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Lumiere (T2V)

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Lumiere (T2V) is a closed-weights video model from Google, released January 2024. Licence: proprietary.

Catalogue identity notes

Google research model (arXiv 2401.12945); never offered as a product or API. The text-to-image-to-video (T2I2V) variant is a separate pipeline and is not merged.

26.9±3.6comparison only: no current run option verifiedfrom 2 benchmark results across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The video models ranked either side of Lumiere (T2V), out of 139 with a score, then more from Google.

#modelscorewhereRAMreleased
126ZeroScope v2 576w cerspense27±3local~7.9GB2023-06
127Hotshot-XL Hotshot27±2local~10GB2023-10
128Lumiere (T2V) Google27±4comparison only2024-01
129DynamiCrafter (1024) CUHK / Tencent AI Lab (DynamiCrafter)27±4comparison only2024-02
130Open-Sora 1.2 HPC-AI Tech27±3local~39GB2024-06
more from Google
1Gemini Omni 1.1 Flash Google81±2api $0.10/sec2026-08
3Gemini Omni Flash Google80±2api $0.10/sec2026-05
19Veo 3.1 Google69±1api $0.40/sec2025-10

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

$ what it's good at

text to video▇▅▄▆█▆▇▂▃▃▂▄ #86 of 94 * · 94% of its score · 2 tests
testresultvs the others in its rundifficultycountswhy
VideoPhy (human, SA=1 & PC=1)9.0% ✎▄▄▄·▄▄█····▄ beats 1/7medium · avg 48%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 66%sharp
VideoPhy (auto-evaluator average)33.0% ✎▅·▃▅··▃█···▃ beats 2/9medium · avg 56%▇▇▇▇▇▇▇▇▇▇▇ 28%at its level

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · VideoPhy / VideoPhy-2 officia… 94%

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
VideoPhy / VideoPhy-2 official repository leaderboards2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 94%MIT
prior · pull toward the middle▇▇ 6%
weighted average= 26.9

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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