Llama 4 vs GPT-5.4 — AI Model Comparison

Meta's open-weight contender vs OpenAI's commercial flagship

Llama 4 Maverick vs GPT-5.4: Open Weight vs Closed API

This comparison isn’t just about model quality – it’s about two fundamentally different approaches to deploying AI. GPT-5.4 is a managed API: you pay per token and OpenAI handles everything. Llama 4 Maverick is open-weight: you get the model files and run them however you want.

The Quality Gap

Let’s be upfront: GPT-5.4 is the better model on benchmarks. It leads by 4.7 points on MMLU (93.1 vs 88.4), 7.1 points on HumanEval (92.8 vs 85.7), and 13.9 points on GPQA (76.2 vs 62.3). That last number is the biggest gap – GPT-5.4’s reasoning capabilities are substantially stronger.

For high-stakes tasks where quality is paramount, GPT-5.4 is the safer bet. But Llama 4 isn’t trying to compete on raw benchmarks alone. Its value proposition is different.

Context Window Advantage

Llama 4 Maverick offers 1M tokens of context – nearly four times GPT-5.4’s 256K. If you’re processing long documents, codebases, or extensive conversation histories, Llama 4’s context window is a real advantage. Combined with self-hosting, you can process sensitive documents without sending them to a third-party API.

The Economics

GPT-5.4 costs $10/$30 per million tokens. Llama 4 Maverick’s weights are free, but you need GPU infrastructure to run it.

At low volumes (say, a few thousand requests per day), an API is almost always cheaper. You’re not paying for idle GPUs, and you don’t need an ML ops team. At high volumes (millions of requests per month), self-hosting Llama 4 can be dramatically cheaper – potentially 5-10x less than API pricing, depending on your infrastructure setup.

There’s also a middle ground: several cloud providers offer hosted Llama 4 endpoints at prices between self-hosting and GPT-5.4’s API. These give you Llama 4’s capabilities without managing your own GPUs.

Data Privacy

This is Llama 4’s trump card. When you self-host, your data never leaves your infrastructure. For healthcare, finance, legal, and government use cases with strict data residency requirements, self-hosting an open-weight model isn’t just a nice-to-have – it’s often a requirement.

When to Choose Each

Choose GPT-5.4 when: You need the highest quality output, especially for reasoning and code generation. You don’t want to manage infrastructure. Your data doesn’t have strict residency requirements.

Choose Llama 4 Maverick when: You need data privacy and full control. You’re running at high enough volume that self-hosting saves money. You need 1M tokens of context. Or you’re building a product that can’t depend on a third-party API’s availability and pricing.

Frequently Asked Questions

Is Llama 4 really free?

The model weights are free to download and use under Meta's license. But you'll pay for compute -- GPU instances, hosting, and infrastructure. At low volumes, self-hosting Llama 4 can cost more than using GPT-5.4's API. At high volumes, it can be much cheaper.

How does Llama 4 compare to GPT-5.4 on quality?

GPT-5.4 leads on all three benchmarks: MMLU (93.1 vs 88.4), HumanEval (92.8 vs 85.7), and GPQA (76.2 vs 62.3). The gap is significant, especially on reasoning tasks.

Should I self-host Llama 4 or use an API?

It depends on volume, privacy requirements, and your team's infrastructure skills. Self-hosting gives you full data control and can be cheaper at scale, but requires managing GPU servers and model updates.

Does Llama 4 have a bigger context window?

Yes. Llama 4 Maverick offers 1M tokens of context versus GPT-5.4's 256K. That's a major advantage for long-document workflows.