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Kimi K2.7-Code: open-source coding model with better token efficiency

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Re: Kimi K2.7-Code: open-source coding model with better token efficiency

#41

Post by kamranjon »

bgins wrote: post #3

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.

I have been using deepseek v4 flash as my main model for everything ever since dwarf star came out. I run it on my M4 Max MacBook Pro with 128gb of memory. I run it usually as a server and connect to it over tailscale with my coding machine and use the Pi coding agent. It’s a big leap over using the Qwen models though it doesn’t have vision - so I still will run those when I use vision. GLM 4.7 flash was my previous go to for coding but I’ve completely switched to deepseek for all non-vision things.

Re: Kimi K2.7-Code: open-source coding model with better token efficiency

#42

Post by papersail »

DCKing wrote: post #27
yanis_t wrote: post #2

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"?

The moat right now is model performance and what that means for how many tokens and additional time you spend. I say this as a relatively frequent user of Kimi models and generally a big fan. But on not-yet-gamed benchmarks like DeepSWE, Kimi K2.6 is beaten soundly by Claude Sonnet 4.6 ($3 / $15) and even slightly by GPT 5.4 Mini ($0.75 / $4.50). There's no question Kimi models are very good for a lot of code tasks.…

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.

Re: Kimi K2.7-Code: open-source coding model with better token efficiency

#43

Post by jdw64 »

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 seems to depend on the workflow, but in my opinion, if you are thorough enough, I wonder if there really is a big difference

Re: Kimi K2.7-Code: open-source coding model with better token efficiency

#44

Post by efromvt »

yanis_t wrote: post #2

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 think the perception is that it is not 'only marginally better'; whether or not you specifically agree that perceived quality gap lets them differentiate on price.

I'd further say that there are probably enough rational actors running evals out there that the marginally better is not pure vibes for the cases where people are spending lots of money, but I only have direct line of sight to some of those eval suites. Maybe everyone is irrational and anthropic is exploiting that!

Re: Kimi K2.7-Code: open-source coding model with better token efficiency

#46

Post by marcyb5st »

bgins wrote: post #3

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.

Anecdotal, but here's my experience.

For personal stuff I use forgecode with openrouter. Firstly, forgecode is a much better harness than Cloude code (IMHO).

Anyway, regarding the models, my experience is that there is not much difference in terms of quality, but the cost difference is insane. At least for how I use agents. Yesterday's example is the following: I am developing a small DSL for search across complex technical documents. I wanted to add a small operator to it and thought that to give fable a spin. It burned through 13 USD and while it delivered the solution it wasn't objectively better than what Deepseek v4 did for 1.7 dollars (same exact task because I was curious).

For full disclosure, I ask agents for piecemeal stuff. Like in the DSL case, I designed the operators and then asked agents to implement them one by one. Probably if I asked to design the whole thing starting from these complex documents Fable would shine, but every time I try to give agents broader scope tasks they burn through millions of tokens, generate questionable code, which I have to spend time familiarize myself with.

Re: Kimi K2.7-Code: open-source coding model with better token efficiency

#48

Post by onlyrealcuzzo »

jdw64 wrote: post #43

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…

In my experience, there's little difference between implementing individual functions between frontier models and SotA ~30B param models.

Once you have a coherent design (the hard part), you can feed it to a pretty small model and get basically the same quality.

They'll not one-shot, but they're faster and cheaper, so it still works out in your favor.

Plus you can do it locally...

Re: Kimi K2.7-Code: open-source coding model with better token efficiency

#49

Post by htrp »

giancarlostoro wrote: post #40

Reading 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

#50

Post by esperent »

DCKing wrote: post #27
yanis_t wrote: post #2

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"?

The moat right now is model performance and what that means for how many tokens and additional time you spend. I say this as a relatively frequent user of Kimi models and generally a big fan. But on not-yet-gamed benchmarks like DeepSWE, Kimi K2.6 is beaten soundly by Claude Sonnet 4.6 ($3 / $15) and even slightly by GPT 5.4 Mini ($0.75 / $4.50). There's no question Kimi models are very good for a lot of code tasks.…

I'm more interested in how much effort I have to put in, at least while I'm paying in the range of current subscriptions (so ~€100-€200 a month or so). If the prices go up much more than that I'll have to switch to caring more about token efficiency. But at current pricing the bottleneck is my attention, not model efficiency. As such, even a small improvement in model quality - and hence, a decrease in how much attention I have to spend on it - makes a big difference.
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