Side by side

See the difference.

Scores, capabilities and costs for the models you’re considering.

Scroll to compare models →

Comparison of multilingual-e5-large-instruct, embeddinggemma-300m, Ideogram V4.5 Text to Image
3 modelsintfloatmultilingual-e5-large-instructRemove ×googleembeddinggemma-300mRemove ×ideogramIdeogram V4.5 Text to ImageRemove ×
What’s differentThe Multilingual-E5 models, initialized from XLM-RoBERTa, support up to 512 tokens per input — any longer text will be silently truncated. To ensure optimal performance, always prefix inputs with “query:”…EmbeddingGemma is a 300M parameter multilingual open embedding model from Google DeepMind, designed for efficient deployment even on low-resource devices, producing high-quality text vector representations…Generate high-quality images, posters, and logos with Ideogram 4.5, with accurate text rendering and low, medium, or high quality tiers.
DateApr 17, 2025ListedSep 16, 2025ListedSep 30, 2026Released
Listed rateSee provider quotesSee provider quotesSee provider quotes
Provider endpoints0 endpoints →0 endpoints →1 endpoint →
AA Image ↗2.1Not reportedNot reported1,015.68 ↗Text-to-image · all categories · quality=highIndependent result95% CI 1,000.68–1,030.68 · n=2,634
Context window5122K—
Max output———
Parameters—300M—

Add another model

Search all models →