Llama 4 Scout is a natively multimodal mixture-of-experts model with 17 billion active parameters across 16 experts and 109 billion total parameters. Released in April 2025, it is built for efficiency, fitting on a single NVIDIA H100 GPU with Int4 quantization while holding strong performance across reasoning, coding, and visual understanding. As served here it supports a 128,000-token context window. Scout was trained on roughly 40 trillion tokens of data covering 200 languages, with dedicated fine-tuning support for 12 languages, and has an August 2024 knowledge cutoff. It uses early fusion for native multimodality and includes architecture features such as NoPE layers and chunked attention to help with long-context handling. The model supports tool use and structured output, which makes it flexible for agentic workflows. Compared to its larger Maverick sibling, Scout prioritizes efficiency and deployability while keeping strong image grounding and visual question-answering capability. Meta reports it outperforms Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1 across a broad range of widely reported benchmarks.
Llama 4 Scout 17B 16E Instruct runs on 2 different routes through the qynio gateway. Compare residency, zero retention, and training posture at a glance — full data-handling detail per route below.
Llama 4 Scout 17B 16E Instruct runs on 2 independently monitored routes through the qynio gateway. A direct integration leaves you on one of them, the gateway serves whichever is healthy.
Measured over the last 30 days from each provider's official status feed via StatusGator. Refreshed hourly. See uptime for every provider qynio monitors.
Each route hosting Llama 4 Scout 17B 16E Instruct has its own privacy posture, residency, and no-KYC terms. Postures are maintained by qynio with a last-verification timestamp.
Zero data retention is on by default on Pay-as-you-go — no action required. No training on customer data. US; unknown; deposit available.
Zero data retention is on by default on Pay-as-you-go — no action required. No training on customer data. US; wallets; deposit available.
Call Llama 4 Scout 17B 16E Instruct through the qynio gateway with one API key. Let your coding agent set it up, or call it directly — qynio is drop-in compatible with the OpenAI, Anthropic, and Google AI SDKs.
Copy this and paste it into a coding agent like Claude Code, Cursor or Codex and it'll wire up qynio for you.
import OpenAI from "openai";const client = new OpenAI({apiKey: process.env.QYN_API_KEY,baseURL: "https://api.qyn.io/v3/compat",});const completion = await client.chat.completions.create({model: "deepinfra/meta-llama/Llama-4-Scout-17B-16E-Instruct",messages: [{ role: "user", content: "Hello" }],});console.log(completion.choices[0].message.content);
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