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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
Source: litellm
Mixed text, image, audio and document workloads that benefit from one model across modalities.
Microsoft Phi 4 Multimodal Instruct processes text, image, and audio inputs and returns text for lightweight multimodal workloads.
phi-4-multimodal-instruct is a Multimodal model from Microsoft (US). HotON.ai tracks it at $0.08 per 1M input tokens and $0.32 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.
phi-4-multimodal-instruct is tracked at $0.08 per 1M input tokens and $0.32 per 1M output tokens. A typical 3:1 output-to-input workload blends to roughly $0.26 per 1M tokens. Figures are illustrative demo data.
Mixed text, image, audio and document workloads that benefit from one model across modalities.
phi-4-multimodal-instruct 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, phi-4-multimodal-instruct is cheaper than 85% of models on input price and ranks #197 of 537 by overall efficiency.
Yes — gemini-2.0-flash-lite-001 is a lower-cost option at $0.30 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 — phi-4-multimodal-instruct (Microsoft): $0.08/1M input, $0.32/1M output. https://hoton.ai/en/models/microsoft-phi-4-multimodal-instructPricing 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.