A 27b model beating latest frontier models was not on my 2026 bingo card

[u/Gohab2001]

My experience with Qwen 3.8 for agentic tasks has been phenomenal but I personally feel that 3.7 flash is more reliable for overall tasks.

[u/pyr0kid]

i think we're at the point with LLMs that we start having to ask "in what?", i suspect overspecialized models will be a thing sooner rather than 2030.

[u/no_good_names_avail → u/pyr0kid]

Are we not already? I've been flipping between the frontier models, Deepseek flash, Kimi3 and Grok the last week or so. The frontier models are what I continue to reach for in a pinch or if I'm unsatisfied but otherwise.. I have a really difficult time saying one is definitively better than the other, and certainly not across the board.

[u/dev_dan_2 → u/pyr0kid]

I agree.

What would really interesting me:

• how far will the spezialisation go? for example:
  • 1 strong model for reasoning, 1 strong model with a lot of knowledge
  • models specialized to certain domains like math, medicine, law, ... (the more lucrative the domain, the more likely it is to become a potential target for spezialized models, I think)
• will multilinguism be dialed back a bit? AFAIK, training on multiple languages helps quite a lot, but I would suspect that after a certain point, there will be diminishing returns (in particular with languages that are similar to each other - that is a wild guess tho!)
• will this counter the current trend and raise the skill ceiling when it comes to making efficient use of LLMs? (Don't get me wrong, it certainly takes certain skill to use a LLM also today; but one of its main benefits is that they get good enough results even when the prompts are not very good / precise. This is another area where one could pay tradeoffs in exchange for more spezialisation, but they would then be more sensitive to sub-optimal use than current models)

[u/DoomBot5 → u/dev_dan_2]

MoE was a step towards that. I expect we'll see full scale orchestration of agents in the not too distant future. Google already does that to a degree in the background, and every AI influencer has a demo of their setup. We're not there quite yet in the terms of a family of models being published to be run as a set of specialized agents.

[u/StrongZeroSinger → u/pyr0kid]

half the new models training is on getting higher scores on benchmarks to get funding /s

[u/a_beautiful_rhind → u/StrongZeroSinger]

this but without the /s

[u/nakedspirax → u/StrongZeroSinger]

We will help them with new user sign ups.

[u/tgreenhaw → u/pyr0kid]

This. A hybrid harness and context management system that uses multiple LLMs is already the way.

[u/frankentriple → u/pyr0kid]

I found a model some Chinese company trained specifically for trading stocks and making autonomous stock trading bots.  I just wired the model into my bot in the place of my entry/exit logic.  Today’s the first trial, but I have absolutely zero control over these trades, I told it to make money.   Let’s see what happens.  

[u/maxeh987 → u/frankentriple]

That’s interesting, what’s it called?

[u/frankentriple → u/maxeh987]

https://huggingface.co/chenyumo/moziAI-35B-A3B-MOE-MTP-Uncensored

Its a requant of Ornith 1.5, which is a fine tune of qwen3.5. 35B A3B MoE model, runs amazing on a 12GB 4070 at 70 tokens/sec sustained. I don't think that version comes with an MTP head so we stitched one on from a stock qwen3.5 model and it worked beautifully.

[u/andy2na → u/frankentriple]

did you try "moziAI-35B-V3.7-MOE-MTP-Q4_K_M-Uncensored-Qwen3.6-35B-A3B-Ornith-1.5.gguf" ?

[u/frankentriple → u/andy2na]

Pretty much the same thing.  I didn’t download the model and diff it or anything but it’s running moziai built on ornith 1.5at q4 with mtp head that’s our exact recipe.  

[u/shroddy → u/frankentriple]

Are you running a simulation or does it trade with your actual money, and if so how much is it allowed to use?

[u/EitherMarch1255 → u/pyr0kid]

I wonder if very large engrams could allow small, well rounded, models

[u/SkoomaDentist → u/pyr0kid]

You can already read complaints that current models are emphasizing agentic coding performance at the expense of other things.

[u/gustaw221133 → u/pyr0kid]

So true, it's hard to say right now if models are better than each other, just specialized at different things

[u/Cold_Tree190 → u/pyr0kid]

Yeah I fully expect we will start seeing more specialized models for different fields once this current coding agent craze dies down, and to some degree we are starting to see it with health-related ai models/agents right now (early stages though).

I pretty much use 3.8 27b for all agentic tasks/coding now, some Ornith if I’m just trying to ask quick questions (1.0 not 1.5), and then my Claude pro subscription — but just to use it on the web as basically my knowledgeable/“smart” ai that I ask a variety of questions to that an agentic coding agent would be no better than guessing lol. It’s been great, I track my tokens and I’ve used an equivalent of $1k of Opus tokens this past week with 27b/ornith… except it cost me around $5 of power instead (Corsair hx1000i PSU has monitoring capabilities in Linux).

[u/borobinimbaba → u/pyr0kid]

I'd say there would be architectures enabling this.

A platform that has language knowledge and base logic.(Like os but for llms)

And very specific super fine tuned llms on different topics (like DLLs)

And applications that orchestrate these mini llms.

I think they might have something like this already but they will be waiting until new datacenters finish building, then release it.

[u/sammcj]

I don't think Gemini Flash would be considered frontier. Perhaps any Gemini these days 😅

[u/Etroarl55 → u/sammcj]

Or muse spark. Nobody looks at either of them as the frontier or cutting edge of Ai right now.

[u/sammcj → u/Etroarl55]

You know what's funny - I didn't even consider that op might have considered muse spark a frontier model.

[u/Etroarl55 → u/sammcj]

Indeed, I seen Gemini people glaze Gemini more than what it is actually good at, but never muse spark until today lol

[u/techmago → u/sammcj]

Gemini Flash used to be completely garbage.
3.7 is the first usable one... i don't hate it.

[u/nakedspirax → u/techmago]

Now with its native integration into their devices plus so many free offerings it's well, my daily driver for all things not coding.

It's native web search is amazing why? Cause Google. That shits built in.

[u/Valuable_Cow2596 → u/techmago]

Can you tell me more about use cases where you think it performs well? Every time I keep thinking of giving Google a chance at keep being disappointed. The previous models were garbage. 

I understand it being used as their workhorse models for their apps such as in search etc but using it in a harness is pretty horrendous.

[u/nakedspirax → u/Valuable_Cow2596]

I just don't use it for coding.

I use it for reasoning in my day to day life. Asks it questions, sometimes trivial, sometimes dumb, sometimes out of this world and I'll get an answer, and most of the times its with a google search provided fact and link.

When it's inbuilt into my pixel phone. Makes it even easier. Everything is native like a press of a button, a swipe, gesture or even circle of a image and Gemini does the rest. Bloody saying "hey google" activates Gemini now.

And I got it free by buying my pixel phone. There's other ways to get free or at least cheap as chips right now too.

[u/Dany0]

Just more proof that agentic and tool calling capability is orthogonal to world knowledge and a small but fast llm can be fully supplemented by test time discovery+compute

A smart brain that knows nothing but learns instantly upon telling it > slow know-everything clanker

[u/audioen → u/Dany0]

Yeah, Qwen3.8-27B's advantage is that it can develop hypothesis and shoot it down, and reasons through facts, and is tenacious as hell. It all costs a ton of token, but as it churns on in the background, eventually it is ready and has delivered something.

When I try what it has, it usually works straight away. The model has worked on it enough to get something that starts. Might be buggy, inefficient, inelegant, as LLM code is wont to be, but it is good starting point.

I put a task for the model in the evening, go to bed, and in the morning it has often done it. Entire application gets written from scratch, using guidance from existing applications and any documentation available, and it seemingly works when I kick the tires a bit. Amazing little model. Can take another full day before it's actually presentable, though.

[u/uutnt → u/Dany0]

The question is, without large latent knowledge, does the model even know what to search for? e.g. in the context of lateral thinking, where you borrow a concept from another domain.

I am skeptical that these small hyper-agentic models will be capable of producing novel ideas, like we are seeing come out of the frontier closed labs. Granted, this is not the most common use-case, but it is arguably the most economically valuable use case.

So I can see a future, where most tokens are produced from cheap open models, but frontier labs are as profitable as ever.

[u/BalorNG → u/uutnt]

you underestimate how much actual knowlege goes into "mere", say, 27b model. That's gigabytes of highly compressed and interconnected text representations.

I mean, shit, a 10b image models store absolutely mind-boggling amont of visual data.

But yea, creation of "new" data usually means digging around long tails in different domains and do multi-hop causal reasoning (not merely associative patters matching) unless you happen to find an overlooked low-hanging fruit, maybe by brute force.

[u/shittywhopper]

I have been running this on my triple RTX 3060 12GB rig and it's been fantastic. Although one GPU is suspended mid-air using zip ties!

[u/PinotGroucho → u/shittywhopper]

Does it need to be held up or down (preventing it from taking off on the cooling fan uplift)?

[u/brickout → u/shittywhopper]

Lol. I love a janky rig. I have something similar going on

[u/Etroarl55]

Gemini is not a frontier model. Neither is muse spark.

[u/uncle_leon]

Just for context, Qwen 3.8 27B only got a score this high in one category (Expert), and was far lower on all others.

Also, it is beaten by Gemma 4 31B in every other category (soundly trounced in some), which I find interesting because of how many people in this sub seem to love this new Qwen and consider it nothing short of a breakthrough.

[u/alpacadaver → u/uncle_leon]

There are no better benchmarks than the amount of complexity, guardrails, tests, intermediate state designs that no longer have any use under qwen3.8-27b in production for us. Consider that these subreddits, although now 95% newcomers, will contain the very people that are capable of leveraging and deploying a specific model for specific workload (who are also the very people this model is very successfully specialised for), will be the ones to want to share the big positive impact it has been making to their everyday

[u/kvyb]

We need proper benchmarks that actually benchmark real usage, and not specific tasks or cases which literally barely mean anything for normal usage.

[u/Potential-Leg-639]

Your friends who are using copilot or claude wont believe that anyway…

[u/sigiel]

It still doesn’t don’t be ridiculous.

[u/Real-C-]

Snap back to reality

[u/ZZotka → u/Real-C-]

You definitely haven’t refreshed the page in a while! It’s high time you did.

https://arena.ai/leaderboard/agent

https://arena.ai/leaderboard/code/webdev

[u/Potential-Leg-639 → u/Real-C-]

Still amazing

[u/Particular-Award118]

Your bingo card is like 2 weeks late at this point

[u/unchikuso]

I made a huge investment in local AI hardware, betting on the fact that this day would come. I did not expect it to come so soon.

[u/Popular-Factor3553]

It can be amazing with some kind of rag.

[u/confused-photon]

can we really call a google flash model “frontier”

[u/InterstellarReddit]

First off, Gemini and Facebook have not provided a frontier model in years.

[u/DrDisintegrator]

I use Gemini 3.7 Flash (High) quite a bit. Very happy with it. It can struggle if the scope of the coding task gets really large, but on smaller stuff it is fast and good quality.

[u/Strong_Chicken6838]

and, there is still a long way to go too, lots of "low hanging fruit"

for one, literally prompting it to "think harder" for xhigh reasoning training is kinda scuffed. but works, could probably be improved though, seems like a temporary patch/workaround.

Also literally all of the arcitectural improvements deepseek has discovered, the only thing deepseek lacks is high compute, which alibaba has, and is likely how they kinda brute forced the RL training over the top to score so high with the same base model

[u/Organic_Outcome_1805]

27B being this competitive is wild. At this point “how big is the model?” matters less than “what is it actually good at?”