whatmodel.sh / image / Ideogram 2.0
Ideogram 2.0
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Ideogram 2.0 is a closed-weights image model from Ideogram.
Score within the LMArena text-to-image comparison set only.
$ how it compares
The image models ranked either side of Ideogram 2.0, out of 197 with a score, then more from Ideogram.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 116 | 47±2 | api $0.050/image +local | |||
| 117 | 47±15 | comparison only | |||
| 118 | 46±2 | api $0.080/image | |||
| 119 | 46±5 | comparison only | |||
| 120 | 46±2 | api $0.040/image | |||
| more from Ideogram | |||||
| 19 | 68±3 | comparison only | |||
| 23 | 66±2 | api $0.060/image +local | |||
| 94 | 52±2 | api $0.060/image | |||
$ what it's good at
text to image▂▅▅▃▄▄█▃▁▂▁▁ #77 of 126 · 98% of its score · 4 tests
Beats Latent Diffusion text2img-large (LDM-KL-8-G, LAION-400M), VQ-Diffusion (ITHQ) here, though they rank higher overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| LMArena text-to-image | 1013.3 ✎ | ▂▂▃▄▇█▄▂▄▂·▂ beats 8/75 | 57% |
| GenEval | 61.5% ✎ | ██·▄·▄·█···▄ beats 2/8 | 20% |
| MMMG knowledge-image generation (MMMG-Score, average over education levels) | 12.2% ✎ | ▃··▃·█▃▃▅··▃ beats 2/9 | 12% |
| MMMG image generation (average over 15 tasks) | 50.8% ✎ | █··█▄▄·▄▄·▄▄ beats 5/9 | 10% |
$ where to run it
Through an API
api · Replicate · $0.080/image · replicate.com ↗ · price sourceUSD per output image on Replicate's official ideogram-ai/ideogram-v2 model (billing: $0.08 per output image, "or around 12 images for $1"; hardware time not added). Product: Replicate model API. API model: ideogram-ai/ideogram-v2 (Ideogram 2.0, not the Turbo variant). Settings: one output image. Price basis: published list rate. fal's fal-ai/ideogram/v2 also bills $0.08 per image. Ideogram's own API still serves V_2 through its legacy /generate endpoint, but its API pricing page did not render a price. Currency: USD. Not an Ideogram direct tariff. Replicate official model page, observed 2026-09-26.
$ how the score was worked out · LMArena leaderboard dataset 57%, MMMG: a Comprehensive and Rel… 30%, MMMG: A Massive, Multidiscipl… 12%
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 |
|---|---|---|
| 1 | 57% | |
| 2 | 30% | |
| 1 | 12% | |
| prior · pull toward the middle | 2% | |
| weighted average | = 46.2 |
$ 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