Most teams overthink the flagship question and underthink this one. The workhorse tier is where your bulk traffic lives, and picking well here moves your bill far more than the flagship choice does.
Is there a real price difference?
Barely. Sonnet 4.6 charges $3/Mtok in and $15/Mtok out. GPT-5.4 charges $2.50 in and the same $15 out. On 1M input plus 200K output per day, that's $180/month for Sonnet and $165/month for GPT-5.4. A $15/month difference is noise; do not let it decide anything.
Context windows are equally close: 1M tokens for Sonnet, 1.05M for GPT-5.4. Both fit a whole repository plus documentation in a single prompt.
Where does each model win?
Sonnet 4.6 is, in our experience, the better iterative coder. It's the model we reach for first for day-to-day development work: implement this function, fix this test, refactor this module. It inherits the agentic reliability that makes Claude models good inside editor tooling and CI loops.
GPT-5.4's concrete advantage is output capacity: up to 128K tokens in a single response against Sonnet's 64K. If your workload generates large single artifacts, a full report, a big migration file, a complete document in one shot, that headroom is real. GPT-5.4 also reads PDFs natively, as does Sonnet; both take images; neither handles audio or video.
Does the output limit matter in practice?
For most API usage, no. Iterative work naturally produces responses in the hundreds-to-thousands of tokens, nowhere near either cap. It starts to matter in two cases: single-shot generation of very large outputs, and agent designs that dump big tool results back out verbatim. If that's you, GPT-5.4's 128K ceiling removes a class of truncation bugs that you'd otherwise engineer around.
How should you decide?
Pick Claude Sonnet 4.6 if:
- Iterative coding is the core workload
- You're building agents that make many small tool calls
- Your team already runs Claude elsewhere and wants one vendor
Pick GPT-5.4 if:
- You generate very large single responses
- Your input skews slightly heavier (the $0.50/Mtok input saving compounds)
- Your tooling is OpenAI-compatible already
Whichever you choose, set up prompt caching first. At this tier, cached input costs drop enough that cache hit rate influences your bill more than the list-price difference between these two models.