I would really love if we brought back some colloquialisms in this field. Not that long ago most folks in tech would have had pretty blank looks on their faces when someone started talking about the "Pareto frontier"
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).
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?
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
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.
I think people make mistake here, google’s approach is not to spend $2.3 on every $1.0 earned, they’re riding on serving to masses “luna”, they absolutely have way more powerful models internally but they don’t clutter their infrastructure with fragile and costly intelligence-of-size inference frontier. I think “underdog” perception is illusory/temporary, not stupidity - calculated, conscious, longer term bet.
I tend to agree, but I also should highlight how expensive this shit really is.
in one month, Google actually went cash-negative. [0] even still, they are subsidizing their stuff a lot less, have the most opaque and variable limits, and increase adoption through bundling and shuffling features. I can't even share my Google One storage without subscribing to a Google AI plan anymore, but previously any plan except Google One Lite was shareable.
if you tell me that's not enough to go after frontier, then how much money are Anthropic and OpenAI burning?
Where do you get this "not to spend $2.3 on ever $1.0 earned" from?
Google might not have compelling frontier offerings, their chat harness is complete garbage compared to any other lab (in large part due to a bizarrely badly designed harness where something like code execution requires the prompt to undergo some sort of classification step, no idea what they are doing).
But they absolutely kill in terms of usage offerings. Google lets one subscription be used by *SIX* different google accounts on a family plan.
Plus I currently literally get *$40/month* of Gemini API credits on developer.google.com because they gave me a $10/month grant 4 times.
They give you 200 cloud compute units on google collab, this literally lets you spin up an H100 for around 40 hrs or something if you want to try spinning up local models.
You get Jules (huge allotment btw), Image gen, Video gen, Music Gen, antigravity usage, 5 TB of cloud storage, Notebook LLM...
Okay 5tb cloud storage is their most expensive plan. But what do you actually use video or image gen for? Or music gen? Antigravity is garbage, I guess you can do some stuff with Gemini models over API.
I was paying for Google ai and then realized that between obscure limits and gimmick features I don't really need it. Canceled my subscription and didn't even notice a difference
It's an example number, which doesn't matter much but it comes from widely reported $2.30 spent on ops for every $1 of revenue in spring of 2025 for Anthropic.
The difference between contributing to OS and AI, is that the first is a hobby alternative to woodworking or hiking, while the other can easily bootstrap you a company you can get millions in investment, at least for time being.
"Oh darn, you know that thing I made and released with explicit, precise language defining who can use it and what, if any, restrictions apply? Well now someone is using it in complete accordance with those conditions I set out, and that's somehow making me upset"
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.
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.
Because we do. The GPL isn't a suggestion. If you can take open source code and make private software out of it then what are we all doing? No, license requirements and agreement are law for a reason.
There is little to no point reading the article as well. It's stripped of all alpha.
task and environment feedback
on-policy planning and learning
feedback connects decisions to their consequences
These are deliberately the least informative phrases you could possibly use to describe what you have done, while still being in the realm of words that go over a generic investor who has no idea whats going on and may be dazzled by sciencey sounding language.
Cursor compose 2.5 article where they used and described on policy self distilation was actual alpha.
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, I think the only place where it’s still interesting is ui design. Visually kimi and muse feel much nicer than frontier models to me, but maybe it’s an artifact of everything terrible being Claude Design
I was surprised by that. I run my benchmark [1] every couple of days and was sure this model will be ath the pareto frontier, if not THE pareto frontier. But no:
Ember isn't picked yet. In planning, Opus 5.5 wins under the planning weights. In code, GPT-6 Sol dominates it: also 10/10, but with a higher quality score and a lower estimated cost. Ember has no intelligence index, so its starting score is only 0.73, which holds its 10/10 down to 0.954 against Sol's 0.975.
Sol pricing dropped but so did the quality few days ago. I wonder when these companies are sued for making the terms from their side to go downwards while taking the same subscription cost.
Sure they are, but do you think they are continuing training to improve a model after release without bumping the version number, presumably only to game the benchmarks?
They're impossible to objectively track by design and intent. Intelligence is such a nebulous target that one can find any number of metrics to support any premise: that the model is smarter, and that the model is dumber. We use benchmarks to attempt to standardise comparisons but these are quickly ingested into the training data and then become effectively useless. You might have heard the term "bench-maxed." Meaning that a benchmark has an effective lifespan in months.
They've been using the Pelican test in the /r/Codex subreddit with some success. One major finding is that OpenAI has been silently degrading the model while charging Astra prices. Another finding is that even when the model has not been silently degraded, pelican quality is significantly lower. Sometimes comically so. The general consensus right now is that the new GPT-6 Sol model is an updated Terra model. Many intelligence metrics are roughly similar. Meaning the most recent model updates were an attempt to rebalance compute rather than improve intelligence.
Ultimately I've never seen users as upset about GPT-6 Sol/Luna than I have right now. Even Astra has been noticeably degraded for me and everyone else I have asked. This is compounded by the fact that Opus 5.5 is a generational improvement at an affordable price. There is currently no competition.
I am glad I am not the only one to notice. I feel like I've gone back to Sonnet 4 levels of incompetence!
With Sol 6 I am back in a world where the model writes bad code because it is lazy ("You're absolutely right, I did not [do it properly] because I did not want to edit [a normal amount of files]").
100%, I wish for a legislation which would require the providers to give you at least a unique hash identifying the model (and infra running it, if it affects output) - such that the same hash must give the same output given the same seed. Right now it's all just vibes
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.
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
Lots of use cases!
I've personally used it for the following:
1. Evals (once you have your rubric defined and tuned using a reasoning model, jev can be great for running periodic evals especially those that run daily.
2. e-commerce catalog classification
3. quick search using anything as context and query mapping to a pre-defined set.
LLMs can be too creative and often too verbose. Sometimes there is a right answer and a way to get there with the understanding of language, but despite using structured outputs, the model insists on inventing variations not in the schema or coming up with something completely different. A model like Jev that can not do those things, and can give the same output every time with given the same input, and be able to measure probabilities has many use cases.
LLM inference has two very different regimes of work: prefill & decode. You can think of the former roughly as processing a pre-specified prompt, and the latter as sequential processing (auto-regressive token generation) eg. "chain of thought". The latter is very important for LLMs and cannot be ignored; it deeply influences infra design, even necessitates copious amounts of high-bandwidth memory. Jev-like models can ignore the latter and therefore optimize much better for the former, consequently operating at both better cost and latency.
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.
Obviously this research was done before 6.0 Sol and Opus 5.5 came out. Your point stands that the frontier moves quickly and small gains can be eclipsed quickly.
This is the golden age of model training. Some days ago, I decided I wanted a local CPU only model that can perform exceptionally well for English to Bash translation (to avoid the googling for command syntax). I got a bunch of subagents to generate large amount of training data (140k+ samples), got the Qwen 3 0.6B base model, pointed Astra at it, and off to the races. It trained for 2 days (on and off) and I got a surprisingly good model for my task! The total active time I spent was a few hours. And it is still improving, what a time to be alive!
I didn't have a local GPU, so I asked it to go out and find hardware. It found a google TPU v6e which seemed reasonably priced. I gave it my google api key. I told it to use TPU only when training and bring it down afterwards. That's about it.
On the cloud side, nothing valuable existed, so the training couldn't ruin anything it didn't create. On the laptop side, I usually ask the agents to create named scripts for everything it needs to access, then those local script directory is green-lit with approve all. For cost, I kept giving it new budget in the 20-30 dollar increments.
I had to intervene a few times. For instance, as smart as the models are said to be (Astra), it would copy the full training run, train on the server, pull every checkpoint to the local machine, then run tests, update. So, the bandwidth bill was as high as training bill for the first 6 hours. It could have simply tested each checkpoint on the server, saved time and money, didn't occur to it until I said.
There’s a safer way to do this with nearly no added friction. Give it a read only API key. Then just ask it to write the API calls into a bash script and then read it and run it yourself. The agent can still inspect the live resources and diagnose and give you more commands to run. I do agree I wouldn’t give it create / write access.
I've done this sort of thing before but with Vast. Pre-deposited some money online, then let the LLM request and manage a training run on an allocation. Worked pretty well without risking bankruptcy.
I am thinking about opensourcing everything, although this is not my main domain or my main startup, so the overhead of huggingface etc seems a bit unnecessary
All synthetic data. For this usecase, it was easier because all current generation LLMs, even the small models, are really good at bash commands (and SQL queries too)), so you can reasonably start batches of cheap subagents whose output is reviewed by a more capable model and merge into main training set. After 100k, I had to standing instructions to run the generation loops selectively, meaning only update samples in a given area where we see poor capability.
That's a really impressive result. There are all kinds of small tasks like this I use an LLM for, but theoretically if you broke all the sub-use cases into local-only models, and had something lightweight that routed to the right model, you could have faster and cheaper workflows. E.g. something trained on the linux man pages for common commands, since it's usually quicker to ask an LLM for a specific command with flags than to consult the man pages.
The models he is using to generate training data are presumably commercial models. He is distilling their bash knowledge into a much smaller model he can run locally fast and cheap.
A very small, highly specialized model can use negligible resources (CPU, energy) to accomplish the same task.
For everyday work that happens frequently it's better to have a tiny specialized model instead of making billable API calls or turning your laptop into an 80W space heater for 20 seconds to run a general purpose model.
The large models can be used to generate synthetic training data. Tell them to make up 100,000 tasks paired with the resulting output as a 1-time cost. Then use that to train a small model.
Think of it as distillation, but focused on a specific task.
Given that they're just using it to avoid the googling for bash command syntax, I'm not sure they'll save in the end against the 140k training examples they generated.
I think OP's point remains, if you generate 140k pairs, your local model would need to run that many to offset having just used the generator (SOTA or not) model to begin with.
I wonder if another approach if latency is a concern is just to do a two shot pass with Jev (perhaps given small context you'd want one to match command, then one to match args of given command) would be an extremely fast, and cheap way to do it - rather than training your own.
If it's about the latency / flow disruption, spending a few hours once could easily be worth it if the result is actually good enough to skip googling/retries.
This example is not that niche. Lots of people use human to bash.
I'd probably use a small pre trained model if it was easy to use. I use a little script right now that calls a cheap model.
Actually.. $5 would probably will last me over a year so the only real benefit would be if I didn't have an internet connection.
You can buy a $10 subscription for a month to generate the training data, then cancel your subscription. The trained model is yours to use (and share with others) forever.
Yea specialized models could also be resold in a shareware style, like even if it cost 50$ to produce you'd just have to sell 10 copies of it to people for 5$.
Which 5$ is a pretty easy sell if its useful in any way, It's pretty easy to justify a purchase if its yours forever and doesn't use much CPU so is easy to run I mean people were spending 1000$+ on mac mini setups to run local llms or run remote agents.
This is so cool - I'm aware of this in a vague way. Can you write a little tutorial or give some good links. I want this to be the next new things I do :)
I feel like we need a good index for these kinds of specialized models, especially if you plan to open them up. The downside is a new bash version means potentially new training.
If it’s one of thing that you want just for English to bash shell commands, I will create AST, it is deterministic, exceptionally fast, no tokens so no need to fine tune existing model, please let me know your thoughts.
You can go quite far using a human language to Bash grammar based setup but at some point the input prompts are harder to translate. The OP has existing projects that work with AST quite deeply so I assume they know about that already.
I am building a natural language to CSV/Excel commands for a "wrangler" type desktop app. Same issues. The MVP is being built with parsers of sorts, entirely code generated. Then I want to fine-tune a tiny model at some point.
Golden age before the age that ends humanity. Not talking about any "rogue AI", just the known statistical models of what is coming due to climate change.
I'm literally working on context/harness engineering right now (a set of opencode plugins)
Aside on the aside, I welcome this new era of really personal software. Not Ai's being sycophants, rather being able to easily and quickly change, adapt, or extend software I am not familiar with.
Seriously, I'm using a Qwen 3.8 27B on the homelab, distilled from supposed Fable traces. Regardless, the difference is notable, less thinking, better output. Distilled / heavy quant is better than the original (imv)
I have been trying a mix of fine-tuning and I am amazed that most people do not see this coming.
A tiny, smaller than 1b parameter model, fine-tuned, can kick ass for constrained work. I do not have a lot of budget, I fine-tune only on a 16GB M4 Mac Mini. But that also tells me the potential is wild. Progress has been slow since I moonlight on this.
I have been trying to build a set of models + agents for full-stack development, where each model does only a small piece, like take user prompt and break into backend/frontend tasks. Then a Rust+Diesel model, a Rust+Auxum model, a Solid+Router model and so on. I know this is wild but this is just theory - can 5 or 6 Qwen 3.5 0.8b models do full-stack web development? My hunch says they can, better than what most people expect. Heck, with a good harness, it might beat all the cheaper models for the specific task, like Haiku or Luna.
I think a sub-set of people see this coming, I also think it isn’t just fine tuning open weight LLM models. A few people I know who are thinking along the same lines with architectures like BERT etc.
That being said it’s much easier at the moment to continue to use the frontier providers for most general tasks, that is the argument I’ve heard.
For creating these types of fine tuned local models, on constrained hardware for inference, I do think this is the way to go for specific tasks too!
I have a single command that fires up llama.cpp on cpu only using gemma4 e2b, answers a single question from the command line and exits. This takes about 3 seconds to load from an SSD, and is smart enough to solve exactly these "remind me of the syntax" scenarios if you dont wanna switch to a browser.
Over on /r/LocalLLaMA there's a group that's been getting popular doing the same thing for the Qwen 27B (and other) models. - https://huggingface.co/ukisai
Does anyone have a real-world usage feedback on this model? Because the benchmarks look, I would say, too good: it scores even higher than Qwen 3.8 27B in some benchmarks - I am not even sure how is it possible. So the question is if this is benchmaxxing or is it actually good for practical usage like coding in complex projects, research, etc.
Aside. I find the "cost per task" charts both useful and uncanny. Is It better a model that takes me to 90% in 1 dollar or one that takes me to 95% in 2 dollars? Or a different model that too scores 90% in 1 dollar? How much will it cost me the last 10% or 5%? At the end of the day, cost to 100% is what matters and the half (90%) backed solution may require more to reach 100% (or not, who knows?)
Is It better a model that takes me to 90% in 1 dollar or one that takes me to 95% in 2 dollars?
It's pretty important to understand if your own work domain is one where the last 5% matters. In a lot of day-to-day software engineering tasks, it doesn't, and one can get crazy mileage out of the cheaper models. OTOH, if you are performing novel research, that last 5% may be worth whatever it costs...
The 90% and 95% are against some blend of tasks meant to be broadly representative. A pricey model seldom fails a problem that cheap models do well, so there's stratification of tasks by difficulty. Someone doing novel research may be in the "hard" 15% of the blend, where P(solution) goes from one third to two thirds.
On the other hand, if it's cheap to tell whether you got a good solution, and you think the 90 and 95% apply to your task blend, then it's almost always worth trying the cheap model first.
I see that with Opus 5, it started thinking like crazy in the last few days , I don't think my workflow is that complicated, still it gets into thinking mode and stays there
What am I missing here? I think of fireworks as an inference provider serving open weights model. The value that they primarily provide to customers is that (i) they improve reliability by balancing across a bunch of clouds/neoclouds, (ii) they get better pricing by buying capacity in bulk, and (iii) they reduce operational costs. So far so good.
I can also see the argument for providing a post-training service from a customer acquisition perspective: "hey, we can fine-tune this open weights model, so it both gives better/more predictable results than OpenAI/Anthropic and also is cheaper. And btw, once we've won your business, please run this model on our infra."
But what I'm struggling to understand is fireworks spending a bunch of money (on salaries and compute) releasing a frontier model that is going to rapidly fall behind the frontier. Is this "just" advertising for them, both for customers and also for hiring? Or are they actually trying to stay on the frontier? If so, to what end?
The end: make lots of money.
The means: systematically take existing reasoning models, do some more post-training of some sort to make them achieve the same outputs with less reasoning tokens (ie, cheaper). Same quality but cheaper is always valuable.
It's unclear if they can do this systematically and it's unclear if they can do it better than others. But, lots of things are unclear in AI at the moment, this doesn't seem outrageous on the surface. And, it could just be marketing. And it could be the first option with the backup of the second.
I'd expect that their business strategy is to compete in more markets, and if successful, they can capture more value. This is the "easiest" for them as they already have GPUs, a training environment etc. For that platform it's not the worst if there's an internal customer team that can help shape the future and provide immediate feedback, and if it results in a good model, even better.
Other things I'd not be surprised they offer in the future in the same vein: A multi-model harness, coding agent (cloud and local), and maybe at a later point in time even a CPU-only cloud compute product.
Why does Cursor or Devin make their own models? If you use their models, then you can't fallback to other people's models on openrouter or anyplace else. You just stick with them.
Been thinking about the feasibility of training a model using synthetic thinking traces that were reduced to caveman-speak prior to being used for training. Seems like it would be fairly easy to generate plenty of suitably lobotomized synthetic traces with a pair of cheap-ish models. Or even just using good old fashioned NLP to aggressively remove stop words and reduce trace words to lemmas.
It’s the first time I know fireworks has a team doing model research. I do have a complex mood in that. On one hand, I’m always happy to see improvement of OSS models, whether that’s on intelligence or cost-efficiency. On the other hand, I would be a little worried about using fireworks as my API provider. Till the moment I saw this news, I had been using fireworks as my provider of deepseek v4 flash, because I thought fireworks acting as a role deploying OSS models and selling calculation resources, should be safe to use without worry of data being used for training since there’s no “conflict of interests”. But I would think twice now.
this work may explain why recent models like qwen-3.8-flash and MiMo-2.6-* have not made it into their offering, which has given me reason to pause my excitement for Fireworks
Seems irrelevant? Of course we don’t use data for training.
…trust me bro.
It’s obviously easier to believe when they’re not training models.
Eh, anyway this whole thing is just an ad:
Looking to take Ember-1 one step further, and optimize it for your use case? We are also launching training support for Ember-1, enabling enterprises to build customized, token-efficient models tailored to their needs with their own data. The future of open models is specialized models trained on your specific workload.
Probably, I guess, fancy serverless infrastructure actually makes virtually no difference to hosting really large models that people want to use, and “just” being an inference provider for open weight models turns out to have no moat.
So this is a bit of a pivot to “use our training infrastructure too…!” imo.
Pivot? Sure. Go them. Not what I signed up for though. /shrug
I think legal jurisdiction matters in discerning these things. The EU or US both have courts that, despite anyones opinion, are regarded as having robust contract enforcement. If Fireworks is domiciled in either then claiming ZDR and instead training on the data would be an enormous financial footgun. There's reasons why companies on both sides of contracts, even those with little to no US or EU activity, agree to use US or EU courts for enforcement.
Hong Kong I think used to be a popular option as well, before the handover from the UK.
Being terminally 'online' can make people cynical about everything, but have to temper things with reality a bit too.
Last I checked they still offer no training ZDR US based hosting. It is one of my three pinned providers for deepseek v4 flash along with Parasail and Deepinfra.
The more I learn about Fireworks the more unsavory they seem as a company. I don’t care what the license says, Moonshot has been openly improving, sharing research, and providing weights for the models that make up your entire bottom line, and the moment you can improve them in reciprocal it’s closed weights, “this is our own proprietary” nonsense? Where are we that China has better open source ethos than America?
Kimi K3 itself isn't FOSS. Speaking of reciprocity: Fireworks is presumably paying Moonshot serious money for the right to do what they are doing here, since Kimi's license[0] excludes commercial inference providers (such as Fireworks) from gratis use. It requires them to: "...enter into a separate agreement with Moonshot AI before using the Software or its derivative works..."
What you say is true, and Moonshot definitely has an agreement with Fireworks, but it still just feels wrong. It’s not the America I grew up in, where if you took, you gave back. I know that’s not a very coherent and practical position, but it feels true to me.
This is undoubtedly great. But most of the inference cost today for dominant use cases (agentic coding) are in the prefill, not the decode. This is one of the reasons that DeepSeek is so aggressively optimizing prefill and caching.
This is cool! But also: am I wrong for thinking “Pareto frontier” is some pretty silly/clever marketing jargon? Is this common phrasing for basically saying: test performance per spend on tokens is decent?
I don't see why? It is a well defined term that existed prior to the recent AI bubble/revolution, and from what I can see they are using it appropriately.
I don't see why I would be interested in this model, considering the price difference. They advertise that it's the same as Kimi K3 in half the tokens. But the pricing is double the pricing of K3. So why do I care if it uses fewer tokens, if I'm paying double per token?
From reading the blog post, it is essentially exactly the same as the car example. It delivers the same performance on tasks, but using 40% fewer tokens. This is the same as a car getting you to the same destination but wasting less energy on excess heat, wind resistance, or whatever else affects fuel economy (I am not an expert, obviously). I am paying for an LLM to complete tasks for me, not for the intermediate tokens.
I'm with you, I wrote prior comment under the assumption that they were priced differently (from GP claim as such), but they are priced the same (on Fireworks)
Will be taking Ember-1 for a spin on Monday and hopefully enjoy those better MPGs
Check out Neuralwatt. They are ZDR and great energy based K3 pricing. They also have a K3-fast which is basically no reasoning (in addition to regular K3).
industry standard is price-per-1M tokens, don't do something different, even Google caved and moved from their char based pricing to tokens (the fundamental unit of computation in ai)
GPUs are rented in $/h, like every other piece of hardware in cloud
K3 TOS says they must sell no lower than what Moonshot charges. If you have a provider selling for less than $15/mtok, they are violating TOS from Moonshot.
I am confused over this... I get efficiency but price seems too high to me. I didn't try the model, so it migth be beyond fast or some other quality that is not obvious.
I am no longer going to be impressed by new model releases unless they introduce an entirely new paradigm of interacting with them, that is going to make the benchmarks look like everything else isn't even 5% as capable.
I work at Fireworks and it's cool to see this was posted.
I'd be interested to hear what people found most interesting about Ember, and what kinds of follow-up research or educational material would be useful to you all?
Analysis paralysis stifles not just human intelligence, but other intelligences too.
Yes and thar makes you wonder if the Paradox of Choice would apply as well ;)
The more options you have, the harder it becomes to be satisfied with the one you picked.
The thinking traces on some Chinese models just output the full response in the thinking trace, then output it again to the user, which is redundant.
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.
I would really love if we brought back some colloquialisms in this field. Not that long ago most folks in tech would have had pretty blank looks on their faces when someone started talking about the "Pareto frontier"
They want it to be the best at something. And it's obviously not the absolute smartest. So here we are.
Pareto frontier on some benchmark that I am hearing of for the first time.
Kimi K3 with less reasoning tokens isn't exactly exciting either, and particularly so if the license is less open than original Kimi K3.
when everyones fighting to be 'somewhere in the pile' they need some way to advertise they have made progress while not being the best.
Is there a Pareto frontier for the number of times articles mention or don't mention a Pareto frontier.
Need this done for DeepSeek, ideally one of the Flash models.
If you have the compute, I have the expertise.
And GLM. Both Deepseek 4.1 Flash and GLM 5.3 Flash are quote verbose when thinking.
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).
Unfortunately, it’s hard to make a chart of that.
It looks like it would be similar to GLM 5.3 Flash, had they tested it...
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?
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
No, because close labs/models borrow but don't contribute back.
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
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.
I think people make mistake here, google’s approach is not to spend $2.3 on every $1.0 earned, they’re riding on serving to masses “luna”, they absolutely have way more powerful models internally but they don’t clutter their infrastructure with fragile and costly intelligence-of-size inference frontier. I think “underdog” perception is illusory/temporary, not stupidity - calculated, conscious, longer term bet.
I tend to agree, but I also should highlight how expensive this shit really is.
in one month, Google actually went cash-negative. [0] even still, they are subsidizing their stuff a lot less, have the most opaque and variable limits, and increase adoption through bundling and shuffling features. I can't even share my Google One storage without subscribing to a Google AI plan anymore, but previously any plan except Google One Lite was shareable.
if you tell me that's not enough to go after frontier, then how much money are Anthropic and OpenAI burning?
[0]: https://www.techspot.com/news/113214-google-records-first-ne...
Where do you get this "not to spend $2.3 on ever $1.0 earned" from?
Google might not have compelling frontier offerings, their chat harness is complete garbage compared to any other lab (in large part due to a bizarrely badly designed harness where something like code execution requires the prompt to undergo some sort of classification step, no idea what they are doing).
But they absolutely kill in terms of usage offerings. Google lets one subscription be used by *SIX* different google accounts on a family plan.
Plus I currently literally get *$40/month* of Gemini API credits on developer.google.com because they gave me a $10/month grant 4 times.
They give you 200 cloud compute units on google collab, this literally lets you spin up an H100 for around 40 hrs or something if you want to try spinning up local models.
You get Jules (huge allotment btw), Image gen, Video gen, Music Gen, antigravity usage, 5 TB of cloud storage, Notebook LLM...
Okay 5tb cloud storage is their most expensive plan. But what do you actually use video or image gen for? Or music gen? Antigravity is garbage, I guess you can do some stuff with Gemini models over API. I was paying for Google ai and then realized that between obscure limits and gimmick features I don't really need it. Canceled my subscription and didn't even notice a difference
It's an example number, which doesn't matter much but it comes from widely reported $2.30 spent on ops for every $1 of revenue in spring of 2025 for Anthropic.
The difference between contributing to OS and AI, is that the first is a hobby alternative to woodworking or hiking, while the other can easily bootstrap you a company you can get millions in investment, at least for time being.
Won't the "frontier" labs figure out whatever techniques were used and apply them to their closed models?
If they can keep up.
The lock-in is less pronounced as it is with AWS or MS.
Like how the last 2 decades of tech companies are thinly veiled open source pilfering into business units.
"Oh darn, you know that thing I made and released with explicit, precise language defining who can use it and what, if any, restrictions apply? Well now someone is using it in complete accordance with those conditions I set out, and that's somehow making me upset"
This was exactly the crux of Nathan Lambert's recent testimony to a group of US Congressional members/staff: https://www.interconnects.ai/p/the-current-balance-of-power-...
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.
https://www.reddit.com/r/LocalLLaMA/comments/1wj3s31/thank_y...
Does anybody know if this would be a good model for creative writing?
Choose one.
Are you a bot?
You really have no sense of irony, do you?
So they trained a model on open weights, and then aren't releasing the weights... am I reading this right?
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.
weight available
It happens. Most open licenses aren't GPL style copyleft.
It happens with open source software all the time, why would we expect any different with open source weights.
Because we do. The GPL isn't a suggestion. If you can take open source code and make private software out of it then what are we all doing? No, license requirements and agreement are law for a reason.
Kimi K3 has its own license which is permissive, it isn't at all like GPL https://github.com/MoonshotAI/Kimi-K3/blob/main/LICENSE
GPL is a specific license, it’s not FLOSS as a whole
Because the licenses that apply to software make no sense in the context of LLMs. With the latter, there is no source code to license.
The words of a license are what the license is.
Aren't Cursor Composer models like this too? At some point all the extra RL you do can be considered as proprietary information added.
Not suggesting this is right or wrong, but is sort of the nature of the technology.
There is little to no point reading the article as well. It's stripped of all alpha.
These are deliberately the least informative phrases you could possibly use to describe what you have done, while still being in the realm of words that go over a generic investor who has no idea whats going on and may be dazzled by sciencey sounding language.
Cursor compose 2.5 article where they used and described on policy self distilation was actual alpha.
This is precisely my point.
Which is fine, that’s legal according to the license
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.
Agreed, I think the only place where it’s still interesting is ui design. Visually kimi and muse feel much nicer than frontier models to me, but maybe it’s an artifact of everything terrible being Claude Design
GLM 5.3 is great too. Thanks for suggesting Muse.
Competition is good. Without K3/GLM/DS4 etc. there would be no pressure on OpenAI to drop Sol's price.
Exactly, competition between both frontier model companies and the chinese labs is the primary factor suppressing consumer prices.
I was surprised by that. I run my benchmark [1] every couple of days and was sure this model will be ath the pareto frontier, if not THE pareto frontier. But no:
Ember isn't picked yet. In planning, Opus 5.5 wins under the planning weights. In code, GPT-6 Sol dominates it: also 10/10, but with a higher quality score and a lower estimated cost. Ember has no intelligence index, so its starting score is only 0.73, which holds its 10/10 down to 0.954 against Sol's 0.975.
[1] https://philippdubach.com/posts/jev-model-router-for-pi/
6 or 5.6? Because 6 is hot garbage
Sol pricing dropped but so did the quality few days ago. I wonder when these companies are sued for making the terms from their side to go downwards while taking the same subscription cost.
Is anybody tracking these quality changes? All I've seen so far are accusations (quite a few at this point) but not really any actual data.
In a Codex subreddit there is a bunch of stats.
I don’t understand how there isn’t a website out there tracking this stuff already.
how the hell do we even track that? and before someone says...
—"Benchmarks!"
...I'll tell that they can be gamed so easily, and they are on a consistent basis.
Sure they are, but do you think they are continuing training to improve a model after release without bumping the version number, presumably only to game the benchmarks?
If you're willing to cheat, isn't it just a matter of grepping for the benchmark's question and pasting the solution in the chain of thought?
https://modelregression.com/
Haven't looked into how accurate the page is, but the list of regressions on the bottom looks terrifying, at first glance?
Yes, it looks like regressions are frequent, but sometimes performance goes back to baseline quite fast.
They're impossible to objectively track by design and intent. Intelligence is such a nebulous target that one can find any number of metrics to support any premise: that the model is smarter, and that the model is dumber. We use benchmarks to attempt to standardise comparisons but these are quickly ingested into the training data and then become effectively useless. You might have heard the term "bench-maxed." Meaning that a benchmark has an effective lifespan in months.
They've been using the Pelican test in the /r/Codex subreddit with some success. One major finding is that OpenAI has been silently degrading the model while charging Astra prices. Another finding is that even when the model has not been silently degraded, pelican quality is significantly lower. Sometimes comically so. The general consensus right now is that the new GPT-6 Sol model is an updated Terra model. Many intelligence metrics are roughly similar. Meaning the most recent model updates were an attempt to rebalance compute rather than improve intelligence.
Ultimately I've never seen users as upset about GPT-6 Sol/Luna than I have right now. Even Astra has been noticeably degraded for me and everyone else I have asked. This is compounded by the fact that Opus 5.5 is a generational improvement at an affordable price. There is currently no competition.
it's been known since the early days of GPT-4 that they alter the models whenever they feel like it.
https://arxiv.org/pdf/2307.09009
the accusations are quite a few because people notice.
I am glad I am not the only one to notice. I feel like I've gone back to Sonnet 4 levels of incompetence!
With Sol 6 I am back in a world where the model writes bad code because it is lazy ("You're absolutely right, I did not [do it properly] because I did not want to edit [a normal amount of files]").
100%, I wish for a legislation which would require the providers to give you at least a unique hash identifying the model (and infra running it, if it affects output) - such that the same hash must give the same output given the same seed. Right now it's all just vibes
With DeepSeek's pricing, no other value prop has been great.
Mimo has entered the conversation.
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.
I can’t help but think it’s more expensive tinker.
Do they mean Opus 5.5 or Opus 5?
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
What are the useful applications of Jev so far? Not to sound dismissive, I just haven’t seen what people are using it for yet.
Lots of use cases! I've personally used it for the following:
1. Evals (once you have your rubric defined and tuned using a reasoning model, jev can be great for running periodic evals especially those that run daily.
2. e-commerce catalog classification 3. quick search using anything as context and query mapping to a pre-defined set.
At least for 1, evils, you’d want to use a good old reasoning model to get the best eval results.
Here's a third-party (not Jev) showcase of things people built, which helped me kind of get the appeal. https://bentossell.com/jev/ (not mine).
LLMs can be too creative and often too verbose. Sometimes there is a right answer and a way to get there with the understanding of language, but despite using structured outputs, the model insists on inventing variations not in the schema or coming up with something completely different. A model like Jev that can not do those things, and can give the same output every time with given the same input, and be able to measure probabilities has many use cases.
Why not using a cheap LLM with thinking completely disabled ? I don't think it will be much more expensive than jev.
I’ve tested this with some local LLMs and their accuracy is in general better than Jev/Laya, but they are super slow in comparison as well
For example, a typical/stock LLM can’t really play Doom in real time, but a Jev-like model can. Just because of latency
Of course, if you want the best Doom player, there are way better and faster adhoc models
LLM inference has two very different regimes of work: prefill & decode. You can think of the former roughly as processing a pre-specified prompt, and the latter as sequential processing (auto-regressive token generation) eg. "chain of thought". The latter is very important for LLMs and cannot be ignored; it deeply influences infra design, even necessitates copious amounts of high-bandwidth memory. Jev-like models can ignore the latter and therefore optimize much better for the former, consequently operating at both better cost and latency.
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
Obviously this research was done before 6.0 Sol and Opus 5.5 came out. Your point stands that the frontier moves quickly and small gains can be eclipsed quickly.
This is the golden age of model training. Some days ago, I decided I wanted a local CPU only model that can perform exceptionally well for English to Bash translation (to avoid the googling for command syntax). I got a bunch of subagents to generate large amount of training data (140k+ samples), got the Qwen 3 0.6B base model, pointed Astra at it, and off to the races. It trained for 2 days (on and off) and I got a surprisingly good model for my task! The total active time I spent was a few hours. And it is still improving, what a time to be alive!
what hardware are you using to train?
I didn't have a local GPU, so I asked it to go out and find hardware. It found a google TPU v6e which seemed reasonably priced. I gave it my google api key. I told it to use TPU only when training and bring it down afterwards. That's about it.
Neat!
What kind of observability did you have over this process? I’m interested in how my peers are operating these efforts.
On the cloud side, nothing valuable existed, so the training couldn't ruin anything it didn't create. On the laptop side, I usually ask the agents to create named scripts for everything it needs to access, then those local script directory is green-lit with approve all. For cost, I kept giving it new budget in the 20-30 dollar increments.
I had to intervene a few times. For instance, as smart as the models are said to be (Astra), it would copy the full training run, train on the server, pull every checkpoint to the local machine, then run tests, update. So, the bandwidth bill was as high as training bill for the first 6 hours. It could have simply tested each checkpoint on the server, saved time and money, didn't occur to it until I said.
Perhaps I wasn’t clear. What kind of instrumentation and alerting, if any, did you employ to keep an eye on it?
instrumentation: scripts to watch the runs, measure, report. alerting: none, the model was access limited and constrained by other means.
I wouldn't put my house on it. Brave.
This is the part where the narrator looks at the camera and says "Don't try this at home, kids!"
Why? Isnt the API key scoped to a project and specifically made for this?
Are you confusing this with an OAuth token or something?
Until astra goes bonkers and use the tpu for days
this is what billing caps are for
https://docs.cloud.google.com/billing/docs/how-to/budgets-sp...
This is kind of what it was supposed to do, in this case
There’s a safer way to do this with nearly no added friction. Give it a read only API key. Then just ask it to write the API calls into a bash script and then read it and run it yourself. The agent can still inspect the live resources and diagnose and give you more commands to run. I do agree I wouldn’t give it create / write access.
You’re absolutely right, I shouldn’t have rented a 200 GPU cluster for $35,000/hour. That’s on me.
[Search: Can I refund Google cloud?]
It looks like we’re not able to ask for a refund since we did actually use all of that compute intentionally.
Would you like me to write you a pleading email to send to the support team?
I've done this sort of thing before but with Vast. Pre-deposited some money online, then let the LLM request and manage a training run on an allocation. Worked pretty well without risking bankruptcy.
i also need more info!
I am thinking about opensourcing everything, although this is not my main domain or my main startup, so the overhead of huggingface etc seems a bit unnecessary
Edit: will do as soon as possible
just ask the agent to write it up if you don't have time to do a write-up yourself
Just use the post-one-off-project-to-huggingface-skill.md
+1, would like to see. Even if it's not fully "ready for consumption", it's probably enough to reproduce the results.
Please do! Small, specialized models need more love and the time you spent would be a gift!
Would also love to read a write-up about this!
Please do share it.
Curious about how you generated the training data? Was it just asking an existing model to generate a bunch of examples?
I ask cause would this be a kind of model distillation?
I have a small model I'm looking to train on some data, and I have some real live data but I'd love to be able to extend it.
All synthetic data. For this usecase, it was easier because all current generation LLMs, even the small models, are really good at bash commands (and SQL queries too)), so you can reasonably start batches of cheap subagents whose output is reviewed by a more capable model and merge into main training set. After 100k, I had to standing instructions to run the generation loops selectively, meaning only update samples in a given area where we see poor capability.
Do you have a write-up or git repo for this? Would love to learn more and/or dig into the guts
edit: others have asked any you have replied "soon (tm)", looking forward for that day
It would be awesome to share your training set on hugging face if it’s easy to de-personalize it. The largest I could find was only 800 rows.
I’ve previously fine tuned (SFT, currently trying to distill) models using this https://huggingface.co/datasets/westenfelder/NL2SH-ALFA
It is a form of distillation, as long as you're working a very narrow "trivial" topics it works perfectly.
That's a really impressive result. There are all kinds of small tasks like this I use an LLM for, but theoretically if you broke all the sub-use cases into local-only models, and had something lightweight that routed to the right model, you could have faster and cheaper workflows. E.g. something trained on the linux man pages for common commands, since it's usually quicker to ask an LLM for a specific command with flags than to consult the man pages.
we dont know what the result is and how its impressive.
I don't understand. If you have a model that can do bash examples already (your subagents), then why would you need to train a model?
Or are the subagents generating your training data using a closed/paid model?
The models he is using to generate training data are presumably commercial models. He is distilling their bash knowledge into a much smaller model he can run locally fast and cheap.
A very small, highly specialized model can use negligible resources (CPU, energy) to accomplish the same task.
For everyday work that happens frequently it's better to have a tiny specialized model instead of making billable API calls or turning your laptop into an 80W space heater for 20 seconds to run a general purpose model.
The large models can be used to generate synthetic training data. Tell them to make up 100,000 tasks paired with the resulting output as a 1-time cost. Then use that to train a small model.
Think of it as distillation, but focused on a specific task.
Given that they're just using it to avoid the googling for bash command syntax, I'm not sure they'll save in the end against the 140k training examples they generated.
you can probably generate quite a few example pairs in a single shot, you also likely don't need the best models for this either
Same token burn / cost though right?
I think OP's point remains, if you generate 140k pairs, your local model would need to run that many to offset having just used the generator (SOTA or not) model to begin with.
I wonder if another approach if latency is a concern is just to do a two shot pass with Jev (perhaps given small context you'd want one to match command, then one to match args of given command) would be an extremely fast, and cheap way to do it - rather than training your own.
Good observation! It would have to be offset with O(140k) queries to the model, which is, well, unlikely.
If it's about the latency / flow disruption, spending a few hours once could easily be worth it if the result is actually good enough to skip googling/retries.
Just like with OSS in general, being able to distribute it is what makes the effort worthwhile.
This particular example is maybe a niche, but 1400 people can use a few hundred queries in a reasonable amount of time.
This example is not that niche. Lots of people use human to bash. I'd probably use a small pre trained model if it was easy to use. I use a little script right now that calls a cheap model. Actually.. $5 would probably will last me over a year so the only real benefit would be if I didn't have an internet connection.
You can buy a $10 subscription for a month to generate the training data, then cancel your subscription. The trained model is yours to use (and share with others) forever.
Yea specialized models could also be resold in a shareware style, like even if it cost 50$ to produce you'd just have to sell 10 copies of it to people for 5$.
Which 5$ is a pretty easy sell if its useful in any way, It's pretty easy to justify a purchase if its yours forever and doesn't use much CPU so is easy to run I mean people were spending 1000$+ on mac mini setups to run local llms or run remote agents.
This is so cool - I'm aware of this in a vague way. Can you write a little tutorial or give some good links. I want this to be the next new things I do :)
Better yet package it up in a skill!
I feel like we need a good index for these kinds of specialized models, especially if you plan to open them up. The downside is a new bash version means potentially new training.
Or you could just google the syntax to accomplish the same task faster and with fewer resources.
If it’s one of thing that you want just for English to bash shell commands, I will create AST, it is deterministic, exceptionally fast, no tokens so no need to fine tune existing model, please let me know your thoughts.
You can go quite far using a human language to Bash grammar based setup but at some point the input prompts are harder to translate. The OP has existing projects that work with AST quite deeply so I assume they know about that already.
I am building a natural language to CSV/Excel commands for a "wrangler" type desktop app. Same issues. The MVP is being built with parsers of sorts, entirely code generated. Then I want to fine-tune a tiny model at some point.
https://github.com/brainless/baho
Golden age before the age that ends humanity. Not talking about any "rogue AI", just the known statistical models of what is coming due to climate change.
Do those statistical models account for declining birth rates or are they based on prior population growth projections?
It's good to hear you're enjoying yourself, but I suggest retiring that expression. It's really beginning to grate.
It’s not so good to hear your pessimism, but I suggest retiring spreading it online. It’s really beginning to grate.
Ehh, I’m more annoyed by people starting comments with “ehh”
Sorry for the aside, but I noticed half the usecase of AI is fixing the awful DX.
I appreciate the aside. Interesting observation
I'm literally working on context/harness engineering right now (a set of opencode plugins)
Aside on the aside, I welcome this new era of really personal software. Not Ai's being sycophants, rather being able to easily and quickly change, adapt, or extend software I am not familiar with.
Seriously, I'm using a Qwen 3.8 27B on the homelab, distilled from supposed Fable traces. Regardless, the difference is notable, less thinking, better output. Distilled / heavy quant is better than the original (imv)
https://huggingface.co/vwdubb/Qwen3.8-27B-Fable-Distill-NVFP...
side quest, are fable distillations only wrong when it's another country?
Did your Astra do any RL or just SFT? did it make up any benchmark to ensure the fine-tuning was a success?
See, you should now share it, so others can benefit without everyone having to do the same re-training :)
Here's a similar project for those who want to replicate: https://github.com/ThorOdinson246/whatisit-nl2sh
Not my project
Wow that's awesome
I have been trying a mix of fine-tuning and I am amazed that most people do not see this coming.
A tiny, smaller than 1b parameter model, fine-tuned, can kick ass for constrained work. I do not have a lot of budget, I fine-tune only on a 16GB M4 Mac Mini. But that also tells me the potential is wild. Progress has been slow since I moonlight on this.
I have been trying to build a set of models + agents for full-stack development, where each model does only a small piece, like take user prompt and break into backend/frontend tasks. Then a Rust+Diesel model, a Rust+Auxum model, a Solid+Router model and so on. I know this is wild but this is just theory - can 5 or 6 Qwen 3.5 0.8b models do full-stack web development? My hunch says they can, better than what most people expect. Heck, with a good harness, it might beat all the cheaper models for the specific task, like Haiku or Luna.
remember to test against benchmarks. I would love to hear about your progress.
I think a sub-set of people see this coming, I also think it isn’t just fine tuning open weight LLM models. A few people I know who are thinking along the same lines with architectures like BERT etc.
That being said it’s much easier at the moment to continue to use the frontier providers for most general tasks, that is the argument I’ve heard.
For creating these types of fine tuned local models, on constrained hardware for inference, I do think this is the way to go for specific tasks too!
This is a really good story. I love it. Really hope you can share your experience, either blog post or GitHub repo
I use this solution for your exact use case:
I have a single command that fires up llama.cpp on cpu only using gemma4 e2b, answers a single question from the command line and exits. This takes about 3 seconds to load from an SSD, and is smart enough to solve exactly these "remind me of the syntax" scenarios if you dont wanna switch to a browser.
Over on /r/LocalLLaMA there's a group that's been getting popular doing the same thing for the Qwen 27B (and other) models. - https://huggingface.co/ukisai
Does anyone have a real-world usage feedback on this model? Because the benchmarks look, I would say, too good: it scores even higher than Qwen 3.8 27B in some benchmarks - I am not even sure how is it possible. So the question is if this is benchmaxxing or is it actually good for practical usage like coding in complex projects, research, etc.
Aside. I find the "cost per task" charts both useful and uncanny. Is It better a model that takes me to 90% in 1 dollar or one that takes me to 95% in 2 dollars? Or a different model that too scores 90% in 1 dollar? How much will it cost me the last 10% or 5%? At the end of the day, cost to 100% is what matters and the half (90%) backed solution may require more to reach 100% (or not, who knows?)
It's pretty important to understand if your own work domain is one where the last 5% matters. In a lot of day-to-day software engineering tasks, it doesn't, and one can get crazy mileage out of the cheaper models. OTOH, if you are performing novel research, that last 5% may be worth whatever it costs...
The 90% and 95% are against some blend of tasks meant to be broadly representative. A pricey model seldom fails a problem that cheap models do well, so there's stratification of tasks by difficulty. Someone doing novel research may be in the "hard" 15% of the blend, where P(solution) goes from one third to two thirds.
On the other hand, if it's cheap to tell whether you got a good solution, and you think the 90 and 95% apply to your task blend, then it's almost always worth trying the cheap model first.
I see that with Opus 5, it started thinking like crazy in the last few days , I don't think my workflow is that complicated, still it gets into thinking mode and stays there
Would be nice to include in fire pass.
What am I missing here? I think of fireworks as an inference provider serving open weights model. The value that they primarily provide to customers is that (i) they improve reliability by balancing across a bunch of clouds/neoclouds, (ii) they get better pricing by buying capacity in bulk, and (iii) they reduce operational costs. So far so good.
I can also see the argument for providing a post-training service from a customer acquisition perspective: "hey, we can fine-tune this open weights model, so it both gives better/more predictable results than OpenAI/Anthropic and also is cheaper. And btw, once we've won your business, please run this model on our infra."
But what I'm struggling to understand is fireworks spending a bunch of money (on salaries and compute) releasing a frontier model that is going to rapidly fall behind the frontier. Is this "just" advertising for them, both for customers and also for hiring? Or are they actually trying to stay on the frontier? If so, to what end?
I think they are trying to show potential customers what is possible.
The end: make lots of money. The means: systematically take existing reasoning models, do some more post-training of some sort to make them achieve the same outputs with less reasoning tokens (ie, cheaper). Same quality but cheaper is always valuable.
It's unclear if they can do this systematically and it's unclear if they can do it better than others. But, lots of things are unclear in AI at the moment, this doesn't seem outrageous on the surface. And, it could just be marketing. And it could be the first option with the backup of the second.
I'd expect that their business strategy is to compete in more markets, and if successful, they can capture more value. This is the "easiest" for them as they already have GPUs, a training environment etc. For that platform it's not the worst if there's an internal customer team that can help shape the future and provide immediate feedback, and if it results in a good model, even better.
Other things I'd not be surprised they offer in the future in the same vein: A multi-model harness, coding agent (cloud and local), and maybe at a later point in time even a CPU-only cloud compute product.
Vertical integration.
Why does Cursor or Devin make their own models? If you use their models, then you can't fallback to other people's models on openrouter or anyplace else. You just stick with them.
Been thinking about the feasibility of training a model using synthetic thinking traces that were reduced to caveman-speak prior to being used for training. Seems like it would be fairly easy to generate plenty of suitably lobotomized synthetic traces with a pair of cheap-ish models. Or even just using good old fashioned NLP to aggressively remove stop words and reduce trace words to lemmas.
It’s the first time I know fireworks has a team doing model research. I do have a complex mood in that. On one hand, I’m always happy to see improvement of OSS models, whether that’s on intelligence or cost-efficiency. On the other hand, I would be a little worried about using fireworks as my API provider. Till the moment I saw this news, I had been using fireworks as my provider of deepseek v4 flash, because I thought fireworks acting as a role deploying OSS models and selling calculation resources, should be safe to use without worry of data being used for training since there’s no “conflict of interests”. But I would think twice now.
That also could explain why openrouter is worth that much
Just read the terms of service and read this blog post and I think your concern will be addressed.
https://trust.fireworks.ai/
this is our preferred open weight token vendor
this work may explain why recent models like qwen-3.8-flash and MiMo-2.6-* have not made it into their offering, which has given me reason to pause my excitement for Fireworks
Seems irrelevant? Of course we don’t use data for training.
…trust me bro.
It’s obviously easier to believe when they’re not training models.
Eh, anyway this whole thing is just an ad:
Probably, I guess, fancy serverless infrastructure actually makes virtually no difference to hosting really large models that people want to use, and “just” being an inference provider for open weight models turns out to have no moat.
So this is a bit of a pivot to “use our training infrastructure too…!” imo.
Pivot? Sure. Go them. Not what I signed up for though. /shrug
I think legal jurisdiction matters in discerning these things. The EU or US both have courts that, despite anyones opinion, are regarded as having robust contract enforcement. If Fireworks is domiciled in either then claiming ZDR and instead training on the data would be an enormous financial footgun. There's reasons why companies on both sides of contracts, even those with little to no US or EU activity, agree to use US or EU courts for enforcement.
Hong Kong I think used to be a popular option as well, before the handover from the UK.
Being terminally 'online' can make people cynical about everything, but have to temper things with reality a bit too.
Last I checked they still offer no training ZDR US based hosting. It is one of my three pinned providers for deepseek v4 flash along with Parasail and Deepinfra.
The more I learn about Fireworks the more unsavory they seem as a company. I don’t care what the license says, Moonshot has been openly improving, sharing research, and providing weights for the models that make up your entire bottom line, and the moment you can improve them in reciprocal it’s closed weights, “this is our own proprietary” nonsense? Where are we that China has better open source ethos than America?
Why is proprietary-licensed software unethical?
Kimi K3 itself isn't FOSS. Speaking of reciprocity: Fireworks is presumably paying Moonshot serious money for the right to do what they are doing here, since Kimi's license[0] excludes commercial inference providers (such as Fireworks) from gratis use. It requires them to: "...enter into a separate agreement with Moonshot AI before using the Software or its derivative works..."
[0] https://huggingface.co/moonshotai/Kimi-K3/blob/main/LICENSE#...
presumably...
What you say is true, and Moonshot definitely has an agreement with Fireworks, but it still just feels wrong. It’s not the America I grew up in, where if you took, you gave back. I know that’s not a very coherent and practical position, but it feels true to me.
They are giving back - money to Moonshot, most likely. Why should they give "back" to you? Did you make Kimi K3, so that "back" makes sense?
Given what an experience i had with Ember-2… I’m not sure I’d want to engage with its predecessor.
https://en.wikipedia.org/wiki/Exapunks?wprov=sfti1
This is undoubtedly great. But most of the inference cost today for dominant use cases (agentic coding) are in the prefill, not the decode. This is one of the reasons that DeepSeek is so aggressively optimizing prefill and caching.
This is cool! But also: am I wrong for thinking “Pareto frontier” is some pretty silly/clever marketing jargon? Is this common phrasing for basically saying: test performance per spend on tokens is decent?
I don't see why? It is a well defined term that existed prior to the recent AI bubble/revolution, and from what I can see they are using it appropriately.
Fair enough!
I don't see why I would be interested in this model, considering the price difference. They advertise that it's the same as Kimi K3 in half the tokens. But the pricing is double the pricing of K3. So why do I care if it uses fewer tokens, if I'm paying double per token?
Because you care about how many tokens are used per task. What you said is like only caring about the price of gas and not gas mileage of your car.
it's not exactly the same, the model stills "weighs" the same
here, it does less work, it's more like driving half as far but still paying the same total cost
this being said, K3 and E1 models are priced the same at $3.00 / $0.30 / $15.00
https://fireworks.ai/models/fireworks/kimi-k3
https://fireworks.ai/models/fireworks/ember-1
From reading the blog post, it is essentially exactly the same as the car example. It delivers the same performance on tasks, but using 40% fewer tokens. This is the same as a car getting you to the same destination but wasting less energy on excess heat, wind resistance, or whatever else affects fuel economy (I am not an expert, obviously). I am paying for an LLM to complete tasks for me, not for the intermediate tokens.
I'm with you, I wrote prior comment under the assumption that they were priced differently (from GP claim as such), but they are priced the same (on Fireworks)
Will be taking Ember-1 for a spin on Monday and hopefully enjoy those better MPGs
Why are you saying it drives half as far when both models complete the same task but one uses half the tokens?
Wouldn't the op be more correct with their gas/distance comparison?
Because both cars get to the same end destination (complete the same task).
Unless your end goal is to see the token numbers go up, but I'm not sure why that would be of interest.
It’s same price for lower latency.
the pricing is the same, where are you seeing double?
K3 is cheaper from other providers. $1/$9, though I can't speak to how good those providers are.
https://openrouter.ai/moonshotai/kimi-k3
as the saying goes, you get what you pay for
we require ZDR and Fireworks provides that on contract, so for us they are the same price
Check out Neuralwatt. They are ZDR and great energy based K3 pricing. They also have a K3-fast which is basically no reasoning (in addition to regular K3).
that is nothing like how we consume Ai
industry standard is price-per-1M tokens, don't do something different, even Google caved and moved from their char based pricing to tokens (the fundamental unit of computation in ai)
GPUs are rented in $/h, like every other piece of hardware in cloud
Why are you assuming the gpus are rented?
Anyway, they still have token based pricing if you prefer. The energy pricing is often cheaper though
K3 TOS says they must sell no lower than what Moonshot charges. If you have a provider selling for less than $15/mtok, they are violating TOS from Moonshot.
looks like the underlying vendor is charging correct prices https://inference.net/models/kimi-k3/
however the listing on open router has a `/fp4` suffix, so perhaps this is an unlisted, quanted model for a lower price?
Half the tokens presumably means tasks get done twice as fast.
Agentic work costs ~input^2. For a single message, they cost the same. For long conversations, you end up paying much less.
I mean, Gpt-6 Luna seems a lot better in every aspect:
https://aibenchy.com/compare/fireworks-ember-1-high/openai-g...
Could be a really interesting article but they disabled reader mode so I guess I’ll never know.
I am confused over this... I get efficiency but price seems too high to me. I didn't try the model, so it migth be beyond fast or some other quality that is not obvious.
Anyone knows more or used this model?
I am no longer going to be impressed by new model releases unless they introduce an entirely new paradigm of interacting with them, that is going to make the benchmarks look like everything else isn't even 5% as capable.
Is this a useful model or just an ad for Fireworks runtime? I can’t really tell…
I work at Fireworks and it's cool to see this was posted.
I'd be interested to hear what people found most interesting about Ember, and what kinds of follow-up research or educational material would be useful to you all?
What made you choose/advertise using Doximity's benchmark? I use to work there and it was interesting seeing it pop up.
https://lifearchitect.ai/models-table/
So are they going to release this tuned model? Or keep it for themselves?
Cut my teeth on Ember back in the 1.x days. The learning curve was steep, but the productivity once you got it was immense.
Still remember diving into Ember back around 2015. Always appreciated its strong conventions, made for really maintainable apps in the long run.
I don't know, I have a weird feeling that Ember-1 is a distil/combination of Qwen + Gemini 3 Flash.
Looking at the hamsters drawings in this comparison, never saw any other model make such a similar version:
https://aibenchy.com/compare/fireworks-ember-1-low/google-ge...
that's really interesting. would people be interesting in reviewing it at https://unbenchmark.com?