Comparison · Updated 2026-10-09

DeepSeek V4 Pro vs GPT-5.4 Cost: How to Compare Without Guessing

Rates move: provider rate cards change without notice, and any figure printed here would rot. This page therefore keeps the method — what actually drives the bill for DeepSeek V4 Pro and GPT-5.4 — and links to the live rates for both models. Numbers you can reproduce beat numbers you can quote.

The short answer

Budget flagship against mainstream flagship: the gap is large enough that workload shape rarely saves it.

What drives the cost

For both models the bill is the same arithmetic: input tokens multiplied by the input rate, plus output tokens multiplied by the output rate, divided by one million. Three things move the result. The output multiplier: generation is sequential compute, so output is priced above input. The context weight: long contexts are billed as input on every call, and multi-turn workloads re-send history. Cache rules: cached input is usually discounted, and the discount changes the ranking of rate cards that look close on paper.

DeepSeek V4 Pro: workload shape

Open-weight-lineage pro tier positioned on price. Its cost profile is the rate card applied to that shape; the current values live on the DeepSeek V4 Pro model page.

GPT-5.4: workload shape

Mainstream production tier positioned on breadth. Same arithmetic, different rate card — see the GPT-5.4 model page for the current values.

The comparison with live numbers

Open DeepSeek V4 Pro and GPT-5.4 for the current per-million rates, then enter the same token counts for both in the LLM cost calculator. Comparing two rate cards on one workload shape is the only comparison that means anything; comparing headline rates on paper is how budgets get surprised.

What this estimate excludes

Taxes, platform fees, cache-specific billing rules beyond the published unit rates, minimum purchases, failed requests and retries. Verify a real bill against the provider's usage records.