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Llama 4 logo

Llama 4

Llama 4 Scout and Maverick remain Meta's last open-weight frontier models (April 2025) with up to 10M-token context. Meta paused the open Llama line in 2026 in favor of a new proprietary flagship.

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The verdict on Llama 4: Developers and companies who want to self-host a capable model instead of calling a closed API Llama 4 is the default open-weight choice for developers and companies that want to self-host a capable frontier-class model rather than call a closed API, and its massive community means integrations, fine-tuning guides, and third-party tooling are plentiful. Pricing: Free and open-weight, no Llama 5 has shipped.

Best For

Developers and companies who want to self-host a capable model instead of calling a closed API

Standout Feature

Open weights with a long context window and multimodal input, competitive with closed frontier models on most benchmarks

TL;DR

The default open-weight choice until something newer ships, but running the larger variants requires real hardware.

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Fit Score: use-case reference

How this is scored

Llama 4 scored 1 to 5 on five dimensions for the named use cases below. Part of our catalog-wide use-case reference, not a product-quality review rating.

Self-hosting a capable model instead of calling a closed API

4.0/5
Capability
5/5
Reliability
4/5
Value
5/5
Ease
2/5
Compatibility
4/5

Llama 4's bestFor and standout both describe exactly this use case: an open-weight model competitive with closed frontier models for teams that want to self-host. Ease is marked down because cons and ourTake both flag that larger variants 'require serious compute infrastructure.'

Commercial deployment at large scale without a dedicated legal or compute review

2.4/5
Capability
3/5
Reliability
2/5
Value
2/5
Ease
2/5
Compatibility
3/5

OurTake and cons both flag that 'the license carries restrictions at commercial scale that are worth reading before you build on it,' a real friction point for this use case that the self-hosting developer use case doesn't carry.

Editorial judgment grounded in this tool's own published pricing, features, and our existing pros and cons, not a certification or a measured benchmark. Product quality is assessed separately under our review methodology.

Overview

Not yet reviewedProduct quality is assessed from original evidence. How reviews work.

Llama 4 is Meta's latest family of open-weight large language models, representing a highly capable open-source AI models available as of their release, with long context windows, multimodal input capability, and performance competitive with closed frontier models on most standard benchmarks. The open-weight licensing that has defined the Llama series continues: model weights are freely downloadable and deployable on your own hardware or cloud infrastructure, enabling use cases that closed API models cannot support, full data privacy, unlimited inference volume at fixed cost, fine-tuning on proprietary datasets, and integration into air-gapped environments. Llama 4's multimodal variants process both text and image inputs, enabling vision-language applications without additional model dependencies.

The Scout variant is optimized for efficient inference at scale; the Maverick and Behemoth variants trade efficiency for capability, targeting the highest-quality reasoning and instruction-following use cases. Meta distributes Llama through Hugging Face, and the models run on a range of hardware from high-end consumer GPUs to enterprise server clusters. The ecosystem of fine-tuned Llama variants developed by the open-source community provides specialized versions for medical, legal, coding, and multilingual applications.

Llama 4's significance extends beyond the models themselves, Meta's release pattern has established open-weight models as a viable enterprise choice, reducing the industry's dependence on proprietary API providers.

The source of the legacy catalog rating and review count has not been substantiated. Neither is presented here as a verified review rating or included in review structured data.

Workflow Fit Scores are separate, use-case-specific editorial judgments, not user-review averages. Read the Fit Score methodology.

Pros
  • • Industry-standard open model
  • • Massive community support
  • • Free to use
Cons
  • • Large variants need serious hardware
  • • License restrictions at scale

Key Features

  • Open weights
  • Long context window
  • Multimodal variants
  • Huge fine-tuning ecosystem

Trust & Data

Verified 2026-09

Training on your data
Not publishedLlama 4 is an open-weight model; when self-hosted no data is transmitted to Meta. Meta's published AUP and Responsible Use Guide govern downstream developer usage but do not address whether Meta collects or trains on data from self-hosted deployments.
Compliance
Not published
Retention
Not published
Export
Not published

Sources: llama.com, ai.meta.com

As published by the vendor. Verify independently before purchase decisions.

Our Take

The multimodal input and long context window put it in a competitive range on most benchmarks. The honest constraint is hardware: the larger variants require serious compute infrastructure, and the license carries restrictions at commercial scale that are worth reading before you build on it. If your team already has the GPU capacity, this is the benchmark everything else gets compared against.

Was this useful?
Pricing

Free and open-weight, no Llama 5 has shipped

Use Cases
Models

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