Korea's AI Blitz: Four Frontier Models, One Week

Four frontier models landed in Korea in the last week of December. A.X K1 from an SK Telecom consortium on the 27th, VAETKI and K-EXAONE on the 30th, Solar-Open on the 31st. Announced days apart, most of them presented at the same government event at COEX in Seoul, which tells you this was scheduled rather than coincidental.

The headline numbers are big — 519 billion parameters for A.X K1, 236 billion for LG's K-EXAONE — and I think the headline numbers are the least interesting part. What caught my attention is that all four made the same two bets, and neither bet is the one the US and Chinese labs have been making.

Almost nothing is switched on

Every one of these models is sparse. Look at what actually runs per token:

A.X K1 activates 33B of its 519B. VAETKI activates 10-11B of 112B. Solar-Open, 12B of 102B. K-EXAONE, 23B of 236B. That's between 6% and 12% of the declared parameter count doing any work at a given moment. The rest is Mixture of Experts scaffolding — capacity that exists so the router has somewhere to route, not because it all fires.

This is the number that matters commercially, because inference cost tracks active parameters, not total ones. A 236B model that behaves like a 23B model at serving time has completely different economics from a 236B dense model, and LG leaned into that: K-EXAONE runs on A100s. Not H100s, not B200s. Hardware you can already rent cheaply, or already own.

There's a pattern here that shows up elsewhere too. The Lottery Ticket Hypothesis found that a trained network's useful computation lives in maybe 10-20% of its weights, with the rest serving as search space. MoE arrives at a similar ratio from the opposite direction — decide up front that most of the model stays dark, and route around it. Different mechanism, same underlying suspicion: the parameters that matter are a small fraction of the parameters you paid for.

Open, and openly strategic

The second bet is that all four are open or heading there. Solar-Open shipped under an Apache-2.0-style license with weights on Hugging Face the day it was announced. K-EXAONE is up as LGAI-EXAONE/K-EXAONE-236B-A23B. VAETKI is released. A.X K1 has a stated plan for open weights, APIs, and partial training data disclosure.

I don't read this as generosity. Korea is fourth or fifth in line for attention in a race that gets covered as US-versus-China, and open weights are the cheapest way to get built on. If developers fine-tune your model, you become infrastructure without having to win a marketing war. SK Telecom is explicit about the ambition — it's positioning A.X K1 as a teacher model for distilling smaller ones, with 20-plus institutions signed up.

The distribution story is the part that's easy to miss from outside. A.X K1 has SK Telecom's A-DoT platform behind it and something like 20 million users of consumer surface area. Most open model releases are weights and a README. This one arrives attached to a telco.

Are they actually good

Somewhat, in the places they're aiming at.

LG reports K-EXAONE averaging 72.03 against Qwen3's 69.37 and GPT-OSS-120B's 69.79 — a real result, and also a vendor-reported one on a benchmark suite the vendor chose. I'd hold it loosely until independent evaluations land. Solar-Open trained on roughly 19.7 trillion tokens with a 128K context; K-EXAONE natively handles 256K and claims a 70% memory reduction from its 3:1 hybrid attention scheme.

None of that makes them the best models in the world at English reasoning or creative work, and I don't think anyone involved is claiming so. What they are is credible at Korean and English, deployable on-premise, cheap to serve, and licensed permissively. For an enterprise that needs a model inside its own network for regulatory reasons, that combination beats a slightly smarter model behind someone else's API.

Why I think this is worth noticing

The interesting thing isn't that Korea has frontier models. It's the argument implicit in how they built them: that the frontier worth competing on is deployment economics, not benchmark ceilings.

Everyone hits diminishing returns on scale eventually. The labs that got there first are now spending enormous effort on making inference cheaper — distillation, quantization, sparsity, routing. Korea skipped to that phase, partly by necessity. When you have less compute and less capital than your competitors, efficiency isn't a virtue you choose, it's the only door available.

Which makes this a useful natural experiment. If a coordinated national program with a fraction of the budget can produce open, sparse, A100-deployable models that are competitive for most real workloads, that says something about how much of the current spending is buying capability versus buying the last few points on a leaderboard.

We'll know more in six months, when people have actually fine-tuned these things and reported back. For now the thing I'd take away is narrower and more durable: sparse and open is a coherent strategy, four serious teams converged on it in the same week, and none of them needed the biggest cluster in the world to do it.

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Primary sources if you want to dig in: SK Telecom on A.X K1, Solar-Open on Hugging Face, K-EXAONE on Hugging Face, and The Korea Times on the national foundation model project.