Meta open source Llama 4 400B: Apache 2.0 license, performance comparable to GPT-4

Core conclusion

Meta announced at the end of April 2026 that the Llama 4 400B large model will be fully open sourced under the Apache 2.0 license. This is currently the open source large model with the largest number of parameters and the most generous license.

• Parameter size: 400B (MoE architecture, active parameters are about 70B)
• Context Window: 1 million Tokens
• License Agreement: Apache 2.0 (complete commercial freedom, no additional restrictions)
• Benchmark Performance: Surpasses or equals GPT-4 in multiple dimensions (MMLU 90.5%, HumanEval 82.4%)

Key Points

• Release time: end of April 2026 -Affected objects: AI application developers, content automation teams, small and medium-sized enterprises
• Core changes: For the first time, the open source model can compete head-on with GPT-4 in terms of performance, with zero restrictions on commercial use

Background: The key transition from closed to open

The competitive landscape of AI large models will reach an important turning point in 2026. Previously, GPT-4 level models were either closed source APIs (such as OpenAI, Anthropic), or open source but with strict license restrictions (such as Llama 3's "Acceptable Use Policy"). Llama 4 400B is open source under the Apache 2.0 license, which means that anyone can freely use, modify, deploy, and commercialize it without reporting to Meta or paying.

Core parameters and architectural highlights

MoE (Mixed Expert) Architecture is the core technology of Llama 4 400B. Although the total parameters reach 400 billion, only about 70 billion parameters are activated for each inference, so the inference speed is close to the level of the 70B model, but the intelligence level reaches the 400B level. This enables inference deployment on consumer GPUs such as the 2xRTX 4090.

Benchmark test: Comprehensive benchmarking GPT-4

Data released by Meta and third-party reviews show that Llama 4 400B surpasses or equals GPT-4 in multiple dimensions:

For content production scenarios, Llama 4 400B performs well in long-form writing, code generation, data extraction and other tasks. With the support of 1M Token context, the entire book-level input can be processed at one time, which is a major benefit to the automated content production pipeline.

The business implications of Apache 2.0

Apache 2.0 is an OSI-certified permissive license, which is fundamentally different from Llama 2/3’s custom license:

This means enterprises can directly: deploy Llama 4 400B as an internal API service, fine-tune business-specific models based on it, integrate into SaaS products and charge directly.

Controversies and concerns

Open source does not come without costs. The release of Llama 4 400B also raised several issues worthy of attention:

Data copyright not disclosed: Meta did not disclose the specific source of the training data. The company has previously been sued for unauthorized use of copyrighted books to train Llama. For compliance-sensitive businesses, this requires risk assessment.

Weak Security Guardrails: The base version of the Llama 4 400B has weaker security filtering compared to the GPT-4’s RLHF guardrails. If it is used directly in user-facing products, you need to add a security layer yourself.

Geopolitical Dimension: Chinese AI companies can legally use and improve Llama 4 under the Apache 2.0 license. Some U.S. lawmakers have expressed concerns about this, believing that it will accelerate the global spread of AI technology.

Practical Advice

For content producers and AI automation practitioners, the open source of Llama 4 400B brings several practical opportunities:

1. Self-built AI writing API: Use Llama 4 400B to replace the paid API, and the cost is greatly reduced. The MoE architecture allows it to run on 2xRTX 4090.
2. Fine-tuned vertical model: With the Apache 2.0 license, it can be legally fine-tuned with industry data and then commercialized.
3. Content Analysis Breakthrough: 1M Token context means that the entire book or large batches of conversation records can be analyzed at one time.
4. Combined with n8n to build a pipeline: Connect Llama 4 400B to the automated workflow to achieve end-to-end content processing.

Tool entry

Tools that appear naturally in the text: ChatGPT, OpenAI, Claude, Anthropic, DeepSeek, n8n

Internal link guidance

• Want a cheaper alternative? Watch the tutorial: DeepSeek V4 vs Claude Code: 90% Cheaper, Same Quality
• Want to build an automated content production pipeline? Watch the tutorial: How to build an AI content automated distribution system with n8n + ChatGPT: a complete 30-minute tutorial
• Real case: Indie Developer: n8n + OpenClaw Automation Workflow Earning $5,000/mo
• Real case: He earns over 10,000 per month by relying on AI code review + specification-driven development: a practical review of a freelance developer