llama-4-maverick-17b-128e-instruct

Introduction

Llama 4 Maverick is Meta’s flagship open-weight large language model, released in April 2025 under the Llama 4 Community License. It is a mixture-of-experts model with 400 billion total parameters (17 billion active per forward pass across 128 experts), multimodal text-and-image input, and a very large context window of roughly 1 million tokens. Unlike a packaged legal product, Maverick is a general-purpose foundation model that legal teams and vendors use directly via API or self-hosting, or as the engine behind third-party legal tools, rather than as a finished legal assistant.

Legal Research

Maverick’s huge context window lets it ingest entire case files or research archives without chunking, which is useful for summarizing and cross-referencing large volumes of text. However, it has no native connection to legal databases like Westlaw or Lexis, no built-in citation verification, and general benchmarks place it behind top closed-source models on reasoning-heavy tasks, so raw legal research accuracy and citation reliability must be validated independently or paired with a retrieval layer.

Contract Analysis

With the right prompting or a purpose-built pipeline, Maverick can extract clauses, flag risks, and summarize contract terms, and its long context window allows reviewing lengthy agreements in a single pass. It does not include contract-specific tooling out of the box, so firms typically need custom engineering or a third-party wrapper product to get a usable contract-review workflow.

Document Drafting

Maverick can draft contracts, memos, and correspondence from prompts or templates, and its performance on general writing and instruction-following benchmarks is competitive with other current-generation models, though it trails top proprietary models on demanding coding and multi-step reasoning benchmarks such as SWE-bench. There is no legal-specific drafting interface, template library, or Word plugin bundled with the model itself.

Legal Translation

As a multilingual model, Maverick can translate and summarize legal text across many languages, which can help with reviewing foreign-language documents. It is a general translator rather than a legal-terminology-tuned one, so jurisdiction-specific terms and nuance should be checked by a qualified translator or bilingual attorney before relying on its output.

Predictive Analytics

Maverick has no built-in litigation-outcome or judge-analytics capability. Any predictive use would require a custom pipeline built on top of the model plus proprietary case-outcome data, which the base model does not provide.

Pricing

Llama 4 Maverick is open-weight and free to self-host under the Llama 4 Community License (subject to Meta’s usage terms for very large companies). Most legal teams instead access it through managed API providers, where 2026 pricing runs roughly $0.15–$0.20 per million input tokens and $0.60–$0.70 per million output tokens depending on the host — substantially cheaper per token than most closed frontier models, but firms must still budget separately for any legal-specific tooling, retrieval infrastructure, or hosting built around it.

User Reviews and Ratings

As a developer-facing foundation model rather than a legal SaaS product, Maverick does not have law-firm-oriented review pages on sites like G2 or Capterra. Independent model benchmarks in 2026 place it in the middle of the current model pack — matching top proprietary models like GPT-5-class systems on some knowledge benchmarks (MMLU) while trailing them on complex coding and reasoning tasks — and developer commentary consistently highlights its price-to-context-length ratio as its strongest selling point.

Verdict

Llama 4 Maverick is best understood as infrastructure rather than a finished legal product: an inexpensive, very-long-context, open-weight model that legal-tech vendors and technically capable in-house teams can build on. It is a poor fit for a firm wanting a ready-to-use legal assistant with citation verification, legal databases, and a polished interface — for that, a purpose-built legal AI product (open-weight-model-based or not) will generally serve better out of the box.