Migrating GPT-5.6 Sol to GPT-6: Half the Price, and 6 Sol Beats 6.1 Sol

If you are on GPT-5.6 Sol, moving to GPT-6 Sol cut our bill by 60% with no change in graded results. The surprise is the other GPT-6 Sol: GPT-6.1 Sol costs the same as 6 Sol, ran slower on all three tasks, and cost slightly more. On this battery the newer point release bought nothing.

Prices are list rates from our pricing table as of 2026-10-19.

How we measured

Three script-graded tasks: strict JSON extraction from a messy support ticket, a four-step arithmetic word problem (answer 580), and an instruction-adherence task asking for exactly three bullets of 12 words or fewer with no preamble. Five runs per task per model, max_tokens 1500, run on 2026-10-19 through our own provider handlers (the same request bodies Promptster sends in production). The battery's default temperature is 0.7, but GPT-5 and later reject a custom temperature, so our handler leaves it out for all three models here. Costs are list rate against actual token counts.

All three models passed all 15 calls. Nothing in this post is an accuracy difference. The differences are cost, latency and output shape.

The bill

The list price halves. The measured bill falls further than that, because both GPT-6 Sol models also wrote fewer tokens: 6 Sol emitted 25% fewer output tokens than 5.6 Sol for the same 15 answers. We saw the same effect in the GPT-5.5 to 5.6 move, where Sol came in cheaper than 5.5 at identical rates because it wrote less. Terseness is a discount the rate card does not show.

Per task, median cost per call:

The JSON task is exactly half on both GPT-6 models because all three emit the same 33 output tokens with zero reasoning tokens. The other two tasks are where verbosity and hidden reasoning differ.

Latency: 6.1 Sol is the slow one

Median latency per call, one machine, one session:

GPT-6 Sol is 25% faster than 5.6 Sol on the word problem and 20% faster on the bullets, and 12% slower on the JSON task. GPT-6.1 Sol is slower than both on every task, roughly twice 6 Sol's median on the word problem and the bullets.

Latency is the least reliable number here: it is wall-clock from one location, not a provider SLA. But the 6.1 gap is consistent across all three tasks and all 15 runs add up to nearly double the wall time, so we would not dismiss it.

What actually changes in the output

Passing a grader is not the same as producing the same bytes. Three differences from the run files that matter if anything downstream reads the raw text.

1. 89.50 versus 89.5. All three models return identical keys in the same order, compact, no code fences. But the refund amount is written 89.50 in 2 of 5 GPT-5.6 Sol runs, 5 of 5 GPT-6 Sol runs and 4 of 5 GPT-6.1 Sol runs; the rest write 89.5. Both are valid JSON and parse to the same number. It still breaks snapshot tests, response hashing for caches, and any string comparison on the raw body. If you diff outputs before and after a migration, parse first.

2. The working is formatted differently. On the word problem, GPT-5.6 Sol answers as a bullet list in all five runs and adds a LaTeX display block in four:

- Shelf B: \(48 \times 1.25 = 60\) boxes
...
\[
(48+40)\times 2.5 + 90\times 4
=220+360=580\text{ kg}
\]

GPT-6 Sol writes a single prose paragraph in all five runs, with no display block:

Shelf B has \(48 \times 1.25 = 60\) boxes, and shelf C has \(48 + 60 - 18 = 90\) boxes.

GPT-6.1 Sol puts one shelf per line and uses the display block in four of five. All three use inline \( \) LaTeX delimiters in every run and all three end with the requested Answer: 580 line. If you render responses as plain text, every one of them will show backslashes; if you split on list markers, only 5.6 Sol gives you a list.

3. GPT-6 Sol often skips reasoning on easy instructions. On the three-bullet task, 6 Sol reported zero reasoning tokens in 3 of 5 runs and its median output was 40 tokens, against 75 for 5.6 Sol and 62 for 6.1 Sol. That is most of why its bullets cost 70% less than 5.6 Sol's. The bullets themselves read the same:

- Ensure reproducible outputs across deployments and experiments.
- Avoid unexpected behavior changes when providers update the model.
- Control testing, compliance, and costs before upgrading.

The request shape does not change

GPT-6 uses the same request conventions as GPT-5, so if you are already on 5.6 Sol this is a model-string change. Coming from older models, the rules are:

• max_completion_tokens, not max_tokens. It caps reasoning tokens plus the answer. Set it too low and you get an empty response with a full bill.
• reasoning_effort instead of temperature. A custom temperature is rejected. Our handler sends reasoning_effort: "medium" and no temperature.
• System prompts go in the developer role, not system.
• Match the family, not a version. Our handler treats anything matching ^gpt-[5-9] this way. A check written as a gpt-5 prefix lets GPT-6 through with the old parameters, and that class of bug fails silently until the first 400.

What we would do

Migrate to gpt-6-sol. It matched 5.6 Sol on every graded task, cost 60% less, and was faster on two of three tasks.

Do not pick 6.1 Sol by default because it is newer. Same price as 6 Sol, 11% more on this battery, and slower on every task. If your own evaluation shows 6.1 Sol doing something 6 Sol cannot, that is a reason; the version number is not.

Parse before you diff. The 89.50 / 89.5 split and the change in working layout are the kind of drift that turns a green migration red in a test suite while changing nothing a user would notice.

Re-baseline on measured tokens. The rate card says 50% off. Our bill said 60%. Yours will say something else, and only your own prompts can tell you what.

For where GPT-6 Sol sits against the rest of the field, see the open-weight vs closed-source benchmark and its October refresh.

Try it yourself

• Run the three-bullet test — the task where GPT-6 Sol's output dropped by almost half

No account, no API key. To run your own prompts against GPT-5.6 Sol and both GPT-6 Sol models side by side with token counts and cost, that's Promptster.

Run 2026-10-19 through Promptster's own provider handlers, five runs per task per model, max_completion_tokens 1500, reasoning_effort: "medium", no temperature (rejected by GPT-5 and later). Costs are list prices from our pricing table against actual token counts; neither cache discounts nor batch pricing are applied. All three models report reasoning tokens inside the output count, so the displayed cost and the billed estimate are identical here. Latency is median wall-clock from one machine in one session and should be read as indicative. n=5 per task is enough to say nothing failed, not enough to rank models on reliability.