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Wan 2.6 Text-to-Image

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Wan 2.6 Text-to-Image is a closed-weights image model from Alibaba, released January 2026. Licence: proprietary.

61.3±2.5#37 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 Wan 2.6 Text-to-Image, out of 197 with a score, then more from Alibaba.

#modelscorewhereRAMreleased
41Imagen 4 Ultra Google61±2api $0.060/image2025-06
42GPT Image 1 OpenAI61±1api $0.042/image2025-04
43Wan 2.6 Text-to-Image Alibaba61±3api $0.030/image2026-01
44Reve V1.1 Reve61±4comparison only
45Seedream 4.0 ByteDance Seed61±1api $0.040/image2025-09
more from Alibaba
11Qwen-Image-3.0-Pro Alibaba75±2api $0.040/image2026-07
18Qwen-Image-2.1 Alibaba69±2local~12GB2026-09
20Qwen-Image-2.0-Pro (2026-06-22) Alibaba67±2api $0.075/image2026-06

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

$ what it's good at

text to image▂▅▅▃▄▄█▃▁▂▁▁ #35 of 126 * · 97% of its score · 1 test

Beats Wan 2.7 Image Pro, Wan 2.7 Image here, though they rank higher overall. Trails ERNIE-Image, ERNIE-Image-Turbo here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
LMArena text-to-image1137.5 ✎▂▂▃▄▇█▄▂▄▂·▂ beats 38/75avg 1027▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 97%typical

$ where to run it

Through an API

api · DeepInfra · $0.030/image · deepinfra.com ↗ · price sourceUSD per generated image on DeepInfra; single per-image rate listed (default size 1280*1280, 1-4 images per request billed per image). Product: DeepInfra text-to-image. API model: Wan-AI/Wan2.6-T2I. Settings: num_images=1. Price basis: published list rate. Currency: USD. DeepInfra official model page, observed 2026-09-25.

$ how the score was worked out · LMArena leaderboard dataset 97%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 97%CC BY 4.0
prior · pull toward the middle▇ 3%
weighted average= 61.3

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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