There's no comparison. GLM 4.7 is fine and reasonably competent at writing code, but K2.5 is right up there with something like Sonnet 4.5. it's the first time I can use an open-source model and not immediately tell the difference between it and top-end models from Anthropic and OpenAI.

Kimi k2.5 is a beast, speaks very human like (k2 was also good at this) and completes whatever I throw at it. However, the glm quarterly coding plan is too good of a deal. The Christmas deal ends today, so I’d still suggest to stick to it. There will always come a better model.

From what people say, it's better than GLM 4.7 (and I guess DeepSeek 3.2)

But it's also like... 10x the price per output token on any of the providers I've looked at.

I don't feel it's 10x the value. It's still much cheaper than paying by the token for Sonnet or Opus, but if you have a subscribed plan from the Big 3 (OpenAI, Anthropic, Google) it's much better value for $$.

Comes down to ethical or openness reasons to use it I guess.

It's waaay better than GLM 4.7 (which was the open model I was using earlier)! Kimi was able to quickly and smoothly finish some very complex tasks that GLM completely choked at.

The old Kimi K2 is better than GLM4.7

Is the Lite plan enough for your projects?

> It is possible to run locally though

> running one of the heavily quantized versions

There is night and day difference in generation quality between even something like 8-bit and "heavily quantized" versions. Why not quantize to 1-bit anyway? Would that qualify as "running the model?" Food for thought. Don't get me wrong: there's plenty of stuff you can actually run on 96 GB Mac studio (let alone on 128/256 GB ones) but 1T-class models are not in that category, unfortunately. Unless you put four of them in a rack or something.

Open source models costs are determined only by electricity usage, as anyone can rent a GPU qnd host them Closed source models cost x10 more just because they can A simple example is Claude Opus, which costs ~1/10 if not less in Claude Code that doesn't have that price multiplier

Open source models can be hosted by provider, in particular plenty of educational institutions host open source models. You get to choose whatever provider you trust. For instance I used DeepSeek R1 a fair bit last year but never on deepseek.com or through its API.

* It's cheaper than proprietary models

* Maybe you don't want to have your conversations used for training. The providers listed on OpenRouter mention whether they do that or not.

You can run it on consumer grade hardware right now, but it will be rather slow. NVMe SSDs these days have a read speed of 7 GB/s (EDIT: or even faster than that! Thank you @hedgehog for the update), so it will give you one token roughly every three seconds while crunching through the 32 billion active parameters, which are natively quantized to 4 bit each. If you want to run it faster, you have to spend more money.

Some people in the localllama subreddit have built systems which run large models at more decent speeds: https://www.reddit.com/r/LocalLLaMA/

You need 600gb of VRAM + MEMORY (+ DISK) to fit the model (full) or 240 for the 1b quantized model. Of course this will be slow.

Through moonshot api it is pretty fast (much much much faster than Gemini 3 pro and Claude sonnet, probably faster than Gemini flash), though. To get similar experience they say at least 4xH200.

If you don't mind running it super slow, you still need around 600gb of VRAM + fast RAM.

It's already possible to run 4xH200 in a domestic environment (it would be instantaneous for most tasks, unbelievable speed). It's just very very expensive and probably challenging for most users, manageable/easy for the average hacker news crowd.

Expensive AND hard to source high end GPUs, if you manage to source for the old prices around 200 thousand dollars to get maximum speed I guess, you could probably run decently on a bunch of high end machines, for let's say, 40k (slow).

You can run it on a mac studio with 512gb ram, that's the easiest way. I run it at home on a multi rig GPU with partial offload to ram.

That would be quite the space heater, too!