Llama4 Maverick, Lllama4 Scout, Llama4 Behemoth
Finally, Meta woke up from sleep amid the war between OpenAI and Google for the best GenAI model, and they released the new series of Llama models, Llama 4, that too completely open-sourced.
https://medium.com/media/b2acbb3d03dd5f794a1056f0bf9f7573/href
What is Llama 4 by Meta?
Llama4 is a series of LLMs, not just one LLM, comprising multiple AI models released by Meta in different sizes that support both multilingual and multimodal capabilities.
https://medium.com/media/57c39382c7de7326b0dd88c1cbdf766f/href
Key Takeaways: Llama 4 Overview
https://medium.com/media/febe615a3a9e6ca78ee90a9b73b90f63/href
- First open-weight, natively multimodal models in the Llama ecosystem.
- Mixture-of-Experts (MoE) architecture for efficiency and performance.
Three models announced:
Llama 4 Scout (17B active params, 16 experts) — Efficient, long-context.
Llama 4 Maverick (17B active params, 128 experts) — High-performance multimodal.
Llama 4 Behemoth (288B active params, 16 experts) — Teacher model (still training).
- Unprecedented 10M token context window (Scout).
This is a huge context length, this is almost equivalent of 75 textbooks or entire encyclopedia !
- Outperforms competitors like GPT-4o, Gemini 2.0, and DeepSeek v3 in benchmarks.
1. Models and Their Capabilities
(a) Llama 4 Scout
- Params: 17B active, 16 experts, 109B total.
Key Features:
10M token context window (industry-leading).
Fits on a single NVIDIA H100 GPU (with Int4 quantization).
Optimized for multi-document summarization, long-code reasoning, and image grounding.
Beats Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1 in benchmarks.
(b) Llama 4 Maverick
- Params: 17B active, 128 experts, 400B total.
Key Features:
Best-in-class multimodal performance (text + images).
Comparable to DeepSeek v3 in reasoning/coding at half the active params.
Runs on a single H100 host (distributed inference optional).
Outperforms GPT-4o and Gemini 2.0 Flash in benchmarks.
c) Llama 4 Behemoth (Preview)
Params: 288B active, 16 experts, ~2T total.
Role: Teacher model for distillation (not yet released).
Performance: Beats GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro in STEM benchmarks (MATH-500, GPQA Diamond).
2. Architecture & Training Innovations
Understanding in brief the architectural nuances
(a) Mixture-of-Experts (MoE)
Only a subset of experts activated per token, improving efficiency.
Llama 4 Maverick: 128 routed experts + 1 shared expert per layer.
FP8 training for efficiency (390 TFLOPs/GPU on 32K GPUs for Behemoth).
(b) Native Multimodality (Early Fusion)
Unified text + vision backbone (joint pre-training).
MetaCLIP-based vision encoder, fine-tuned for LLM compatibility.
Supports multi-image inputs (pre-trained on 48 images, tested up to 8).
Training Techniques
MetaP: Dynamic hyperparameter tuning (learning rates, initialization).
Mid-training enhancements: Extended context (10M tokens via specialized datasets).
Post-training:
Lightweight SFT → Online RL → Lightweight DPO.
Hard-prompt filtering (removed 50% “easy” data for better reasoning).
Continuous RL with adaptive difficulty scaling.
(d) Data & Multilinguality
30T+ tokens (2x Llama 3), including text, images, and videos.
200 languages (100+ with >1B tokens each).
3. Benchmarks and Performance
Summarizing the above results for Llama 4
- All-rounder: Performs at or near the top across vision, text, reasoning, coding, and multilingual tasks.
- Multimodal: Actually understands images, documents, and charts — not just token strings.
- Cost-efficient: Major-league performance without the major-league price.
- Long-context savvy: Perfect for summarizing or reasoning over huge chunks of info.
- Great in tough benchmarks: Especially GPQA and DocVQA — these are seriously hard.
4. How to Use Llama 4
There are many ways in which Llama 4 can be used out because it is completely open-sourced.
- Download: Available on llama.com and Hugging Face.
- Meta AI Integration: Live in WhatsApp, Messenger, Instagram Direct, and Meta.AI.
- Free API keys present on OpenRouter.
Conclusion
Meta’s Llama 4 is here — Scout, Maverick, and the massive Behemoth — all open-source, efficient, and beating rivals like Gemini 2.0 and DeepSeek v3. With mega-context, multimodal smarts, and cutting costs, it’s a game-changer for coders, creators, and researchers. Grab it free on Hugging Face or via Meta’s apps, and dive into smarter, faster AI. The future’s open — go build something awesome!
Meta Llama4 released: Beats DeepSeek v3, Gemini 2.0 was originally published in Data Science in Your Pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.
