whatmodel.sh / image / Recraft V3
Recraft V3
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Recraft V3 is a closed-weights image model from Recraft, released October 2024.
Model information sources
Score within the LMArena text-to-image comparison set only.
$ how it compares
The image models ranked either side of Recraft V3, out of 197 with a score, then more from Recraft.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 98 | 51±2 | api $0.0050/image | |||
| 99 | 51±2 | api $0.019/image | |||
| 100 | 50±2 | api $0.040/image | |||
| 101 | 50±2 | api $0.040/image | |||
| 102 | 50±2 | api $0.015/image +local | |||
| more from Recraft | |||||
| 31 | 64±2 | api $0.21/image | |||
| 46 | 61±3 | api $0.21/image | |||
| 54 | 59±3 | api $0.040/image | |||
$ what it's good at
text to image▂▅▅▃▄▄█▃▁▂▁▁ #71 of 126 · 84% of its score · 3 tests
Trails Runway Gen-4 Image, GLM-Image, FLUX.1 Kontext [pro] here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| LMArena text-to-image | 1020.6 ✎ | ▂▂▃▄▇█▄▂▄▂·▂ beats 10/75 | 56% |
| GenEval | 73.2% ✎ | ██·▄·▄·█···▄ beats 6/8 | 18% |
| MMMG image generation (average over 15 tasks) | 44.1% ✎ | █··█▄▄·▄▄·▄▄ beats 3/9 | 10% |
other tests1 not in a task · 14% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Rapidata human-face generation alignment (Elo) | 1105.1 ✎ | ▃··▅▃··▃▃··█ beats 6/8 | 14% |
$ where to run it
Through an API
api · $0.040/image · www.recraft.ai ↗ · price sourceUSD per generated raster image, Recraft V3 (vector output $0.08; image-to-image, inpainting and outpainting with V3 are also $0.04). Billed as 40 API units per image from pre-purchased unit packages. Product: Recraft API raster image generation. API model: Recraft V3. Settings: raster, text-to-image, 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 56%, MMMG: a Comprehensive and Rel… 28%, Rapidata/Face_Generation_Benc… 14%
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 |
|---|---|---|
| 1 | 56% | |
| 2 | 28% | |
| 1 | 14% | |
| prior · pull toward the middle | 2% | |
| weighted average | = 50.4 |
$ 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