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Theoretically but I've used DeepSeek V4.1 Flash for several hundred millions of tokens already and it chews through tokens but it is surprisingly good at making it to the end.

MiMo V2.6 Pro I want to love, but I've hit three deathloops in a row. Either my luck is catastrophically bad, or someone needs to patch vLLM or something.

I am sure DeepSeek V4.1 Flash can deathloop, too, but so far it feels less prone to it than other models I've tried like GLM 5.3 Flash so, I'm impressed so far.

I always wonder what the deal with these failure modes are. Google, OpenAI and Anthropic seem to have found good enough workarounds, and I am surprised I don't hear more people talking about them. I thought maybe it was shitty broken providers on OpenRouter, but then I started making presets just for using only the upstream provider and found that no, really, the models do fail that way.

Which is a shame because on paper MiMo V2.6 Pro seems strong, but I haven't gotten through a hard task with it yet.

by eli3 hours ago|

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I read they identified a training bug and were going to push out an updated release to fix the looping. I really like it overall.

GLM 5.3 Flash is also very good. I think a little smarter and a little more expensive.

by jchw50 minutes ago|

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I did like GLM 5.3 Flash but it's just way too often I'd run it on some long running task and come back to it repeating the same tokens or tool calls endlessly, just doing nothing. It wasn't unusable, but I couldn't trust it. That's really frustrating and I think new models have to do better not just on benchmark scores but general reliability and user experience as well.

At some point Anthropic and OpenAI models definitely could fall into similar traps so I do think it is a solvable problem and likely not a reflection of the models themselves being bad. In this case it may indeed be a training bug of some kind, but I also suspect mitigations on the inference side are possibly lacking or not effective enough for the open models and their runtimes.