bro.

Re: GPT-5.2

#434

I suppose this is as good a place as any to mention this. I've now met two different devs who complained about the weird responses from their LLM of choice, and it turned out they were using a single session for everything. From recipes for the night, presents for the wife and then into programming issues the next day. Don't do that. The whole context is sent on queries to the LLM, so start a new chat for each topic.…

Problem is that by default ChatGPT has the “Reference chat history” option enabled in the Memory options. This causes any previous conversation to leak into the current one. Just creating a new conversation is not enough, you also need to disable that option.

This is also the default in Gemini pretty sure, at least I remember turning it off. Make's no sense to me why this is the default.

Re: GPT-5.2

#436
Looks like they've begun censoring posts at r/Codex and not allowing complaint threads so here is my honest take:

- It is faster which is appreciated but not as fast as Opus 4.5

- I see no changes, very little noticeable improvements over 5.1

- I do not see any value in exchange for +40% in token costs

All in all I can't help but feel that OpenAI is facing an existential crisis. Gemini 3 even when its used from AI Studio offers close to ChatGPT Pro performance for free. Anthropic's Claude Code $100/month is tough to beat. I am using Codex with the $40 credits but there's been a silent increase in token costs and usage limitations.

Re: GPT-5.2

#437
Undoubtedly each new model from OpenAi has numerous training and orchestration improvements etc.

But how much of each product they release also just a factor of how much they are willing to spend on inference per query in order to stay competitive?

I always wonder how much is technical change vs turning a knob up and down on hardware and power consumption.

GTP5.0 for example seemed like a lot of changes more for OpenAI's internal benefit (terser responses, dynamic 'auto' mode to scale down thinking when not required etc.)

Wondering if GPT5.2 is also case of them in 'code red mode' just turning what they already have up to 11 as a fastest way to respond to fiercer competion.

Re: GPT-5.2

#438
ChatGPT seems to just randomly pick urls to cite and extract information from. Google Gemini seems to look at heuristics like whether the author is trustworthy, or an expert in the topic. But more advanced

Re: GPT-5.2

#439
Excited to try this. I’ve found Gemini excellent recently and amazing at coding. But I still feel somehow like ChatGPT understands more. Even though it’s not quite as good at coding - and nowhere at as fast. It is much less likely anti spontaneously forget something. Gemini’s is part unbelievably amazing and part amnesia patient. I still kinda trust ChatGPT more.

Re: GPT-5.2

#440
> Models were run with maximum available reasoning effort in our API (xhigh for GPT‑5.2 Thinking & Pro, and high for GPT‑5.1 Thinking), except for the professional evals, where GPT‑5.2 Thinking was run with reasoning effort heavy, the maximum available in ChatGPT Pro. Benchmarks were conducted in a research environment, which may provide slightly different output from production ChatGPT in some cases.

Feels like a Llama 4 type release. Benchmarks are not apples to apples. Reasoning effort is across the board higher, thus uses more compute to achieve an higher score on benchmarks.

Also notes that some may not be producible.

Also, vision benchmarks all use Python tool harness, and they exclude scores that are low without the harness.

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