comparison

GPT-5.4 nano vs Llama 4 Maverick

Token pricing, context window and real monthly cost, side by side. Llama 4 Maverick is the cheaper of the two for a typical workload — about 1.8× less.

cheaper for a typical workload
Llama 4 Maverick
saves 43% vs GPT-5.4 nano at 1,500 in / 500 out × 200,000/mo
GPT-5.4 nano $185/mo
Llama 4 Maverick $105/mo
GPT-5.4 nano versus Llama 4 Maverick specifications and price.
metric GPT-5.4 nano Llama 4 Maverick
Input / 1M $0.20 $0.15
Output / 1M $1.25 $0.60
Context 400K 1.0M
Cost @ typical workload $185/mo $105/mo
Modality text + image text + image
Price source list routed
Provider OpenAI Meta

Snapshot . Cost uses a typical workload; tune it in the calculator. How we measure →

Which should you pick?

On a typical workload, Llama 4 Maverick costs $105/mo against GPT-5.4 nano's $185/mo — roughly 1.8× cheaper. But the ranking depends on your output-to-input ratio: output is the pricier direction for both, so an output-heavy job (code generation, long answers) widens the gap while an input-heavy one (summarization, retrieval) narrows it. If you need to fit more in a single prompt, Llama 4 Maverick has the larger 1.0M-token window (~1,573 pages).

These are list and routed market prices, not measured outcomes. Two models at the same rate can still cost different amounts to finish the same task, because verbose or reasoning-heavy models emit more tokens. That gap is exactly what measured cost-per-task captures.

Frequently asked questions

Is GPT-5.4 nano or Llama 4 Maverick cheaper?

For a typical workload (1,500 input + 500 output tokens × 200,000 requests/month), Llama 4 Maverick costs $105/mo versus $185/mo for GPT-5.4 nano — about 1.8× less. Because output is priced higher than input, the winner can flip if your workload writes much more or less than this; check your own numbers in the calculator.

What's the main difference between GPT-5.4 nano and Llama 4 Maverick?

On price, GPT-5.4 nano is $0.20/$1.25 per 1M (in/out) and Llama 4 Maverick is $0.15/$0.60. Llama 4 Maverick has the larger context window at 1.0M tokens.

More comparisons

Related