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Free AI Visibility AuditGPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 series, built for complex reasoning, coding and agentic workflows, with particular strength in command-line and multi-step coding 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 is a general-purpose reasoning and coding model that sits at the top of OpenAI's GPT-5.6 line. It is built to hold long, multi-step tasks together: planning a change, running it across a codebase, working through a terminal session. It is not a lightweight classifier or a cheap high-volume text worker, and using it that way wastes most of what you pay for.
Where it sits in its line, and when a sibling is the better pick.
GPT-5.6 Sol was first seen in July 2026 as the flagship of the GPT-5.6 series, sitting above the smaller siblings in the same family. Reach for it when the job is genuinely hard, agentic coding, multi-step reasoning over a large body of material, work that has to survive dozens of turns without drifting. For short answers, routing, tagging or summarising a single page, a smaller model in the series does the same job for less.
The API, the apps it powers, and what its limits let you do.
GPT-5.6 Sol is reached through the OpenAI API and powers the top tier of OpenAI's own assistant surfaces. It takes text, images and files as input, so you can hand it a screenshot, a PDF or a directory of source and ask it to reason across all of it in one pass. The context window is large enough to hold an entire repository, a long document set or a full agent trace without chunking, and the output ceiling is high enough to write back substantial files rather than fragments.
What changes because this exists, or why it does not.
The combination of a very large context window and a high output ceiling removes a class of engineering that used to exist purely to work around model limits: chunking, retrieval scaffolding, stitching partial files back together. Teams building coding agents can now keep the whole task in view and let the model run the terminal steps itself. That is an incremental improvement rather than a new capability, but it is the difference between an agent that mostly finishes and one that needs babysitting.
Follows GPT-5.6 Luna Pro. Superseded by GPT-5.6 Sol Pro. 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 |
GPT-5.6 Sol is priced as a flagship, with output costing several times more than input, which is the usual shape for reasoning models. It is not the cheapest way to answer a simple question, and the economics only work when the task genuinely needs the reasoning depth, so most production stacks route the easy traffic elsewhere and save Sol for the hard cases.
| 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 |
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 Luna ProCame after
GPT-5.6 Sol Pro →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
A model that can hold a million tokens of context reads more of your site in one go, so the shallow, keyword-shaped page matters less and the substantive, internally consistent one matters more. File and image input means your documentation PDFs, architecture diagrams and pricing tables are now readable evidence, not dead weight. If your product claims are only defensible in a sales deck and contradicted by your docs, a model working at this context depth will notice.
Buyers meet GPT-5.6 Sol in two places: inside OpenAI's consumer and business assistant products, where it serves the highest tier of requests, and directly through the API when their own vendors build on it. In practice, a buyer researching your category is likely to be reading an answer composed by a model in this family, whether or not they know which one.
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.