Migrating GPT-5.5 to GPT-5.6: The Same-Price Swap Is the Wrong Default

GPT-5.5 was one model at $5 input / $30 output per million tokens. GPT-5.6 arrived as three:

Sol matches GPT-5.5's price exactly, so it reads as the obvious swap: same tier, same cost, newer model. Change the string, ship it.

We measured all four in a single session. Sol is a defensible choice, but as a default it's the wrong one — and the reason is latency, which no pricing page shows.

Nothing broke

The JSON task is the one that matters most for a migration. Output-shape drift is what actually breaks pipelines — a downstream parser doesn't care whether the model got smarter, it cares whether the response still matches the schema. All four produced the exact key set with correct values, including inferring seats: 12 from "about a dozen".

On this battery, migrating from GPT-5.5 to any GPT-5.6 tier is behaviourally safe.

What actually changes

Three things worth pulling out.

Sol is cheaper than GPT-5.5 despite identical list pricing. Same rates, 25% lower bill, because Sol emitted 556 output tokens against 5.5's 757. You get a discount for switching even at the same price tier — just not the one on the pricing page.

Sol is also the slowest model in the table, and slower than the model it replaces. If you migrate on price parity alone, you ship a 28% latency regression to whatever GPT-5.5 was serving.

Terra is the actual drop-in. It cost 2.7× less than GPT-5.5, ran 42% faster, and passed every task including the schema check. On this workload there is no measurement that favours Sol over Terra.

Our recommendation

Default to Terra. Cheaper and faster than the model you're leaving, with no behavioural difference we could detect. If you change one string, change it to gpt-5.6-terra.

Consider Luna for high-volume work. 4.6× cheaper than GPT-5.5 at the same speed as Terra. It's wordier — 830 output tokens against Terra's 557 — so its 5× list discount doesn't fully materialise, but it's still the cheapest option here. Note it's weaker on hard output constraints (7/10 versus Gemini 3.6 Flash's 10/10), so validate if you depend on strict formats.

Reach for Sol deliberately, not by default. It's the most capable tier and our battery doesn't reward that. If your workload is genuinely frontier-difficulty, Sol is the right choice — but then you should be choosing it on evidence from your prompts, not on price parity with a retired model.

Migration checklist

The API shape is unchanged between 5.5 and 5.6, so this is mostly a string swap. Things to check anyway:

1. Reasoning-model conventions still apply. The gpt-5* family uses max_completion_tokens rather than max_tokens, reasoning_effort in place of temperature, and the developer role rather than system. If you were already on 5.5 you have this; if you're coming from 4.x you don't.
2. Raise max_tokens. It covers reasoning tokens plus the answer. Too low and you get an empty response with a full bill.
3. Re-baseline your cost model on measured tokens. All four models here have different verbosity; the rate card won't tell you the bill.
4. Re-run your own output-shape assertions. Ours held across all four models, but ours is one schema on one input.
5. Don't hardcode a version regex. gpt-5.6-sol doesn't match patterns written for gpt-5.5, and that class of check fails silently.

Try it yourself

• The JSON-shape task — the one that predicts pipeline breakage

To run your own prompts across all four models and compare cost, latency and output shape, that's Promptster.

Run 2026-08-09 via the Promptster API, max_tokens: 4000, four tasks, one run per model. All four models measured in the same session, so latency comparisons share network conditions. Costs computed from list pricing against actual token usage. Single-run figures: we've measured 26% run-to-run cost variance on repeated identical batteries, so treat gaps narrower than that as unresolved — the Terra-vs-Sol and Terra-vs-5.5 gaps here are well outside it, the Terra-vs-Luna latency difference is not.