Comment by wyrdcurt
14 hours ago
Tinfoil hat time: but the way they waited a couple weeks to evaluate Mimo 2.6 Flash, allowing GPT-6 Luna to sit on the Pareto frontier in the meantime, really did make me think they might have some bias going on... (Luna was displaced when they finally evaluated Mimo 2.6 Flash, which had been released the day before Luna)
edit to add: there totally is benchmaxxing going on, but I'd be really surprised if it wasn't across the board. After all, how can any lab tell if their model has improved over a previous iteration, if not by comparing benchmark performance?
I’ve very mixed feelings on Mimo. The new models do surprisingly well on some tasks, but they don’t allow you to set the thinking level and the model often times spends next to zero time thinking about a problem. If you coax it just a little bit, you can get it to solve it, but this isn’t an issue with other models. Also it’s incredibly easy to get it to get caught in a thinking loop.
I've personally experienced overthinking and the thinking loop issue more often than seeing it not think enough, I know what you mean though. I tried Haiku 5.5 and the most impressive thing about it to me was that it seemed not to overthink when it wasn't necessary, yet it somehow knew when it should spend some more time reasoning about a task.
American models are too rich for my blood though. I use GLM 5.3 Flash more than any other model and it has the same issues as Mimo. Deepseek 4.1 Flash is turning out to be a sweet spot for me; it's cheap and blazing fast on CoralBricks where I've been using it, so I don't mind when it overthinks (I try to pay attention to what my agents are doing, and it tends to finish tasks before I can skim half of its reasoning)