10 million tokens of context — enough to read 30 novels in one conversation. Llama 4 Scout is Meta's ultra-long context AI, free on LITHOVEX AI.
Llama 4 Scout 17B (16 Experts) is an AI model released April 2025 by Meta Llama, a leading organization in large language model research. Llama-4 Scout — MoE model with 10M token context. Unmatched for massive documents, full codebases, and ultra-long tasks. With 17B active / 16 experts and a 10M tokens context window, Llama 4 Scout 17B (16 Experts) is designed to excel across a range of demanding tasks — from software engineering and mathematical problem-solving to multilingual content generation and long-document analysis.
Llama 4 Scout 17B (16 Experts) employs a Mixture-of-Experts (MoE) architecture with 17B active / 16 experts. In a MoE design, only a subset of "expert" sub-networks activates for each token during inference — Llama 4 Scout 17B (16 Experts) has 17B active / 16 experts total parameters but uses only a fraction per token. This allows the model to store significantly more learned knowledge than a comparably-priced dense model. MoE architectures have proven effective at reaching frontier-level capability while keeping serving costs manageable — a key reason Llama 4 Scout 17B (16 Experts) can be offered at the scale that platforms like LITHOVEX AI make possible.
As of 2026-10-16, Llama 4 Scout 17B (16 Experts) by Meta Llama demonstrates measurable performance on standard AI evaluation benchmarks. On MMLU — which tests world knowledge across 57 academic subjects from elementary to professional level — it achieves 79.9%. For software engineering tasks, Llama 4 Scout 17B (16 Experts) scores 80.1% on HumanEval, which tests ability to write correct functions from docstrings. These scores place Llama 4 Scout 17B (16 Experts) — at 17B active / 16 experts of parameters — in a competitive position within its model class. Note that benchmark scores reflect controlled evaluation conditions; real-world performance varies by task, prompt design, and domain specificity.
The primary strengths of Llama 4 Scout 17B (16 Experts) are: 10 million token context — largest publicly available context window as of 2025 (roughly 30 full novels); Native multimodal: text, images, video frames; Efficient 16-expert MoE — faster inference than Maverick. These make it particularly well-suited for Ultra-long document processing: entire legal code sets, multi-volume research, Video understanding with multi-hour temporal context, Multi-document enterprise knowledge bases. Users working on complex, multi-step problems benefit most from Llama 4 Scout 17B (16 Experts)'s ability to maintain coherence across long inputs and produce structured, verifiable outputs.
Like all current AI systems, Llama 4 Scout 17B (16 Experts) has limitations: Quality degrades significantly at extreme context extremes; Not as capable as Maverick on complex reasoning. For production deployments, validating outputs against authoritative sources — especially for domain-specific or time-sensitive information — remains best practice. Meta Llama continues to improve Llama 4 Scout 17B (16 Experts) through ongoing research.
Llama 4 Scout 17B (16 Experts) is available on LITHOVEX AI with a free account. Unlike direct API access — which requires billing setup, API key management, and per-token charges — LITHOVEX AI provides a seamless browser interface with no setup required, making Llama 4 Scout 17B (16 Experts) accessible to students, researchers, developers, and early-stage teams globally.
As of 2026-10-16, Llama 4 Scout 17B (16 Experts) by Meta Llama demonstrates measurable performance on standard AI evaluation benchmarks. On MMLU — which tests world knowledge across 57 academic subjects from elementary to professional level — it achieves 79.9%. For software engineering tasks, Llama 4 Scout 17B (16 Experts) scores 80.1% on HumanEval, which tests ability to write correct functions from docstrings. These scores place Llama 4 Scout 17B (16 Experts) — at 17B active / 16 experts of parameters — in a competitive position within its model class. Note that benchmark scores reflect controlled evaluation conditions; real-world performance varies by task, prompt design, and domain specificity.
Mixture-of-Experts: 17B active / 16 experts, native multimodal
The training pipeline for Llama 4 Scout 17B (16 Experts) involves multiple stages: large-scale pretraining on Native multimodal pretraining; exact data composition proprietary to develop broad world knowledge, followed by supervised fine-tuning on instruction-response pairs, and alignment techniques such as RLHF or DPO. Each stage refines the model's helpfulness, accuracy, and safety. Meta Llama's research team — based in United States — has optimized Llama 4 Scout 17B (16 Experts) for the specific capability profile described above.
Inference efficiency: Llama 4 Scout 17B (16 Experts) at 17B active / 16 experts is designed to deliver quality outputs at practical serving speeds. Its 10M tokens context window enables processing entire documents, multi-file codebases, and extended conversation histories — particularly valuable for contract review, technical documentation analysis, and codebase-level AI assistance.
Getting started with Llama 4 Scout 17B (16 Experts) on LITHOVEX AI takes under two minutes. Follow these steps:
For best results with Llama 4 Scout 17B (16 Experts): provide specific, context-rich prompts; include examples when generating code; specify output format when structure matters; break complex tasks into focused sub-questions.
Processing entire codebases (10M tokens) for repository analysis
Long legal case analysis and precedent research
Multi-document synthesis for academic research
Book-length content editing and summarization
Extended agentic workflows requiring full conversation history
Meta AI is the artificial intelligence research division of Meta Platforms (formerly Facebook), founded in 2013 as Facebook AI Research (FAIR). With one of the largest AI research teams in the world and access to Meta's vast computational resources, Meta AI has produced foundational research across computer vision, natural language processing, and LLMs. The Llama model family — from Llama 1 in February 2023 through Llama 4 in 2025 — became one of the most significant open-weight model releases in AI history, enabling thousands of derivative models and downstream applications. Llama 4 introduced native multimodality and context windows exceeding 1 million tokens, setting new benchmarks for what open-source AI can deliver.
Funding: Public company (NASDAQ: META, $1.4T+ market cap)
Founded: 2004, headquartered in United States.
Llama 4 Scout 17B (16 Experts) by Meta Llama competes in a field with several strong alternatives. Below is a detailed side-by-side comparison against commonly listed alternatives, using benchmark data and capability profiles in the catalog.
Both Llama 4 Scout 17B (16 Experts) and the models above are available free on LITHOVEX AI — test them side by side on your real tasks before committing to a subscription with any provider.
Llama 4 Scout 17B (16 Experts) is released under Llama 4 Community License, which means you can download and run it on your own hardware. Below are the quickest ways to get started.
LITHOVEX AI runs Llama 4 Scout 17B (16 Experts) on cloud infrastructure, so you can use it instantly in your browser without downloading anything, configuring drivers, or managing VRAM. Create a free account and start in under 60 seconds.
Llama 4 Scout represents Meta's leap into native multimodality and extreme context lengths. With a 10M token context window, it enables processing of entire codebases, legal case files, and research libraries in a single session — a capability valued globally by researchers, lawyers, and engineers alike.
Llama 4 Scout 17B (16 Experts) by Meta Llama has the following version history and notable updates:
For the complete changelog and latest release notes, visit the official Meta Llama model page.