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Recraft V4.1 Utility Pro
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Recraft V4.1 Utility Pro is a closed-weights image model from Recraft, released May 2026.
Model information sources
Score within the LMArena text-to-image comparison set only.
$ how it compares
The image models ranked either side of Recraft V4.1 Utility Pro, out of 197 with a score, then more from Recraft.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 29 |  Seedream 5.0 Lite | 65±2 | api $0.035/image | ||
| 30 |  Seedream 4.5 | 64±1 | api $0.040/image | ||
| 31 | Recraft V4.1 Utility Pro | 64±2 | api $0.21/image | ||
| 32 |  FLUX.2 [max] | 64±1 | api $0.070/image | ||
| 33 |  Wan 2.7 Image Pro | 63±2 | api $0.075/image | ||
| more from Recraft | |||||
| 46 | Recraft V4.1 Pro | 61±3 | api $0.21/image | ||
| 54 | Recraft V4 | 59±3 | api $0.040/image | ||
| 67 | Recraft V4.1 Flash | 57±3 | api $0.0070/image | ||
$ what it's good at
text to image▂▅▅▃▄▄█▃▁▂▁▁ #22 of 126 * · 84% of its score · 1 test
Beats Grok Imagine Image Pro, Seedream 5.0 Lite, Seedream 4.5 here, though they rank higher overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| LMArena text-to-image | 1169.2 ✎ | ▂▂▃▄▇█▄▂▄▂·▂ beats 52/75 | 84% |
other tests1 not in a task · 13% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Rapidata Benchmark.ai image benchmark (overall Elo) | 953.3 ✎ | ▁▁▁▄▃█▂▁▁▂▁▁ beats 10/34 | 13% |
$ where to run it
Through an API
api · $0.21/image · www.recraft.ai ↗ · price sourceUSD per generated raster image, Recraft V4.1 Utility Pro (same row and rate as V4.1 Pro; V4.1 Utility $0.035; vector output $0.30). Billed as 210 API units per image from pre-purchased unit packages. Product: Recraft API raster image generation. API model: Recraft V4.1 Utility Pro. Settings: raster, n=1. Price basis: published list rate. Currency: USD. Recraft official API pricing page, observed 2026-09-26.
$ how the score was worked out · LMArena leaderboard dataset 84%, Rapidata/text-to-image-human-… 13%
Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works
| source | results | share of the score |
|---|---|---|
 LMArena leaderboard dataset | 1 | 84% |
 Rapidata/text-to-image-human-preference-6M (Benchmark.ai Image Benchmark) published dataset-card rankings | 1 | 13% |
| prior · pull toward the middle | 2% | |
| weighted average | = 63.9 |
$ improve this page
Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.
data updated · sources and licences · json