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Free AI Visibility AuditGPT-5 Pro is OpenAI's most advanced model, built for complex tasks that need step-by-step reasoning, careful instruction following and high code quality. It takes text, images and files as input, and is the model OpenAI positions above the rest of the GPT-5 line.
OpenAIVerified 24/09/2026Released 06/10/202501 / 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 126 models we track, not against an absolute standard.
Capability
Not measured
No published benchmark scores yet
Input / 1M tokens
$15.00
field median $0.30
Output / 1M tokens
$120.00
field median $1.25
Context
400K
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 Pro is a reasoning model from OpenAI, aimed at work that has to be thought through rather than answered quickly: multi-step analysis, code that has to hold together, instructions that have to be followed exactly. It reads text, images and files. It is not the model to reach for when you need cheap, high-volume classification or a fast conversational reply, because the reasoning it does is the thing you are paying for.
Where it sits in its line, and when a sibling is the better pick.
GPT-5 Pro was first seen on 6 October 2025, sitting at the top of OpenAI's GPT-5 family as the most capable tier. Pick it when a task genuinely fails on the smaller GPT-5 variants, long analytical chains, difficult code, briefs with many constraints. For anything routine, a lighter sibling will do the same job for a fraction of the spend.
The API, the apps it powers, and what its limits let you do.
GPT-5 Pro is reached through the OpenAI API and appears in OpenAI's own products as the top reasoning tier. Its context window runs to several hundred thousand tokens with a very large maximum output, so you can put a full document set, a codebase section or a long research pack in front of it and get a long, structured answer back rather than a summary. Image and file input mean the source material does not have to be flattened into plain text first.
What changes because this exists, or why it does not.
GPT-5 Pro makes it practical to hand a model an entire brief, with attachments, and ask for a finished piece of reasoning rather than a fragment you then stitch together. The combination of a large input window and a very large output allowance removes the usual chunk-and-reassemble workaround for long deliverables. What it does not change is cost discipline, this is a model you route to deliberately, not one you put behind every request.
Follows GPT-5 Nano. Superseded by GPT-5.1. 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 | $15.00 | per 1M tokens | 2026-09-24 | Check |
| Output | $120.00 | per 1M tokens | 2026-09-24 | Check |
GPT-5 Pro sits at the expensive end of the market, with output costing several times its input, so verbose answers are where the bill accumulates. Treat it as a model you route specific hard tasks to, with a cheaper sibling handling the everyday volume.
| Workload | Input / month | Output / month | Cost |
|---|---|---|---|
| A small product team | 20M tokens | 5M tokens | $900.00 |
| A busy support assistant | 200M tokens | 40M tokens | $7,800.00 |
| A document pipeline | 1000M tokens | 100M tokens | $27,000.00 |
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 | 400,000 tokens |
|---|---|
| Maximum output | 128,000 tokens |
| Modalities | image, text, file |
| Released | 06/10/2025 |
| Status | Current |
| Catalogue identifier | openai/gpt-5-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 NanoCame after
GPT-5.1 →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 a buyer asks a considered, multi-part question about your category, GPT-5 Pro is the kind of model answering it, and it will read further into the material it retrieves than a fast model would. That rewards depth: comparison pages, documented limitations, pricing logic and real implementation detail get used, thin positioning copy gets skipped. If your public material only supports a one-line answer, you will be cited for one line while a competitor's substance fills the rest.
Buyers meet GPT-5 Pro inside OpenAI's consumer and business products, where it is offered as the most capable option on the model picker, and through the API when a vendor has chosen it for a high-stakes feature. In practice that means deep-research style tasks, document analysis and coding assistants, the places where a buyer is asking a long question and expecting a considered answer.
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.