This is partly the appeal of Jev et al; having a quick model for simple tasks, that doesn’t require that much thinking
It’s amazing all the workflows that models like that can unlock. And yes, classifiers and other ML models have been around for a while for these types of tasks, but Jev has made it easy and cheap to play and experiment. This in turn, is incentivizing people to try them for a bunch of stuff, unlocking creativity and producing a lot of new cool (and eventually potentially very useful) applications
Technically kimi k-3 weights license is not open weight (it has a lot of restrictions). I would classify it as ‘weight open’ similar to the bsl and fsl ’source open’ licenses.
Ignoring for the moment issues of what "counts" as open, won't open models rapidly advance due to stuff like this in ways that it's less possible for the proprietary ones to do? This is exactly how Linux & Wikipedia, for example, overtook their "frontiers", right?
> Ignoring for the moment issues of what "counts" as open, won't open models rapidly advance due to stuff like this in ways that it's less possible for the proprietary ones to do? This is exactly how Linux & Wikipedia, for example, overtook their "frontiers", right?
I suspect the advantage that catapulted Linux ahead of the establishment was less technical potential and talent and more organizational advantage. That's not to diminish the technical talent of the Linux crew, but them being unencumbered gave them more degrees of freedom. The rest is history.
So as long as the AI companies don't succumb to "big company" dynamics, they can outlead. To wit: Open AI and Anthropic are kicking Google's ass.
Diff people have diff motives to experiment, then new work is done on top of stuff that "hits" in a way no one anticipated. Then work gets piled on top in a way that might make it hard to port
> then new work is done on top of stuff that "hits" in a way no one anticipated.
Indeed. And when you have freedom to play, you are able to find new stepping stones that you didn't anticipate. And you can combine stepping stones in new ways to make new discoveries.
not necessarily, just knowing something is possible will motivate others to achieve it somehow. Which is why there are so many LLMs and OAI doesn't have a monopoly
Off topic:With sol pricing drop tbh kimi k3’s value prop has not been that great. For our internal use case/testing/benchmarks sol come out with way better quality and much cheaper costs.
Kimi really needs to drop their pricing (I heard it’s set by them across all the neoclouds)
Sol is at 2/10 vs kimi’s 3/15
The result? Ember-1 set a new Pareto frontier for Bedside Bench across both open and closed models including GPT-5.6 Sol, GPT-6 Astra, and Claude Opus 5 on cost/task.
The pareto frontier needs clearer distinction. Benchmarks miss half the story. What, if any, capability is lost by the token reduction (for example, was it like super awesome at Golang before and now kind of sucks? that kind of distinction).
This is really interesting. I think the Fireworks Serverless Training infrastructure they used to develop it is also unique and needed. Except if someone works at one of a handful of the largest labs, it is very difficult to set up or try any sort of training pipeline. The managed training infrastructure makes it available to more people.
On the smaller end, Quen 3.8, while being extraordinarily capable for a small local model, also suffers from extreme thinking. I wonder if the techniques described here generalize to other models too.
I suspect it might generalize to other large models, but I don't think Qwen3.8 27B is one of them. Kimi K3 is a 2.8 trillion parameter model, and I suspect that is playing a big role in being able to reduce the length of CoT without taking a hit in quality.
> The problem: thinking models think too much
This is partly the appeal of Jev et al; having a quick model for simple tasks, that doesn’t require that much thinking
It’s amazing all the workflows that models like that can unlock. And yes, classifiers and other ML models have been around for a while for these types of tasks, but Jev has made it easy and cheap to play and experiment. This in turn, is incentivizing people to try them for a bunch of stuff, unlocking creativity and producing a lot of new cool (and eventually potentially very useful) applications
So they trained a model on open weights, and then aren't releasing the weights... am I reading this right?
It happens. Most open licenses aren't GPL style copyleft.
Technically kimi k-3 weights license is not open weight (it has a lot of restrictions). I would classify it as ‘weight open’ similar to the bsl and fsl ’source open’ licenses.
Ignoring for the moment issues of what "counts" as open, won't open models rapidly advance due to stuff like this in ways that it's less possible for the proprietary ones to do? This is exactly how Linux & Wikipedia, for example, overtook their "frontiers", right?
> Ignoring for the moment issues of what "counts" as open, won't open models rapidly advance due to stuff like this in ways that it's less possible for the proprietary ones to do? This is exactly how Linux & Wikipedia, for example, overtook their "frontiers", right?
I suspect the advantage that catapulted Linux ahead of the establishment was less technical potential and talent and more organizational advantage. That's not to diminish the technical talent of the Linux crew, but them being unencumbered gave them more degrees of freedom. The rest is history.
So as long as the AI companies don't succumb to "big company" dynamics, they can outlead. To wit: Open AI and Anthropic are kicking Google's ass.
Diff people have diff motives to experiment, then new work is done on top of stuff that "hits" in a way no one anticipated. Then work gets piled on top in a way that might make it hard to port
> then new work is done on top of stuff that "hits" in a way no one anticipated.
Indeed. And when you have freedom to play, you are able to find new stepping stones that you didn't anticipate. And you can combine stepping stones in new ways to make new discoveries.
Greatness cannot be planned.
No, because close labs/models borrow but don't contribute back.
Yes, absolutely, but only if people keep contributing in the open.
not necessarily, just knowing something is possible will motivate others to achieve it somehow. Which is why there are so many LLMs and OAI doesn't have a monopoly
Off topic:With sol pricing drop tbh kimi k3’s value prop has not been that great. For our internal use case/testing/benchmarks sol come out with way better quality and much cheaper costs. Kimi really needs to drop their pricing (I heard it’s set by them across all the neoclouds) Sol is at 2/10 vs kimi’s 3/15
Agreed. Even on the open weight side, GLM 5.3 has roughly equivalent performance to Kimi K3 for less than half the cost.
> The problem: thinking models think too much
Analysis paralysis stifles not just human intelligence, but other intelligences too.
Do they mean Opus 5.5 or Opus 5?
The result? Ember-1 set a new Pareto frontier for Bedside Bench across both open and closed models including GPT-5.6 Sol, GPT-6 Astra, and Claude Opus 5 on cost/task.
"Pareto": 8 hits
"Opus 5.5": zero hits
Well done, and great iteration.
The pareto frontier needs clearer distinction. Benchmarks miss half the story. What, if any, capability is lost by the token reduction (for example, was it like super awesome at Golang before and now kind of sucks? that kind of distinction).
This is really interesting. I think the Fireworks Serverless Training infrastructure they used to develop it is also unique and needed. Except if someone works at one of a handful of the largest labs, it is very difficult to set up or try any sort of training pipeline. The managed training infrastructure makes it available to more people.
Does anybody know if this would be a good model for creative writing?
Need this done for DeepSeek, ideally one of the Flash models.
If you have the compute, I have the expertise.
On the smaller end, Quen 3.8, while being extraordinarily capable for a small local model, also suffers from extreme thinking. I wonder if the techniques described here generalize to other models too.
I suspect it might generalize to other large models, but I don't think Qwen3.8 27B is one of them. Kimi K3 is a 2.8 trillion parameter model, and I suspect that is playing a big role in being able to reduce the length of CoT without taking a hit in quality.
That's just vibes, though.
It looks like it would be similar to GLM 5.3 Flash, had they tested it...
I don't think the article mentions Pareto frontier enough.
Also, did I miss a memo? Suddenly every article on AI seems to be talking about the Pareto frontier - or have I just not been paying attention?
I guess they figure "best bang for your buck" comes off a little too colloquial.
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