Meta Llama 3

Overview

Meta Llama is Meta’s family of open-weight foundation models for text and multimodal work. The VersusTools slug llama-3 covers the whole shipped line people still call “Llama 3.x / Llama 4”—not a single 2024 checkpoint. Meta released the first Llama 3 8B and 70B models in April 2024; the herd then expanded through Llama 3.1 (including 405B), Llama 3.2 (vision + edge sizes), Llama 3.3 70B, and the April 2025 Llama 4 MoE models Scout and Maverick. Weights ship from Meta’s Hugging Face org and partner catalogs; consumer chat runs through Meta AI; developers integrate via self-host stacks, cloud marketplaces, and third-party inference APIs.

In mid-2026 the practical picture is: Llama 3.3 70B and quantized Llama 3.1 8B remain local and serving workhorses; Llama 4 Scout/Maverick are the current open multimodal flagships (17B active parameters, MoE); Llama 4 Behemoth was previewed as a ~2T-parameter teacher and has not been a generally available open-weight download like Scout/Maverick. Meta’s developer hub is at developer.meta.com/ai, with model cards, licenses, and Llama Protections (Llama Guard, Prompt Guard, etc.).

Key features

• Llama 4 Scout (April 2025) — Natively multimodal MoE: ~17B active / ~109B total parameters, 16 experts. Meta positions it as fitting a single NVIDIA H100 with Int4 quantization and claims an industry-leading 10M-token context window (pre/post-trained with long-context recipes; many hosts still expose smaller practical windows).
• Llama 4 Maverick (April 2025) — Multimodal MoE workhorse: ~17B active / ~400B total, 128 routed experts + shared expert. Meta reports strong results vs GPT-4o / Gemini 2.0 Flash class models on its published suites and ~1M context. Designed to run on a single H100 DGX host or with distributed inference.
• Mixture-of-experts architecture (Llama 4) — Alternating dense and MoE layers; only a subset of experts activate per token, so total parameter count is high while active FLOPs stay closer to a 17B dense model. First Llama generation built this way.
• Early-fusion multimodality — Llama 4 jointly handles text and vision tokens in one backbone (image + multi-image inputs; video-frame stills in training). Not a bolted-on vision adapter like some earlier open stacks.
• Llama 3.x dense herd (still widely used)
  • 3.1 (July 2024): 8B / 70B / 405B, 128K context, stronger multilingual + tool-use story; 405B was the first “frontier-scale” open Llama.
  • 3.2 (Connect 2024): first official vision Llama sizes plus lightweight 1B/3B text models for edge/mobile.
  • 3.3 (December 2024): text-only 70B Instruct aiming at 3.1 405B-class quality at far lower serve cost—still a default on Groq and local GPUs.
• Open weights under Meta’s Community License — Free to download, fine-tune, and use commercially for most organizations, subject to the Acceptable Use Policy and the longstanding 700 million monthly active users commercial term (very large apps must request a separate Meta license).
• Ecosystem depth — Hugging Face transformers, vLLM, TensorRT-LLM, llama.cpp/GGUF quants, Ollama, cloud partners (AWS, Azure, GCP, Groq, Fireworks, Together, Databricks, IBM watsonx, NVIDIA NIM, and many others listed on Meta’s Llama 4 launch partners). Llama Guard / Prompt Guard / CyberSecEval for safety tooling.
• Meta AI product surface — Consumer assistant on WhatsApp, Messenger, Instagram, Facebook, and meta.ai, powered by current Llama generations (including Llama 4 after launch). Separate from self-host weights.
• Multilingual pretraining — Llama 4 trained across a large language set (Meta cites 200 languages in pretraining materials, with heavy token budgets for 100+ languages). 3.1/3.3 instruct cards commonly list core languages such as English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.

Pricing

Meta does not charge for official open weights. Cost is (1) your GPUs or (2) a host’s per-token / dedicated-instance bill. Consumer Meta AI chat is free with product rate limits. Approximate mid-2026 hosted rates (confirm on each provider—list prices move):

Access path	What you pay	Notes
Official weights (HF / Meta download)	$0 model fee	Community License + AUP; accept terms; HF gated access is common
Meta AI (consumer)	Free	Product UX, not a stable production API contract
Llama 4 Maverick (multi-host range)	About $0.20–$0.35 / 1M input; ~$0.60–$1.15 / 1M output	Aggregators (e.g. pricepertoken, July 2026) show Vercel ~$0.20/$0.60; DeepInfra/OpenRouter nearby; Google AI Studio higher in some listings
Groq — Llama 3.1 8B Instant	$0.05 in / $0.08 out per 1M	High tokens/sec; great for volume routing
Groq — Llama 3.3 70B Versatile	$0.59 in / $0.79 out per 1M	Still a popular low-latency 70B path
Together / Fireworks / Bedrock / Azure / etc.	Varies by model ID and tier	Scout/Maverick/3.x each have separate SKUs; check context caps per host
Self-host GPU	Hardware + power + ops	8B/3.2 edge fit consumer GPUs; 70B wants 40–80GB-class VRAM (or heavy quant); Maverick needs multi-GPU or specialized hosts; Scout Int4 aims at single H100

• Pricing model on VersusTools: open weights / free downloads → treat as open_source for catalog purposes even though the legal license is Meta’s custom Community License, not Apache/MIT.
• Starting price: $0 for weights and Meta AI; production API spend starts when you pick a host.
• Enterprise custom license: required path if you cross the 700M MAU commercial threshold (or need negotiated terms Meta does not publish publicly).

Limits & gotchas

• Not OSI “open source.” Community License + Acceptable Use Policy + 700M MAU clause. Legal and procurement teams often treat Llama as source-available / open weights, not Apache 2.0. Compare carefully to Gemma, Mistral Apache/MIT drops, or DeepSeek MIT weights.
• Hosted context ≠ model card context. Scout’s 10M window is a headline Meta capability; Groq/Fireworks/Together rolled out with much smaller practical limits at launch, and many APIs still cap far below 10M. Always read the host model page.
• MoE serving complexity. Maverick’s 400B total footprint needs expert-aware runtimes, enough GPU memory for all experts, and careful quantization. “17B active” does not mean “runs like a 17B dense GGUF on a laptop.”
• Coding / agent quality is mixed in community benches. Meta’s blog claims strong multimodal and chat results; independent coding suites (e.g. DevQualityEval threads on r/LocalLLaMA) often place Maverick mid-pack versus smaller specialized code models. Measure on your tasks.
• Behemoth is not a drop-in open model. Previewed as teacher (~288B active / ~2T total). Do not plan production on Behemoth weights unless Meta has published them for your use case—Scout/Maverick are the downloadable Llama 4 pair.
• HF gating & license acceptance. First-time downloads usually require accepting Meta’s terms on Hugging Face; automated CI needs a token and org approval.
• Knowledge cutoffs and post-training tradeoffs. Community notes (e.g. HN Llama 4 threads) flag knowledge cutoffs around late 2024 for early Llama 4 cards and debate safety/refusal changes versus Llama 3.3. Don’t assume “newest Llama” wins every safety or factuality eval.
• Safety is system-level. Meta ships Llama Guard and Prompt Guard as separate models; product liability still sits with the deployer. AUP forbids many high-risk categories (disallowed content, certain weapons/crime assists, etc.).
• Fine-tunes inherit license constraints. Derivatives typically remain under Llama Community License obligations—including the MAU clause and AUP—unless Meta grants other terms.

Community sentiment

On r/LocalLLaMA and Hacker News, Llama 3.1/3.3 cemented Meta’s reputation: 8B for edge, 70B for “good enough everywhere,” 405B as the open frontier moment of 2024, and 3.3 70B as the cost-efficient workhorse that kept serving bills down. Ollama + GGUF culture still centers these dense models for laptops and single-GPU boxes.

Llama 4’s April 2025 launch drew huge attention for MoE multimodality and the 10M Scout context claim. Praise focuses on inference efficiency of 17B-active designs, partner day-one availability (Groq, Fireworks, Together, OpenRouter, etc.), and Meta AI product integration. Criticism is equally loud: some coding/writing benches underwhelmed relative to hype; RoPE/conversion issues hit llama.cpp early; “10M context” rarely appeared fully on day-one APIs; and Behemoth’s delayed open release frustrated people waiting for a true 2T open teacher. Threads titled along the lines of “Llama 4 smells bad” coexist with users who call Scout a sweet speed/quality tradeoff for chat and vision.

Treat Llama as a family: pin exact model IDs, re-bench after every host upgrade, and don’t confuse Meta’s research claims with the SKU your API actually serves.

Who should use it

• Teams that need open weights for on-prem, VPC, air-gapped, or data-residency constraints while staying near frontier quality.
• Startups and products under 700M MAU that want free commercial weights and a huge hosting ecosystem.
• Local-first developers running 8B–70B (or quantized Scout) via Ollama, LM Studio, llama.cpp, or vLLM on owned hardware.
• Multimodal app builders who want a single open backbone for text + images (Llama 4 Scout/Maverick) without a closed vision API.
• Platforms building fine-tunes, RAG, or domain adapters on top of widely supported base checkpoints (especially 3.1/3.3 70B and Llama 4 instruct variants).
• Latency-sensitive backends routing high QPS through Groq/Fireworks-class Llama 3.1 8B or optimized Scout endpoints.

Prefer closed frontier APIs (Claude, ChatGPT/OpenAI, Gemini) when you need strongest agentic coding, vendor SLAs, or product surfaces Meta does not match. Prefer Apache/MIT open models (Gemma, some Mistral / DeepSeek lines) when legal wants a standard OSI license without a 700M MAU negotiation risk.

Alternatives

• DeepSeek — Aggressive open-weight + API pricing; strong coding/agent value; MIT on recent weight drops.
• Qwen — Alibaba’s open herd; often wins multilingual and coder leaderboards at similar sizes.
• Mistral — European lab with open weights + EU-hosted cloud; different compliance story.
• Gemma 4 / Google Gemma 4 — Google open models; Apache-style licensing appeal for many enterprises.
• Claude — Best-in-class careful coding/writing agents when budget allows closed models.
• ChatGPT — Consumer + API ecosystem depth; not open weights.
• Gemini — Google’s long-context multimodal stack and Vertex path.
• Ollama — Local runner for Llama and peers; not a model family itself.

Verdict

In 2026, “Llama 3” on a tool directory should mean Meta’s open-weight Llama platform: a dense 3.x herd that still powers most local and low-cost serving, plus Llama 4 Scout/Maverick as the current multimodal MoE generation. Strengths are free weights, enormous ecosystem coverage, strong efficiency on 17B-active Llama 4 designs, and a clear path from laptop demos to hyperscaler APIs. Weaknesses are the custom license (700M MAU + AUP), gap between paper context lengths and host limits, uneven coding-agent results versus specialized open and closed rivals, and the unfinished open story around Behemoth-scale teachers. For most builders who can accept Meta’s license and will pin model IDs carefully, Llama remains a default open backbone—not the only one, but still one of the most operationally complete.