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Kling Image O1

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Kling Image O1 is a closed-weights image model from Kuaishou (Kling AI), released December 2025.

Model information sources

Release date ↗

60.0±4.0#43 of 144 image modelsfrom 1 benchmark result across 1 source* thinly tested, treat the rank as provisional

Score within the LMArena text-to-image comparison set only.

$ how it compares

The image models ranked either side of Kling Image O1, out of 197 with a score.

#modelscorewhereRAMreleased
48Emu3.5-Image BAAI60±5local~85GB2025-10
49ERNIE-Image Baidu60±2api $0.030/image +local~17GB2026-04
50Kling Image O1 Kuaishou (Kling AI)60±4api $0.028/image2025-12
51Krea 2 Medium Krea60±3api $0.030/image2026-05
52Imagen 4 Google59±2api $0.040/image2025-05

see all image models · find the ones that fit your machine

$ what it's good at

image to image / editing▁▁·▃▂▃█▃▃▄▁▁ #28 of 62 * · 93% of its score · 1 test

Beats FLUX.2 [flex], Nano Banana (Gemini 2.5 Flash Image), GPT Image 1 here, though they rank higher overall.

testresultvs the others in its rundifficultycountswhy
Arena image edit1251.2 ✎▂▁▁▂▆▇█▄▆▅▁▂ beats 22/53avg 1153▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 93%typical

$ where to run it

Through an API

api · Kling AI · $0.028/image · kling.ai ↗ · price sourceUSD per generated image, Kling Image O1 at 1K or 2K: Kling lists 8 Units ($0.028) per image for text-to-image and image-to-image (1 Unit = $0.0035 list price). Product: Kling Image Generation API. API model: Kling Image O1. Settings: resolution 1K/2K, one image. Price basis: 8 units × $0.0035 per unit; Kling prints $0.028. Prepaid resource packages. Currency: USD. Kling AI developer pricing page (price table embedded in the page's configuration data), observed 2026-09-26.

$ how the score was worked out · LMArena leaderboard dataset 93%

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

sourceresultsshare of the scorelicence
LMArena leaderboard dataset1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 93%CC BY 4.0
prior · pull toward the middle▇▇ 7%
weighted average= 60.0

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Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

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