Data quality is
the compute
multiplier.
You pay for every token you train on, and costs are rising fast. Data curation dramatically improves data quality, raising the signal per token to get the best model out of your compute resources.
Better data quality gives you more compute.
More signal per token means your compute budget does more, a lot more.
fewer training FLOPs to match or beat leading models
ÜberWeb 20T-token multilingual curation
What would you do with 10x more compute?
One multiplier. Three ways to spend it.
Higher token quality is a powerful multiplier that you get to allocate to your model training. Spend it training a more capable model, a smaller, faster model, or spend less. The choice is yours.
A better model on the compute you have
Keep your compute fixed. Higher-signal tokens give you a more capable model, so the curve moves and your budget does not.
A smaller model that rivals ones far larger
Curate for your use case, cut parameters, and spend your compute training a smaller model to punch above its size that is cheaper to run on every query.
Train more often, iterate faster
Reaching your target takes far less compute, so each run costs a fraction as much. Put that headroom into speed, iterating faster and testing more ideas, or into breadth, training a specialized model for every use case.
The multiplier doesn't stop at training.
You pay to train once, then pay for inference on every query, and that bill grows as your model goes live. A model built on curated data delivers the same quality at a lower cost per query, and the savings stack and compound over time, making it easier to scale.
A smaller, faster model
A model focused on your domain reaches the quality you need at a smaller size, so it costs less to run on every query it serves.
The Finetuner's Fallacy: a 1B model on curated domain data beat a standard 3B.
Learn MoreA more concise answer
Built for your domain, the model answers directly instead of padding, reaching the same correct answer in far fewer tokens.
Brevity is the Soul of Inference Efficiency: up to ~40× fewer tokens at matched accuracy.
Learn MoreFaster, cheaper reasoning
Reasoning models and agents spend far more tokens on every task: long chains of thought, many tool calls. A faster, more concise model makes that work cheaper at every step, and the edge compounds as token volume climbs.
Arcee's Trinity: a frontier agentic reasoning model shipped at ~96% lower inference cost.
Learn MoreData quality raises the ceiling.
Curated data gets you to a given level faster, and it doesn't stop there: it raises the ceiling. Training on low-quality data eventually hits diminishing returns, where more compute barely moves capability and the curve flattens. Better data lifts where that ceiling sits, so the best model you can reach is one uncurated data never gets to.
One multiplier, proven across model types.
Each paper demonstrates the compute multiplier on a different kind of model, measured to a given capability.
ÜberWeb
3B and 8B models matched strong public baselines with 4–10× fewer training FLOPs, across 13 languages at 20T-token scale.
CLIP Gets a Data Upgrade
Curated data beat SigLIP2, MetaCLIP, and DFN on zero-shot ImageNet with up to 8× training efficiency. Same model, better data.
20/20 Vision Language Models
Curation alone raised accuracy ~12 points across 20 tests, and matched leading models on a fraction of the compute, up to ~87× less.
Real results from teams building their own models.
Legal domain adaptation.
Mid-training against a proprietary legal corpus broke through the post-training ceiling on both public and proprietary evals, on a budget under 1% of base pre-training (100B mid-training vs. 15T pre-training).
Frontier open-weights model.
A frontier-class open weights MoE built without a frontier budget or research team. ~10 people, 20T tokens curated by Datology. Competitive with models trained by 170-person teams that raised $2B.
See the Compute Multiplier in Action
Meet with a data curation expert to see how data quality can be a game changer for your business.