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Free AI Visibility AuditGPT-5 is OpenAI's flagship model, built for complex work that needs step-by-step reasoning, careful instruction following and accurate output. It handles text, images and files, and is the model most people now meet by default when they use ChatGPT.
OpenAIVerified 24/09/2026Released 07/08/202501 / 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
$1.25
field median $0.30
Output / 1M tokens
$10.00
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 is a general-purpose reasoning model from OpenAI, positioned as the most capable thing the company ships. OpenAI points it at complex tasks: multi-step problems, code quality, holding to detailed instructions. It is not a small, cheap classifier you drop into a high-volume pipeline, and it is not a specialist audio or video model.
Where it sits in its line, and when a sibling is the better pick.
GPT-5 first appeared in August 2025 as the head of OpenAI's line, replacing the previous generation as the default choice for serious work. Reach for it when the task has several dependent steps, when the instructions are long and must be obeyed exactly, or when a wrong answer is expensive. For simple extraction, short rewrites or anything you run millions of times a day, a smaller sibling will do the same job for less.
The API, the apps it powers, and what its limits let you do.
GPT-5 is reached through the OpenAI API and powers ChatGPT across web and mobile. It accepts text, images and files in the same request, so you can hand it a document and a question together rather than pre-parsing everything yourself. The context window is large enough to hold long documents, full codebases in sections, or an extended conversation without pruning, and the output ceiling is high enough to generate whole files or long structured reports in one pass.
What changes because this exists, or why it does not.
The practical change is that long-context and long-output work stop being a plumbing exercise. Tasks that previously needed chunking, summarising and stitching, such as reviewing a large contract set or refactoring across many files, can be handled in a single call. Combined with the improvements OpenAI claims in instruction following, that makes it more realistic to put the model behind a workflow and trust the format of what comes back.
Follows GPT-4.1 Nano. Superseded by GPT-5 Mini. 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 | $1.25 | per 1M tokens | 2026-09-24 | Check |
| Output | $10.00 | per 1M tokens | 2026-09-24 | Check |
GPT-5 is priced as a premium model, with output costing substantially more per token than input, so the shape of your prompts matters less than the length of what you ask it to write. It is not the cheapest way to answer a simple question, and teams running it at volume usually route easy traffic to a smaller model and keep GPT-5 for the work that justifies it.
| Workload | Input / month | Output / month | Cost |
|---|---|---|---|
| A small product team | 20M tokens | 5M tokens | $75.00 |
| A busy support assistant | 200M tokens | 40M tokens | $650.00 |
| A document pipeline | 1000M tokens | 100M tokens | $2,250.00 |
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 | text, image, file |
| Released | 07/08/2025 |
| Status | Current |
| Catalogue identifier | openai/gpt-5 |
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-4.1 NanoCame after
GPT-5 Mini →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
Because GPT-5 is the default in ChatGPT, how it describes your category is, for a lot of buyers, the first description they get. Its longer context means it can take in more of a source page at once, so depth and structure on your own site are worth more than they were when models were skimming fragments. If your product's positioning, pricing logic and differentiators are not stated plainly somewhere it can read, it will reason its way to an answer from whatever else it finds.
Most buyers encounter GPT-5 without asking for it, as the model answering them in ChatGPT. Technical buyers meet it through the OpenAI API when they evaluate it against rivals for their own product. It also sits underneath a long tail of third-party tools and AI search features that quietly call OpenAI on the back end, so a buyer can be reading GPT-5 output inside a vendor's own interface.
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.