Do AI models still keep getting better, or have they plateaued?
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The AI hype is based on the assumption that the frontier AI labs are producing better and better foundational models at an accelerating pace. Is that really true, or are people just in sort of a mass psychosis because AI models have become so good at mimicking human behavior that we unconsciously attribute increasing intelligence to them? I decided to conduct a mini-benchmark of my own to find out if the latest and greatest AI models are actually really good or not.\n

I tested 9 flagships (Claude 4.6, GPT-5.2, Gemini 3.1 Pro, Kimi K2.5, etc.) in my own mini-benchmark with novel tasks, web search disabled and zero training contamination and no cheating possible.

TL;DR: Claude 4.6 is currently the best reasoning model, GPT-5.2 is overrated, and open-source is catching up fast, in particular Moonshot.ai’s Kimi K2.5 seems very capable.

  • T3CHT @sh.itjust.worksEnglish
    41·
    19 hours ago

    Thanks for sharing, interesting read and questions. Surely you’ll be down voted here for anything with AI… But c’est la vie.

    Ive been doing coding projects in VS code which uses GPT, Claude and Gemini. Woe are the days when my credits are used and only GPT 4.1 is available. Claudes ability to research and architect multi step software solutions is very, very good and it rarely makes messes or spins tires compared to older models from just a few months ago. This is precisely what converted me to ‘whoa - ai’ which is adjacent to ‘pro ai’.

    Lately I’ve been experimenting with customizing Gemini via instructions which include a link to a drive folder of md files with specific instructions for different agent tasks, such as performing specific market analysis, doing a news roundup with a specific list of topics and omitting prior reviewed items, etc. The files allow for both complex instructions or lists, as well as some chance to construct memory via logging. Results are a mixed bag, lots of additional function created, lots of mixed results.

    Have you considered any tests of more complexity? Something like ‘write a program that…’ I think what will differentiate these models going forward is some have architect capabilities, strategy, insight, decision making, where others are agents - they do specific tasks well but have limits. With that model, the ai architect and it’s ai agents need to work as a team to complete a multi step task.