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Free AI Visibility AuditGPT-5.6 Sol Pro is OpenAI's GPT-5.6 Sol served with its reasoning mode set to "pro", a higher-effort setting that trades speed and cost for better answers on complex tasks.
OpenAIVerified 24/09/2026Released 09/07/202601 / decision
Each figure sits next to the middle of the field, so it can be read as dear or cheap, wide or narrow, rather than floating on its own. Compared against the 151 models we track, not against an absolute standard.
Capability
Not measured
No published benchmark scores yet
Input / 1M tokens
$2.00
field median $0.43
Output / 1M tokens
$10.00
field median $1.75
Context
1,050K
field median 524K
02 / overview
Four questions, answered separately, because somebody arrives at one of them rather than at the top of the page.
The job it was built for, and the job it is not for.
GPT-5.6 Sol Pro is not a separate model, it is the same underlying GPT-5.6 Sol running with `reasoning.mode` set to `pro`. The job it is built for is the harder end of the work: multi-step problems where a first-pass answer tends to be wrong in ways that are expensive to catch. It is not the setting you want for routine classification, extraction or chat, where the standard Sol configuration does the same work for less.
Where it sits in its line, and when a sibling is the better pick.
GPT-5.6 Sol Pro was first seen in July 2026, alongside GPT-5.6 Sol in the same family. Reach for it when a task is genuinely difficult and the cost of a wrong answer outweighs the cost of the extra reasoning, and leave it alone for high-volume, low-stakes calls where plain Sol is the sensible pick.
The API, the apps it powers, and what its limits let you do.
You reach it through the OpenAI API by setting the reasoning mode parameter, which is documented in OpenAI's reasoning guide, so switching between Sol and Sol Pro is a configuration change rather than a migration. It handles text, images and file inputs, and the context window runs past a million tokens, so whole document sets, long codebases or full case histories can go in as a single prompt. Output length is generous enough for long reports and large code edits in one response.
What changes because this exists, or why it does not.
Because the pro mode is a parameter rather than a different endpoint, teams can run one model and dial the reasoning up only on the requests that need it, instead of maintaining routing between a cheap model and a smart one. That makes tiered quality inside a single product much less awkward to build.
Follows GPT-5.6 Sol. Superseded by GPT-5.6 Terra. See the whole line.
03 / evidence
Split by category, so a strong number never hides a thin evidence base. Verified means we read it on the benchmark's own published results; a provider's claim about its own model is shown and labelled rather than dropped.
No published benchmark scores for this model yet.
We publish a score only where we can link the result to where it was published. Until a benchmark result for this model exists in a source we read, this section stays empty rather than being filled with a provider's marketing figure.
How we decide what counts as evidence04 / ledger
Every published row, grouped by category, each compared with the best published score on the same benchmark. 'Is 64% good' is a question nobody can answer; '26 points behind the leader' is one anybody can.
Nothing in the ledger yet.
Each row here carries a score, the benchmark it came from, what the leading model scored on the same test, and a link to the published result. Rows appear as results are published and read.
How we decide what counts as evidence05 / capability
Where this model is strong, and against how many peers. Ranks are against models with evidence in that category, not against everything we track: ranking against models nobody tested would rank who published, not who is better.
No category scores to shape yet.
A category score is the weighted mean of the benchmarks published for it. With no published rows there is nothing to average, and an empty chart drawn at zero would say something false.
How we decide what counts as evidence06 / cost
List API rates as last read from the provider, with the source on every row, plus every change we have recorded since we started tracking it.
| Charge | Price | Unit | Read on | Source |
|---|---|---|---|---|
| Input | $2.00 | per 1M tokens | 2026-09-24 | Check |
| Output | $10.00 | per 1M tokens | 2026-09-24 | Check |
Input and output are priced the same as the underlying Sol model, so the extra cost of pro mode comes from the reasoning tokens it spends, not from a higher rate. That puts it in the mid part of the field on paper, with real spend depending entirely on how much thinking each request triggers.
| Workload | Input / month | Output / month | Cost |
|---|---|---|---|
| A small product team | 20M tokens | 5M tokens | $90.00 |
| A busy support assistant | 200M tokens | 40M tokens | $800.00 |
| A document pipeline | 1000M tokens | 100M tokens | $3,000.00 |
List API rates, no caching and no batch discount, which both providers offer and which change the answer a great deal. Treat these as the ceiling, not the bill.
07 / specs
As listed by the provider's own catalogue and re-read every few hours. Anything absent is absent there too.
| Context window | 1,050,000 tokens |
|---|---|
| Maximum output | 128,000 tokens |
| Modalities | file, image, text |
| Released | 09/07/2026 |
| Status | Current |
| Catalogue identifier | openai/gpt-5.6-sol-pro |
08 / lineage
What this model replaced, what replaced it, and what else its provider has in the field.
Also from OpenAI
09 / line
Every model in this family in release order, so a page from eight months ago says in one glance that two newer ones exist.
Came before
← GPT-5.6 SolCame after
GPT-5.6 Terra →Ordered by release date and worked out from the naming, so a new member slots in as soon as its page exists. A retired model keeps its page and its place in the line.
10 / notes
What this model changes for a brand trying to be cited in AI answers, and every change we have logged since it launched.
What it changes for you
When an AI answer engine turns reasoning mode up, it reads more sources, follows more of the chain and is less easily satisfied by the first plausible page. Thin comparison pages and vague positioning get filtered out at that setting, while documentation, pricing detail and specifics that survive scrutiny get cited. The practical response is to make sure the claims on your site can be checked, because increasingly they will be.
Most buyers meet GPT-5.6 Sol Pro through the API rather than by name, since it is a setting on Sol rather than a separate product in a model picker. In practice they encounter its output inside ChatGPT's harder reasoning modes and inside any tool or AI search surface whose builders turned the reasoning mode up for difficult queries.
Nothing published here yet. Changes appear within hours of a provider announcing them.
11 / questions
The things people ask about this model, answered from what is on this page rather than from anywhere else.
Models change what gets cited. We measure whether AI answers name your brand or your competitors across every assistant, and show you what to change.