HyperCLOVA X SEED 14B Think

What it is

HyperCLOVA X SEED 14B Think is a language model from the Korean technology company NAVER. Its weights were uploaded to Hugging Face on 21 July 2025, so they can be downloaded and run under the accompanying licence.^[1] The model has 14.74 billion parameters and a 32K context window, meaning it can take in roughly 32,000 tokens of text at once.^[1]

"Think" describes the training rather than the size: the model is trained to work through intermediate reasoning steps before giving an answer, a pattern that tends to help on maths, logic and other multi-step questions but costs more tokens when the model is used. Under the hood it is a dense Transformer — every parameter is used for every token, unlike mixture-of-experts designs that activate only part of the network per token. Two less common choices appear in the architecture: Peri-Layer Normalization and maximal update parameterisation.^[1] Normalisation layers keep the scale of activations stable during training; maximal update parameterisation is a way of setting training hyperparameters so that they remain appropriate when the model's width changes.

Where the recipe came from

SEED 14B Think pairs NAVER's lightweighting technology with the reasoning recipe of HyperCLOVA X THINK.^[1] THINK is the reference point for that recipe. A technical report published on 27 June 2025 describes THINK as the first reasoning-focused model in the HyperCLOVA X family, pre-trained on about 6 trillion Korean and English tokens.^[2] SEED 14B Think applies that recipe at a much smaller scale.

How it was made smaller

Compression came from two familiar techniques used together. Pruning removes parameters judged to be of low importance, cutting the raw size of the network. Distillation then has the smaller model learn from a larger model rather than only from the original training text, so that part of the larger model's behaviour is carried over into the smaller one.^[1] The point of combining them is to keep capability that would otherwise be lost when a network is made smaller.

The four-stage reasoning recipe

Reasoning training ran in four stages.^[1] The first is supervised fine-tuning, in which the model is trained on examples of the behaviour that is wanted. The second is reinforcement learning with verifiable rewards: the model is rewarded for answers whose correctness can be checked automatically, such as a numerical result that either matches the expected value or does not. The third is length controllability, which trains the model to keep its chain of reasoning within a chosen budget instead of always spending as many tokens as it can. The fourth combines two signals — reinforcement learning from human feedback, in which human preferences score the outputs, and the verifiable-reward signal — in a single training stage.^[1]

Training cost

NAVER says it trained the model at about 52.6 times lower cost than Qwen2.5-14B and about 91.4 times lower than Qwen3-14B.^[1] The model card gives the training cost as 68,049 A100-80GB GPU hours at 50% model flop utilisation: that is, roughly 68,049 hours of computing on Nvidia A100 GPUs with 80 GB of memory, at an assumed 50% of the hardware's peak throughput.^[1] These are the company's own figures and its own comparisons, so they describe NAVER's claim about its training pipeline rather than an independently verified measurement.

Licence and later models

The weights are released under a custom HyperCLOVA X SEED licence rather than an OSI-approved one, so the conditions of use are set by NAVER rather than by a standard open-source licence.^[1]

The model sits in a sequence of releases from the same organisation, which had published 0.5B and 1.5B text models and a 3B vision model in April 2025.^[4] A 32B Think model followed on 23 December 2025, adding vision input and a 128K context window.^[3] The pattern is a family of models whose reasoning variants are built by applying one shared recipe at several sizes.