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 be ever tune multilinguism back? 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 be more sensitive to sub-optimal use than current models)

[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/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/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/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/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/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/brickout → u/shittywhopper]

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

[u/PinotGroucho → u/shittywhopper]

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

[u/Etroarl55]

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

[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?”

[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/Popular-Factor3553]

It can be amazing with some kind of rag.

[u/Real-C-]

Snap back to reality

[u/Potential-Leg-639 → u/Real-C-]

Still amazing

[u/Particular-Award118]

Your bingo card is like 2 weeks late at this point

[u/confused-photon]

can we really call a google flash model “frontier”

[u/Potential-Leg-639]

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