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Grok Imagine Image 2.0

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Grok Imagine Image 2.0 is a closed-weights image model from SpaceXAI, released August 2026. Licence: proprietary.

78.6±2.1#5 of 144 image modelsfrom 3 benchmark results across 2 sources

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

$ how it compares

The image models ranked either side of Grok Imagine Image 2.0, out of 197 with a score.

#modelscorewhereRAMreleased
4MAI-Image-2.6 Microsoft AI81±2comparison only2026-08
5Nano Banana 2.1 Google81±2api $0.034/image2026-10
6Grok Imagine Image 2.0 SpaceXAI79±2api $0.040/image2026-08
7Reve 2.1 Reve78±2api $0.25/image2026-07
8Muse Image Meta76±2api $0.010/image2026-07

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

$ what it's good at

text to image▂▅▅▃▄▄█▃▁▃▁▁ #6 of 126 * · 64% of its score · 1 test

Trails Reve 2.1 here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
LMArena text-to-image1300.8 ✎▂▂▃▄▇█▄▂▄▃·▂ beats 69/75avg 1027▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 64%typical
image to image / editing▁▁·▃▂▃█▃▃▄▃▁ #5 of 62 * · 24% of its score · 1 test
testresultvs the others in its rundifficultycountswhy
Arena image edit1426.9 ✎▂▁▁▂▆▇█▄▆▅▁▂ beats 48/53avg 1153▇▇▇▇▇▇▇▇▇▇ 24%typical
other tests1 not in a task · 10% of its score
testresultvs the others in its rundifficultycountswhy
Rapidata Benchmark.ai image benchmark (overall Elo)1107.5 ✎▁▁▁▄▂█▂▁▃▂▁▁ beats 28/34avg 875▇▇▇▇ 10%typical

$ where to run it

Through an API

api · xAI · $0.040/image · docs.x.ai ↗ · price sourceUSD per output image, 1K low quality. Image input billed separately at the provider tariff; text-to-image setting has no input images. Product: Image generation API. API model: grok-imagine-image-2.0. Settings: quality=low, resolution=1K, width=1024, height=1024, n=1. Price basis: generated image output component. Currency: USD.

$ how the score was worked out · LMArena leaderboard dataset 88%, Rapidata/text-to-image-human-… 10%

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 dataset2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 88%CC BY 4.0
Rapidata/text-to-image-human-preference-6M (Benchmark.ai Image Benchmark) published dataset-card rankings1▇▇▇ 10%CC-BY-4.0
prior · pull toward the middle▇ 2%
weighted average= 78.6

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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