61–70 of 254 posts
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The Kimi problem is it doesn’t follow instructions and goes off track often. Other than that it’s pretty decent (for the price).
Sounds like it was distilled from Claude. I don't understand the appeal of an agent that does whatever it wants.
Personally, when I use open code or routers, I feel that beyond a certain level, the models don't make a huge difference to me. Except for expensive and mediocre models like Gemini. In that sense, Chinese models are pretty good. I usually write code in function or method units and then design and assemble them together. GPT series models are more thorough and better, but I'm not sure if the difference is enormous. It…
Personally, when I use open code or routers, I feel that beyond a certain level, the models don't make a huge difference to me. Except for expensive and mediocre models like Gemini. In that sense, Chinese models are pretty good. I usually write code in function or method units and then design and assemble them together. GPT series models are more thorough and better, but I'm not sure if the difference is enormous. It…
I really hope we stop using the term "Chinese models". It has this air of Negative connotation. It's the equivalent of calling cars Japanese, which people used to do but now is almost entirely meaningless. You just call them Toyota, Honda, Lexus etc.
I was wondering how does Anthropic and likes keep competitive when Opus is ($5 / $25) 5x times more expensive compared to Kimi K2.6 ($0.7 / $3.4) or other Chinese models, while being only marginally better. My theory is that US enterprise just can't send data to Chinese and that's understandable, but is that "the moat"?
I also wonder if Enterprises have deals for other API pricing that is not posted publicly, so all we see is a high API sticker price.
I am still very new to the open-weight/source models. If anyone is using them full-time, I’d really love to hear about the setup and how they perform, as I am considering moving my org off Anthropic products.
If you look at a file like:
https://github.com/gitsense/gsc-cli/blob/main/internal/cli/r...
you can see that I attribute the models used. What I found was 4.7 was not very good at `go` code which was why you started to see `Gemini 3 Flash` in the attributions.
4.7 is what Cerebras provide and for me, speed in iterations is a lot more important. Having played around with MiMo v2.5.0-Pro, I am 100% sure it could have done what Gemini 3 Flash did.
There were a few points where I was stuck and needed Sonnet to explain things to me, but I think the dirty secret that Anthropic and OpenAI won't tell you is, if you know how to code, the models are honestly good enough.
Based on my experience with MiMo and what others are saying about GLM 5.1, we are now in a hardware race. The Chinese Models are 100% drop in replacement for Claude if you know how to program but want to AI to help amplify what you know. What I will consider now is what provider can provide the fastest inference.
MiMo-v2.5.0-Pro-Ultraspeed is really good at generating good results quickly and burning your money as fast.
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Ah is that what it is? I don't use Cursor, never saw it as being relevant to me, but would not surprise me.
Cursor's composer models are finetuned kimi
I think any new model not demonstrably maybe 20-30% over Deepseek v4 capabilities priced over the price per token of Deepseek is almost automatically deprecated as low use model (maybe for Planning).
Is Deepseek just eating cost or are people able to host their open models for comparable costs?
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Cursor's composer models are finetuned kimi
They are unusable (unless you want to deliberately destroy your codebase). So if Cursor's models are Kimi based, then well. I'll skip them altogether.