User rates model retrieval skills, keeps Astra but skips Sol
The author treats LLMs as information retrievers and rates Opus 5.5, Grok 4.7, GLM, DeepSeek, Kimi, Qwen, Nemotron, Gemini, plus OpenAI's Luna, Astra and Sol, calling Astra great but saying GPT-6.1 Sol doesn't click for them.
For some reason a lot of you find this interesting so I'll try to explain this a little. To me LLMs are simply big text summarizers, I want them to be good at retrieving relevant information and what potentially would be relevant as well so I don't need to go back and forth with it. The only mode I'll let this slide with is Opus 5.5, it is the WORST frontier model when it comes to searching information. Grok 4.7 is the best one by far at this simply because it has access to X. GLM and Deepseek are surprisingly good at this too, Kimi is only good at this if you have Agent Swarm enabled. Qwen 3.8 27B and Nemotron 3.5 Lightning, I will stay on this hill. Nemotron 3.5 Lightning is the most underrated model from Nvidia ever. It is so quick and so smart that it is incredible. Everyone loves Qwen 3.8 27B, I don't think I need to explain much, capable, small enough to host locally but overthinks. It has an existential crisis every time you ask it a question. I love Kimi's personality, if Kimi ever makes a model that is 400B or less I will without a single doubt in my mind run it locally. We stan Kimi. Gemini. Fuck you. OpenAI models, over hated, Luna specifically is awesome. Astra is great but for some reason I can't use GPT 6.1 Sol there is something about that model that just doesn't sit with me. It doesn't vibe with me.
This post quotes another post: x.com/gnukeith/status/2111543443805851945