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Daily blended price ($/1M) — recorded each day, builds into a trend over time.
Typical 3:1 output-to-input mix, per 1M tokens
Price as of 2026-04-28 · Source: legacy_model_catalog
Mixed text, image, audio and document workloads that benefit from one model across modalities.
Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabili...
gemma-3-4b-it is a Multimodal model from Google (US). HotON.ai tracks it at $0.04 per 1M input tokens and $0.08 per 1M output tokens, with a 131K-token context window. Its composite efficiency score is 89/100 at an estimated $0.000 per successful task.
gemma-3-4b-it is tracked at $0.04 per 1M input tokens and $0.08 per 1M output tokens. A typical 3:1 output-to-input workload blends to roughly $0.07 per 1M tokens. Figures are illustrative demo data.
Mixed text, image, audio and document workloads that benefit from one model across modalities.
gemma-3-4b-it supports up to a 131K-token context window — large enough for long documents and extended conversations in a single request.
Within the HotON.ai tracked set, gemma-3-4b-it is cheaper than 94% of models on input price and ranks #287 of 535 by overall efficiency.
Yes — glm-ocr is a lower-cost option at $0.03 per 1M output tokens, while still covering similar Multimodal use cases. Compare them side by side on HotON.ai.
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HotON.ai — gemma-3-4b-it (Google): $0.04/1M input, $0.08/1M output, as of 2026-04-28. https://hoton.ai/en/models/google-gemma-3-4b-itPricing is real (via the TestKey catalog, updated daily). Quality (Arena Elo) is real where the model is ranked on LMArena. Efficiency is a modeled composite of real price and context.