What is the best AI text-to-vector model?

Based on blind community voting, Recraft V4 SVG is currently the #1 ranked AI text-to-vector model with an Elo rating of 1328. Rankings update in real time as new votes come in.

How are AI text-to-vector models ranked on Lumenfall?

Lumenfall Arena ranks AI models through blind community voting. In each matchup, two models generate from the same prompt and voters pick the better result without seeing model names. Votes are processed using TrueSkill, a Bayesian rating algorithm developed by Microsoft Research, that produces a single Elo score reflecting each model's relative quality.

What is an Elo rating for AI models?

An Elo rating is a numerical score representing a model's skill relative to other models. Under the hood, Lumenfall uses TrueSkill, which tracks two values per model: mu (estimated skill) and sigma (uncertainty). The displayed Elo is calculated as 1000 + 10 x (mu - 3*sigma), a conservative lower bound. A model must prove itself consistently across many matchups to earn a high rating.

Best AI Models for Path Cleanliness

Path Cleanliness · Text-to-Vector

Elo rankings from blind votes across 5 challenges in this category.

Highlights

10 images

Quality bought per dollar

Every ranked model plotted by price against its Elo. The upper-left frontier is the best value: high quality at low cost.

Best Text-to-Vector Models by Price

1 model without pricing omitted.

Best AI Models for Path Cleanliness

5 models ranked · Last update: November 5, 2026 9:03 AM

#	Model	Price	¢/img	Speed	Elo
1	Recraft V4 SVG Recraft AI	$$$	8¢		1328
2	Recraft V4 Pro SVG Recraft AI	$$$$	30¢	—	1241
3	Recraft V4.1 SVG Recraft AI	$$$	8¢		1209
4	GPT-5.5 OpenAI	—	—		1208
5	Recraft V4.1 Pro SVG Recraft AI	$$$$	30¢		1205

Recraft AI dominates the Path Cleanliness rankings, with the Recraft V4 SVG leading the category at 1328 Elo and a 77.5% win rate. While GPT-5.5 competes closely in overall Elo, it suffers from a significantly lower win rate of 34.6% and slower generation speeds compared to the specialized Recraft models.

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Highlighted challenges

Text-to-Vector Simplicity & Compactness Prompt Adherence

Pelican on a bike

It’s hard because it forces a model to combine the rigid mechanical geometry of a bicycle with the organic anatomy of a pelican and then compose them into a coherent scene where the bird is actually pedaling. Even leading models frequently get the frame structure wrong or misplace the pelican’s limbs, which makes it a surprisingly revealing test of spatial reasoning.

Text-to-Vector Prompt Adherence Aesthetics

Cat drinking milk

Tests multi-subject scene composition and a spatial relationship that’s easy to get wrong (boy outside, cat inside, looking through glass). The whole point is warmth and charm, not precision, so it rewards clean appealing rendering over technical accuracy.

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