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...
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Meta: Llama 4 Scout holds the #154 spot for context size out of 392 chat models in the CoreAI catalog — 328K tokens, roughly 245,760 words or 492 pages of text in a single conversation. On price, it is the #48 cheapest of 128 budget-tier models by input cost. It joined the catalog on 2025-04-05. It accepts image input alongside text.
Every model in this table is available on CoreAI — open the web app and run the same prompt through Meta: Llama 4 Scout and Meta: Llama 3.3 70B Instruct side by side to judge with your own eyes.
A typical question (about 1,000 tokens in, 500 out) runs $0.00025 on Meta: Llama 4 Scout's raw per-token rates; a heavy session with a 50K-token document costs about $0.007. On CoreAI you don't juggle these numbers — one subscription covers Meta: Llama 4 Scout and 300+ other models under a plan usage limit, with no API keys. See plans.
Meta: Llama 4 Scout handles 328K tokens of context — roughly 245,760 words, or about 492 pages of text in one conversation.
Meta: Llama 4 Scout's underlying rates are $0.1 per 1M input tokens and $0.3 per 1M output tokens. On CoreAI it is included in one subscription alongside 300+ models, so there is no per-token billing to manage.
Yes — Meta: Llama 4 Scout accepts image input, so you can attach screenshots, photos or diagrams and ask questions about them.
No — Meta: Llama 4 Scout answers directly without an extended reasoning phase, which usually means faster responses.
Open the CoreAI web app or the iOS/Android app, pick Meta: Llama 4 Scout in the model selector, and chat — no API key, no per-token billing. One subscription covers Meta: Llama 4 Scout and 300+ other models.
The closest alternatives on CoreAI are Meta: Llama 3.3 70B Instruct, Amazon: Nova Lite 1.0, inclusionAI: Ling-2.6-flash — similar tier and capability. The fastest way to choose is running one prompt through them side by side in CoreAI's Compare view.
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