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Uni-1.1
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Uni-1.1 is a closed-weights image model from Luma AI, 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 Uni-1.1, out of 197 with a score, then more from Luma AI.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 19 | 68±3 | comparison only | |||
| 20 | 67±2 | api $0.075/image | |||
| 21 | 67±2 | api $0.040/image | |||
| 22 | 67±5 | comparison only | |||
| 23 | 66±2 | api $0.060/image +local | |||
| more from Luma AI | |||||
| 27 | 65±2 | api $0.10/image | |||
| 99 | 51±2 | api $0.019/image | |||
$ what it's good at
text to image▂▅▅▃▄▄█▃▁▂▁▁ #20 of 126 * · 71% of its score · 1 test
Trails Uni-1.1 Max, Qwen-Image 2.0 Pro, Ideogram 4.0 here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| LMArena text-to-image | 1184.3 ✎ | ▂▂▃▄▇█▄▂▄▂·▂ beats 55/75 | 71% |
image to image / editing▁▁·▃▂▃█▃▃▄▁▁ #18 of 62 * · 27% of its score · 1 test
Beats Nano Banana 2 Lite (Gemini 3.1 Flash-Lite Image), Qwen-Image-2.0-Pro (2026-06-22) here, though they rank higher overall. Trails Grok Imagine Image Pro, Uni-1.1 Max, Grok Imagine Image here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Arena image edit | 1315.2 ✎ | ▂▁▁▂▆▇█▄▆▅▁▂ beats 34/53 | 27% |
$ where to run it
Through an API
api · $0.040/image · lumalabs.ai ↗ · price sourceUSD per generated 2K (2048-pixel) image, text-to-image, Luma Agents API pay-as-you-go. Product: Luma Agents API image generation. API model: uni-1 (Luma's API page labels this price column "Uni-1.1"; uni-1-max is the Max variant). Settings: text-to-image, 2048px output, no reference images, one image. Image edit or one reference image $0.0434; each extra reference +$0.003. Price basis: Luma's published per-image figure; Luma notes prices are approximate and based on billing tokens. Currency: USD. Luma official API pricing documentation, observed 2026-09-26.
$ how the score was worked out · LMArena leaderboard dataset 98%
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 |
|---|---|---|
| 2 | 98% | |
| prior · pull toward the middle | 2% | |
| weighted average | = 66.6 |
$ 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