Kimi K2.7-Code: open-source coding model with better token efficiency
71–80 of 254 posts
Re: Kimi K2.7-Code: open-source coding model with better token efficiency
#72Personally, 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.
Re: Kimi K2.7-Code: open-source coding model with better token efficiency
#73Personally, 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.
Re: Kimi K2.7-Code: open-source coding model with better token efficiency
#74Earlier quoted context omitted.
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.
You are right. I agree.It may seem like a kind of bias, but I hadn't thought of that part. Thank you for pointing out my bias.
Re: Kimi K2.7-Code: open-source coding model with better token efficiency
#75Re: Kimi K2.7-Code: open-source coding model with better token efficiency
#76Reading their modified license terms, it cracks me up, because they've basically remade the MIT to be the MIT + the one clause that the BSD used to have, which didn't care about MAU or revenue, if you used it in a product, they asked you to 'advertise' them basically. Honestly, its a reasonable request.
This is the cursor callout. Don't make us shame you into disclosure
Re: Kimi K2.7-Code: open-source coding model with better token efficiency
#77I 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.
Use DCP or Magic Context plugin in OpenCode to keep the context below 160k and you're fine.
Re: Kimi K2.7-Code: open-source coding model with better token efficiency
#78Earlier quoted context omitted.
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.
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I have a feeling you'd be slightly salty at people saying "Google and Tesla are making CIA models"
Re: Kimi K2.7-Code: open-source coding model with better token efficiency
#79Earlier quoted context omitted.
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.
[flagged]
Re: Kimi K2.7-Code: open-source coding model with better token efficiency
#80Earlier quoted context omitted.
I'm not sure I would put too much weight on DeepSWE as a benchmark, given that GPT-5.4-mini ended up close to Opus 4.6 there.
Any benchmark is iffy and has weird results, but this is the best we got at the moment. Most people working with Opus and Kimi would likely tell you they're much further apart than the numbers that were quoted for Kimi K2.6, and DeepSWE seems to capture that gap better. One major thing DeepSWE has going for it is that all other benchmarks (including those quoted by MoonshotAI on this page) don't: the other benchmarks…