March 10, 2026Model ReleaseSource: Meta AI Blog

Meta Releases Llama 4 Maverick with 400B Parameters

Meta has released Llama 4 Maverick, a 400B parameter open-weight model that matches GPT-5 on multiple benchmarks. The model is available under a permissive license for commercial use.

Meta has released Llama 4 Maverick, the flagship model in its fourth-generation Llama family, featuring 400 billion parameters and performance that rivals the best closed-source models. Maverick achieves 92.1% on MMLU and 91.8% on HumanEval, placing it within striking distance of GPT-5.2 on most evaluations.

The model uses a mixture-of-experts architecture that activates only 52B parameters per forward pass, making it significantly more efficient to serve than its total parameter count suggests. Meta reports that Maverick runs at 45 tokens per second on a single 8xH100 node, making it practical for production deployment.

Llama 4 Maverick introduces native multimodal capabilities, processing text, images, and audio in a single architecture. The vision capabilities are particularly strong, outperforming GPT-4V on document understanding tasks and matching Gemini 3 on visual reasoning benchmarks.

Meta is releasing Maverick under an updated Llama license that removes the previous user-count restriction, making it fully available for commercial use regardless of company size. The company is also releasing smaller variants at 70B and 8B parameters.

The release continues Meta's strategy of using open-source AI to commoditize the model layer and drive adoption of its broader AI ecosystem. CEO Mark Zuckerberg noted that Llama models now power over 100,000 commercial applications worldwide.

More News

March 13, 2026Product Update

NVIDIA Launches NIM Microservices for Enterprise AI Deployment

NVIDIA has launched NIM (NVIDIA Inference Microservices), a suite of containerized AI model serving packages that reduce enterprise AI deployment time from weeks to hours with optimized inference performance.

March 13, 2026Industry

AI Agents Market Reaches $15 Billion as Enterprise Adoption Surges

The global market for AI agents — autonomous AI systems that can plan, execute, and iterate on complex multi-step tasks — has reached $15 billion in annual spending, according to a new report from McKinsey. This represents a 200% increase from 2025, driven by enterprise adoption of agentic AI for customer service, software development, data analysis, and business process automation. The report identifies three tiers of AI agent adoption: basic agents that handle single-step tasks like email responses and appointment scheduling (adopted by 65% of enterprises), intermediate agents that manage multi-step workflows like report generation and data pipeline management (35% adoption), and advanced agents that autonomously execute complex processes like code deployment and financial analysis (8% adoption). The largest spending categories are customer service agents ($4.2B), coding agents ($3.8B), and data analysis agents ($2.5B). McKinsey projects the market will reach $45 billion by 2028 as agent reliability improves and enterprises become more comfortable delegating complex decisions to AI. Key enabling platforms include OpenAI's Agents SDK, Anthropic's Claude computer-use capabilities, and LangChain's agent framework. The report warns that agent governance and monitoring remain underdeveloped, with most enterprises lacking adequate oversight mechanisms for autonomous AI actions.

March 12, 2026Product Update

Microsoft 365 Copilot Gets Custom AI Agents and Actions

Microsoft has updated 365 Copilot with custom AI agent creation, allowing organizations to build agents that automate complex workflows spanning Word, Excel, Outlook, Teams, and SharePoint without code.

March 12, 2026Analysis

GPT-5.2's Agentic Mode Transforms Enterprise Workflows

OpenAI's GPT-5.2 introduced a fundamentally new approach to agentic task completion that is already transforming enterprise workflows. The model can now maintain coherent plans across 50+ sequential tool calls with parallel execution, reducing latency in complex automation pipelines by up to 60%. Early enterprise adopters report that GPT-5.2's agentic mode handles tasks like multi-step data analysis, cross-platform content publishing, and automated code review workflows that previously required custom orchestration code. The key innovation is what OpenAI calls deliberative alignment — a training approach that lets the model dynamically allocate compute to harder sub-tasks while breezing through simpler ones. This means a single agentic session can handle both quick lookups and deep reasoning without manual configuration. Several Fortune 500 companies have reported 40-70% time savings on analyst workflows by deploying GPT-5.2 agents through the API. However, reliability remains a concern — OpenAI acknowledges a 3-5% failure rate on chains exceeding 30 steps, and enterprise deployments require human-in-the-loop checkpoints for critical decisions.