There's orders of magnitude of low hanging juice to squeeze out of smaller models.
It is almost guaranteed that a 60-90B model can outperform current SOTA in coding tasks within 2-3 years (design not certain, probably unlikely).
It is far less clear that a 1.2T model will be meaningfully better enough to justify training it.
As far as reasoning is concerned, with the recent GRAM release, there may be 4 orders of magnitude of reasoning to tack on to smaller models.
Think about that... Google, OpenAI, Anthropic could train a 30B GRAM-based model in days - and it could potentially have better local reasoning than the best model available today at >1T params... They could upgrade that to a ~600B MoE model in days to have general trivia knowledge rivaling the best models...
You just can't train a 1T+ parameter model that fast. It is a giant if how much GRAM turns out to improve things, but it's unlikely to be trivial or nothing.
Larger models can already sort of tell you anything. They're never going to get everything right unless they stop being LLMs.
There's just not a lot of juice left to squeeze for Gemini to tell you exactly how tall Ke$ha is or when the last time Brittney Spears went to jail was...
(G)enerative (R)ecursive re(A)soning (M)odels. They really wanted the acronym.
As a fellow reader-in-waiting, I applaud that. GMTA :)
"Analysis" was right thereIf he gets that style to be more efficient (they're already competitive) it'll completely kill off LLMs
I agree but with their urgent IPO-driven need to keep increasing prices, the frontier vendors now have every incentive maintain the perception that frontier performance requires endless >$200K racks of unobtanium GPUs and RAM. While they'd love to reduce their actual costs, they'd only want to do it to the extent they are certain they can keep it secret. Otherwise, they can't maintain and keep increasing their prices. And post-IPO audited reporting makes keeping that secret even harder.
Game theory-wise they probably don't want their their armies of leading researchers optimizing frontier performance, at least in any way that would further accelerate the relative price/perf of smaller models or self/cloud-hosting. While they know the open source models will always improve, the still win as long as enough customers demand the latest frontier and the open source lag remains constant.
They profit most in a world where a few frontier labs stay far in front, drag-racing each other and expending vast capital. It keeps their customers reliant and paying top dollar while keeping low-cost alternatives farther back. They probably much prefer competing with a couple other frontier labs who have similar astronomical costs and biz models, than a world where self or cloud-hosted open-source models start closing the gap enough to start commoditizing their business.
So you are saying that frontier AI labs are spending billions of dollars on datacenters as a form of marketing. And they are colluding to hide the fact that they don't need to.
Of course they profit more if they are in front, but bleeding money to pretend to be in front is not a winning strategy. They can't fool the market if their models are not actually better, and they know this.
Given that neither company releases parameter counts, that sort of information would be slow coming out anyway. The most important thing is improvements in actual performance/ benchmark numbers, which allow them to preserve their price points as much as possible.
I don't disagree, but how much of this ends up being distillation? I can't help but imagine that 4.8 was probably trained in part by leveraging Mythos.
If the very large models turn out to be very expensive to run relative to the benefits, it's possible that they could end up still being trained, but ultimately used as a tool to create smaller models that are nearly as effective.
I'm curious if someone here with a stronger background in the space has a similar intuition or not.
There is a real trend of smaller models becoming more "capability-dense" - i.e. the best 8Bs of today beat the best 32Bs of 2 years ago. This is in part a product of distillation being used to train the smaller models.
But people consistently underestimate how "capability hungry" the world is. There are diminishing returns on model capabilities in narrow "summarize the search results" sorts of applications - but as capabilities improve, LLMs enter, get their footing in and begin to dominate new niches. At times, expensive, highly desirable niches.
I do not expect anyone at the frontier to pop up and say "no reason to train a new model" within the following decade. There will always be a demand for an LLM that's 5-10% more capable and more reliable at some highly advanced task, and generational upgrades will keep delivering those 5-10%. From increased scale and improved training both.
The latter is much better (since you can clean up, review, update responses and filter your datasets).
I suspect nobody is doing real student teacher distillation, it’s just easier to do a bunch of training on the same giant corpus then post train on the synthetic corpus with its reasoning traces etc. (which might have been generated by a bigger better LLM)
A lot, so you can bet tens of millions are flowing to congress to have distillation declared illegal before this happens. And then it'll happen anyway.
You don't need distillation. They already have the training sets.
It's MLA + MoE + Medusa (a better version of Speculative Decoding) + 1.58b (possibly - maybe nothing) + GRAM (which will almost certainly not turn out to be a nothing burger, but no one has quickly turned this around yet to prove it).
- this gets reinvented/rediscovered constantly under different names
- it cant be trained very well (right now, will change)
- massive theoretical improvements over current models (log_2(vocabsize)=17, residual stream dim is thousands of dimensions, recursivity means more information bandwidth by ~3 OoM)
- BUT it cant be interpreted or aligned <- this is why no one uses it and no one talks about it. the idea is 100% obvious to all the frontier labs and there is a good reason why it isn't used
I follow this stuff closely, I think I know what I'm talking about (edited for formating)
What are the different names? I haven't seen this before.
> - it cant be trained very well (right now, will change)
If you're sure it will change, then why are you certain that it hasn't yet, and if it's proven a 5000x boost in reasoning... why aren't they exploring this path more aggressively?
> the idea is 100% obvious to all the frontier labs and there is a good reason why it isn't used
Surely someone is willing to take a 5000x boost in reasoning on a small research model... None of them have even tried anything resembling this AFAIK. It does not seem like something 100% obvious to them.
With that said, they are now hitting the walls of energy costs and memory shortages. You brain uses 20W -- don't take it as an insult. There are orders of magnitude to gain from producing energy-efficient models (or model runners).
So I am expecting same performance at lower costs for the coming years.
Most software engineers will just need cheap tokens.
But things like physics and drug discovery have no foreseeable upper bound.
We pay CEOs an enormous amount because a small improvement in performance of an org because of them can make a massive difference in organizational value.
Throwing more intelligence at a problem doesn’t necessarily pan out financially otherwise we wouldn’t have single underemployed biology PhD.
There's still several orders of magnitude of improvement that are almost certainly left - it's just not clear how much is left on the frontier end.
Most people will be very glad to pay Anthropic, OpenAI, Google etc $200 a month to get things done 20x faster than they could IF they had a $8000 MacBook and could theoretically do it locally.
Some people would pay $200 a month forever not to have to open the terminal one time...
Furthermore, if looking at the results takes 10 minutes, that same 1 hour task only sees a 3x improvement. And so on.
No most people will not pay $200 for an LLM subscription. Some software developers do. Also, at $200/month, you are much better getting the macbook machine assuming token output speed is the same or at least reasonable.
LLMs are not very productive for your average person now for them to drop $200 on. They'll need to be more capable and integrated and even so...
An AI bubble is pretty much guaranteed at this point but that doesn't mean there's going to be a new AI winter.
If you subscribe to things like "there are tasks LLMs are innately bad at due to insufficient depth and lack of recurrent capability", then GRAM might be another signal towards that.
But keep in mind: even ARC-AGIs have their frontiers dominated by LLMs. Even if "innately bad" is true, it clearly doesn't go all the way to "innately incapable".
Even as humans there's so much knowledge out there that exists but it's very hard to surface unless you know exactly what you're looking for beforehand.
What insight do you have to make this claim?
I've repeatedly given local models non-trivial projects that involve research and coding which they've successfully completed with minimal intervention from me (almost exclusively in the domain of reviewing the results). Again, nothing comparable with current SotA, but definitely tasks I could not have given SotA models last year (without agent harness).
Now that pure progress from these models seems to have slowed down, we're seeing a ton of options for both making models more efficient and other tools that help improve them (everything from agent harnesses to RLVR).
That's just looking at "what can small do today", when you look at what's possible with larger open models that are still much smaller than SotA from the major providers, their performance is extremely close to SotA, enough that for personal projects I'll just use Kimi instead of any anthropic offerings.
So it's not terribly hard to image a solution in the middle happening within a few years. We still have tons to learn about optimal sizes of these models and how to build them with maximal efficiency (and we've already seen a lot of recent improvements in this space).
A smaller model with better context today can outperform a model with 100x more parameters with bad or diluted context.
2. MoE (already abundant) + MLA (mostly memory efficiency, not quality) + Medusa (speed, not quality) + GRAM (5000-10,000x better reasoning in an extremely small model) + 1.58b (unclear if it will have the impact Microsoft first claimed - but possibly 5x).
The benchmarks need to change. The current coding benchmarks don't capture the realities of software engineering.
I had a bunch of images that got masked by some logic, I had to evaluate something on the original images, Claude 4.7 decided to inpaint the masked images instead of just fetching the actual unmasked images from upstream.
I had another model once that decided that because it couldn't figure out how to fill out a form to log into HuggingFace to download weights for some open source model that it was going to instantiate the model with random weights and run inference on a thousand images.
Its coding was fine, but the solution was not the right one.
There's a lot of room for improving the smaller models at many levels of the stack.
I couldn’t even imagine having to go back to a model from 12 months ago, much less 24 months ago. GPT-5.5 is so much better than GPT-4o that it sure seems like they keep finding new juice to squeeze.
This is like going from dialup internet to DSL and acting like it has peaked before gigabit cable and fiber come along. We are at the beginning of hardware truly made for AI.
Where do I find papers like this? Outside of hacker news comments. It's so hard to find the good stuff in all the noise IMO.
Given how well Qwen3.6-27B performs for such a small model I think you could be right. I suspect that Google,OpenAI,Anthropic must be looking at the Qwen3.6 models (as well as Deepseek V4-flash, MiMo-V2.5) and wondering if they could make some smaller models that are specifically trained for certain activities - like coding. Smaller, more targeted models would take up a lot less resources.
the last?!? I'm excited to see :) I'll take the other side of that since llms are so new
Honestly, there is nothing in my head that Claude cannot handle. Maybe it can be more this or that but I can already barely exploit Opus 4.7.
And I'm using DeepSeek 4 Pro for my personal use and while it's a little behind, it's not that far.
I think the situation can be very dangerous for US AI companies because if current models are already capable of doing mostly anything, nobodoy will want to get to the next model, even if it's 10x better. OTOH, open source models like DeepSeek are doing mostly the same work for 1/10 of the price.
Also the more I play with Pi, the more I think LLMs are already not kept back by their own capabilities but by the lack of agency we allow them to have. There is more value today in a capable harness for current LLMs than in a better LLM.
One idea is that maybe it could figure out how many L's are in the word "google" [1]
Or, maybe which days of the week have a "d" in their spelling [2].
Most software engineers will just need cheap tokens.
But things like physics and drug discovery have no forseeable upper bound.
Software that never breaks (including because it never runs into scaling problems) and never leaks your data is preferable to software that breaks and leaks your data sometimes, but it has been too costly to be practical.
Current models are still very far from the reasoning muscle required to build things that never break, scale to billions of users with no issues, and cannot be exploited.
i think it'll be more like we get 1-10T models and then distill those down into smaller models, though
It seems like the best small models today are all distilled from bigger models
Moreover, I hypothesize Claude Opus 4.7 and now 4.8 are a distillation of Claude Mythos
You can, but it's not as useful as you might think.
It needs to at least understand 1 human language to understand your intent to implement features.
If GRAM turns out to be a 5000x multiplier for local reasoning, you could theoretically train a 500M parameter model on just a programming language to understand stack traces to fix bugs and be incredibly powerful.
But most people also want it to understand human language to implement features as well.
Because then it can't just understand React and JavaScript - it needs to understand thousands of commonly used dependencies, the DOM, CSS, HTML, etc...
And for that you need A LOT more parameters than you might expect.
You can definitely get a ~3B active parameter model that can run comfortably on today's hardware to be VERY good at coding once all of the SOTA architectures are added to a single model - especially if we get better tool calling to give models better context per language.
You might be thinking: why does it need to memorize dependencies? Can't it just stick all of them in it's context and use its super smart brain? No, context is king. You want to keep it as short as possible. The solution is not having a smart model and putting 10M lines of context in it. The solution is having a model with enough parameters to know what it needs to know. Researchers are already working on having "packs" of knowledge where you could download a 20M param pack just for some common dependencies in JavaScript (as an example) - but AFAIK this is likely years away (and may not prove effective).
You could get 100x performance if you feed the models ideal context... So a 3B model today can perform almost as good as ~300B model if you give it really good context vs flood it with mostly garbage it doesn't need across your repository.
If you feed it 100x more context to make up for its limited memorized general knowledge, it's going to perform thousands of times worse, completely eliminating any advantage it might get from GRAM...
Fine tuning a 'lean and smart' model works really well for discrete, repeatable high volume tasks like support ticket triage, lead classification, content filtering, labelling, generating content with a voice, etc.
Inefficient token burn by throwing large models at everything is definitely a problem - it's like hiring Phd's to answer the phone or to wash dishes.
Boomer comparison, but I remember the 8 bit computer era when the hardware was what it was so the later games of that era used hardware better than previous ones.
- why'd a quantum computer help running an LLM?
- of course there'd be need for frontier companies - nobody else has the resources to train frontier models.
Mythos is a bunch of likely overhyped claims at this point. A few experts who looked into the claimed results weren't that impressed.
I’d be surprised tbh. Investors don’t want to hear “everyone else is still training models and seeing improvements, but we don’t want to participate in the arms race anymore.” They want monumental leaps every quarter or two because they have sunk unholy amounts of money into these companies/products.
The whole idea of “hyper scale” doesn’t jive with caution and or otherwise slowing down.
The whole ecosystem will twist and evolve, and the big companies will be left begging for corporate subscriptions.
I finally caved when I realized I could build a PC, for myself, with dual video cards that I wanted, which can play games that I like and run models that I want, without worrying about giving my payment info to someone I don’t trust, or invoking token anxiety that I don’t want.
We have so many ways of optimizing:
- continusly creating more and better training data
- increasing parameters to 20/50/100TB
- We still wait for Mythos access
- We still wait for Mythos distilation (i haven't heard any rumors or so that there is a distilled version of Mythos out)
- Reinforcment learning and evolutionary algortihm only started to appear
- If a small 30GB Model can do stuff, these models can also be used as teachers for the big ones
- We have not seen yet specialized models at all. Like a coding java german expert model. Why? Even with MoE architecture, you still need to have these layers around
- Research for Diffusion and other models is still in progress
- Nvidia just announced/showed a 7x speedup on inferencing for Nemotron
- Multitoken prediction became available just a few weeks ago
- Compute gets only in a range were they can do a lot more and cheaper experiments (see Google IO 2026 announcement)
- World models are showing great progress and we do not know yet what they will bring to the table
- They are probably not finetuning/fixing all areas in parallel. I would argue that Anthropic focuses most of its efforts into coding and agentic. Google for sure does subagent and agentic optimizations too. Plenty of areas are just not touched i would say because they don't have the capacity
- We see more and more mulit modal models (these also consume compute)
- N-Gram paper and co i have not seen all of these things in chinese open models
- We don't even know yet what Meta is doing, but we do know they restarted their efforts again
- Anthropics models got a lot better benchmark wise for dening non sense asks. They do learn how to get rid or reduce hallucinations
- We are in the middle of the biggest Reinforcement loop whith all the training data we give them day to day and its not clear at all if they already use these models in thir training and at what stage.
- We do expect bigger models to be able to comprehend deeper concepts / broader code bases. Big companies with huge code bases probably are waiting for this
- Thre will be also continues progress in harnesses which in it alone is not part of the LLM progress (fair) but these harnesses do get better when you finetune a model to be optimized for a harness
- ChatGPTs Image model 2.0 got relevant better and came out just a month ago
I suspect, based on hardware requirements and progress on hardware infrastructure alone, that the industry wants to go to 100t models and we do not know yet what this will mean. I could see that we might skip normal transformer and find relevant other architectures.
Just a week ago there was a research paper about parallel input and output streams which has not been explored enough.
There was also a research paper were they showed that a LLM can compute things. This will take time to see were this leads to.
I don't think the focus on GRAM and facts is so relevant. Its about context and context handling not just some facts.
If you look at things like Mythic AI and the recent wurtzite ferroelectric nitrides breakthrough from the University of Michigan, huge performance and efficiency gains through new compute-in-memory approaches are around the corner.
And that will get us up to two orders of magnitude more parameters.
It's also plausible to me that before we get all the way to 100T we find some recipe of efficient state synchronization, goal sharing or something so that we are able to get higher collective IQ by combining fast distributed predictive subnetworks.
Can you be a little more specific than that or provide a reference?
I assume you're not indicating universality of neural networks?
I am ready to bet against this. Knowledge benchmark like SimpleQA isn't increasing for small models.
> It is far less clear that a 1.2T model will be meaningfully better enough to justify training it.
Well for one, we know for certain there is Mythos which is meaningfully better. And I think there is a lot of juice left to squeeze for Mythos class model.
Model intelligence and knowledge aren't necessarily directly related. If we can pack greater intelligence and agency at the cost of it forgetting factoids, that would actually be a good thing. We don't need LLMs to memorize facts, we need them to learn how to interact with the world such that they can find the facts that are necessary and surface them to the user.
If we could distill all of the knowledge out of an LLM and just be left with a very agentic model that only knows facts in it's context, I think some very interesting stuff would happen.
There isn't a clear definition of what is knowledge and what is intelligence. Is being able to write in C knowledge? Is knowing undefined behaviour in that knowledge?
Do we?
Have you used it?
What is "meaningfully" better? It's not 3-4 orders of magnitude better. That is definitely happening for smaller models.
Meaningful in the sense it could find security vulnerabilities in browser and kernel that >99% of the engineers couldn't find.
6 is for sure happening...
As is Gemini 4.
It's less certain there will be a Gemini 5 or GPT 7 any time soon that is a true next "generation" and not just an iteration. They will almost certainly call something Gemini 5 and GPT 7...
My conspiracy theory is that Apple recognizes this.
I don't think that's not a conspiracy theory. AFAIK, It's their stated AI policy...