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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 2026-05-11 · Source: meta_reference_catalog
Mixed text, image, audio and document workloads that benefit from one model across modalities.
Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input...
llama-4-scout is a Multimodal model from Meta (US). HotON.ai tracks it at $0.08 per 1M input tokens and $0.30 per 1M output tokens, with a 328K-token context window. Its composite efficiency score is 91/100 at an estimated $0.000 per successful task.
llama-4-scout is tracked at $0.08 per 1M input tokens and $0.30 per 1M output tokens. A typical 3:1 output-to-input workload blends to roughly $0.24 per 1M tokens. Figures are illustrative demo data.
Mixed text, image, audio and document workloads that benefit from one model across modalities.
llama-4-scout supports up to a 328K-token context window — large enough for long documents and extended conversations in a single request.
Within the HotON.ai tracked set, llama-4-scout is cheaper than 85% of models on input price and ranks #62 of 535 by overall efficiency.
Yes — qwen3.5-flash-02-23 is a lower-cost option at $0.26 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 — llama-4-scout (Meta): $0.08/1M input, $0.30/1M output, as of 2026-05-11. https://hoton.ai/en/models/meta-llama-llama-4-scoutPricing 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.