Has anyone else noticed this? Whenever I'm working on bigger projects, doing major code refactoring or even working on parts of my own engine Betelgeuse (especially the parts which are beyond my understanding or i couldn't fix by hand), most models tend to give up surprisingly quickly. And I don't mean they fail to solve the problem. They just stop trying. Give them four separate things to implement and they'll complete one, maybe partially do another, and then wrap up as if the entire task is finished. Yesterday I was working on Betelgeuse's custom kernel which had a weird PP bug I wasn't able to fix. So I tried pretty much everything I had. Free models through OpenCode Zen, OpenRouter and almost every other provider I could find. Then my paid Codex plan and even borrowed someone's Claude Max to test on a new worktree. Models that managed to find and fix the bug: Opus 5.5 Max GPT-6 Astra (also tried 6.1 Sol Max. It was cheaper but not as good) Sonnet 5.5 Max Kimi K3.1 Pro Muse Wonder Max Haiku 5.5 (didn't finish the entire task but managed to identify the bug) Models that gave up or couldn't complete it: Gemini 3.8 Flash (Worst Prop model) GLM 5.3 (burns a LOT of thinking tokens and is quite slow) DeepSeek V4.1 Flash (also burns a lot of thinking tokens and is quite slow) Qwen 3.8 Max MiMo V2.6 Pro MiniMax M3.1 Flash (THE WORST) Qwen 3.8 Flash-Next (Full Q8_XL, burned 178K tokens in thinking before I stopped it) Qwen 3.8 27B (burned through the entire 262K context) I've noticed this quite a lot with open models. DeepSeek V4.1 Flash and GLM 5.3 were the only ones that came close to consistently pushing through the task, although they still missed a few things and never found it Meanwhile GPT-6 Astra and Opus 5.5 have been considerably better at this in my experience. They actually keep working until the specified work is done. Astra is still working on Betelgeuse's kernels as I'm writing this. So far it has managed to squeeze over 10x the prefill speed I was getting with DeepSeek V4.1 Flash Q8_XL (508.4GB). From 7 PP/s to around 70 PP/s. Decode also improved from around 10 TG/s to 14 TG/s. Hardware: RTX 3060 + 16GB RAM + NvME SSD. I'm curious if anyone else has noticed this, especially when working on larger codebases rather than isolated coding tasks. Is this just my experience or are others seeing the same thing? Ready to get downvoted :)   submitted by   /u/zyxciss [link]   [comments]