GPT-5 vs Claude: the entire cost gap is on the input side

Start here: the output prices are identical

GPT-5.4 and Claude Sonnet 4.6 both bill output at $15/1M. Not close — the same number. Every dollar of difference between these two flagships therefore comes from the input side, and that single fact predicts which workloads make them expensive relative to each other.

On input, Sonnet lists at $3 against GPT-5.4’s $2.5 — a 20% sticker gap. But Claude’s tokenizer emits roughly 10% more tokens for the same English text, so the price you actually pay per unit of content is about 1.32× GPT-5.4’s. That is the whole story, and it’s why the gap moves around so much between workloads.

The gap tracks your input:output ratio

Because output is a wash, the flagship premium scales with how input-heavy your traffic is. Same three workloads as the table above, sorted by input:output ratio:

workload                in:out    Sonnet vs GPT-5.4
Simple chatbot          2.0:1     +19%
Multi-step agent        2.5:1     +21%
Chatbot with history    5.0:1     +28%

Monotonic, and not a coincidence. Push further in either direction and the pattern holds: a long-form generation workload (say 500 in / 2,000 out — an inverted ratio) narrows the premium to roughly 11%, while anything that stuffs context — RAG, long transcripts, document analysis — widens it. If you are choosing between these two, the first question isn’t “which is cheaper,” it’s how much input do I send per unit of output.

The bigger lever: tier, not vendor

Here is the uncomfortable framing for a page like this one. On the history-carrying chatbot above, GPT-5.4 runs $490/mo and Sonnet $628/mo — a 28% spread. Claude Haiku 4.5, Anthropic’s budget tier, runs the same workload for $209/mo. That is ~57% below GPT-5.4, the other vendor’s flagship.

In other words: moving down a tier saves you multiples of what switching vendor at the same tier does. If cost is the binding constraint, “GPT-5 or Claude?” is the second question. The first is whether your task actually needs a frontier model at all. The flagship-vs-flagship decision matters when quality is the constraint — which is a real and common situation, just not a cost question.

Where each one earns its price

• GPT-5.4 — cheaper on input, larger standard context. 272K standard against Sonnet’s 200K, and no cache-write premium, which matters on bursty traffic where you keep paying to warm a cache you don’t reuse.
• Sonnet 4.6 — best-in-class cache reads and a 1M option. Cached input at $0.3/1M is the strongest caching economics in our catalog, so a high, steady hit rate erodes much of the input premium. And 1M context is available at standard pricing — above 272K it stops being a price comparison and becomes an architecture one.

Note the tension in those two bullets: Anthropic charges a $3.75/1M cache write where OpenAI charges nothing, but pays it back with a deeper read discount. Which side of that trade you land on depends entirely on your hit rate — so model it rather than guessing. The caching deep-dive works through the break-even.