GPT-5.5 vs Claude Sonnet 4.6, priced honestly
Two frontier models from two vendors, compared on the only dimension we can verify: what they cost to run at published rates on identical workloads.
Published · 6 min read
We are not going to tell you which of these two models is smarter. We have not run a benchmark either of them would agree with, and neither has most of the internet. What we can do is put their published prices side by side and run identical workloads through both, which is the part of the decision that is actually verifiable.
For the vendor-wide view see OpenAI and Anthropic pricing compared. For the full set of cost levers, start at the cost reduction guide.
The numbers, side by side
| Metric | GPT-5.5 | Claude Sonnet 4.6 |
|---|---|---|
| List input (per 1M) | $5.00 | $3.00 |
| List output (per 1M) | $30.00 | $15.00 |
| corouter input (per 1M) | $1.00 | $0.600 |
| corouter output (per 1M) | $6.00 | $3.00 |
| Context window | 1.1M | 1M |
| List cached input (per 1M) | $0.500 | $0.300 |
Same workload, both models
Prices per million tokens are hard to feel. Here is the same monthly volume — 50M input and 5M output tokens — priced through each model at list and at outlet rates.
| Model | At list price | At corouter price | Difference |
|---|---|---|---|
| GPT-5.5 | $400.00 | $80.00 | $320.00 |
| Claude Sonnet 4.6 | $225.00 | $45.00 | $180.00 |
Change the mix and the ordering can change with it. A generation-heavy workload shifts weight onto the output rate; a retrieval-heavy one shifts it onto input. Put your own volumes into the pricing calculator rather than trusting a ranking built on someone else's traffic.
Where they genuinely differ
Context
Both models offer roughly a million tokens of context. That is enough that context size is unlikely to be your deciding factor between these two specifically — which is a real change from a couple of model generations ago, when window size alone often forced the choice.
Cached input
Both publish a cached-input rate, and on both it lands at roughly a tenth of the base input rate. If your application has a large fixed system prompt, model that discount into the comparison before you decide; on prefix-heavy workloads it moves the total more than the base-rate gap does. How caching is billed covers the details.
Everything else
Quality, tool-calling behavior, refusal patterns, and latency all differ, and all of them are workload specific. The honest recommendation is to run both against fifty of your own real inputs and read the outputs yourself. That evaluation costs a few dollars and beats every published leaderboard for your particular use case.
Running the evaluation cheaply
A side-by-side evaluation is exactly the kind of one-off spend that makes prepaid metered billing convenient: no subscription to start, no commitment to keep. Both models are in the catalog at outlet rates — GPT-5.5 and Claude Sonnet 4.6 — with the same model IDs and familiar JSON shapes you are already sending.
Keep reading
- How to reduce your LLM API costsMost advice about cutting inference spend is vague. This is the concrete version: where the money goes, which five levers move it, and how much each one is worth on a real monthly volume.
- OpenAI vs Anthropic API pricingBoth vendors publish per-million-token rates, but they price context, output, and caching differently enough that the cheaper choice depends entirely on your workload shape.
- How to estimate your monthly LLM API costForecasting an inference bill is arithmetic, not prophecy. Here is the method: four numbers to log, one formula, and the mistakes that make estimates come in low.
Price the same models at outlet rates
Same model IDs and familiar JSON shapes, metered per token, with a $5 minimum top-up and no subscription.
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