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Free AI Visibility AuditGPT-5 Nano is the smallest and fastest model in OpenAI's GPT-5 family, built for developer tooling and high-volume work where latency and cost matter more than reasoning depth.
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
$0.05
field median $0.30
Output / 1M tokens
$0.40
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 Nano is a small, low-latency text model that also accepts images and files. It is built for the jobs that sit underneath a product, classification, extraction, routing, autocomplete and other rapid interactions, rather than for long chains of reasoning. It is not the model to reach for when an answer needs careful deliberation, and OpenAI is explicit that its reasoning depth is limited next to the larger GPT-5 variants.
Where it sits in its line, and when a sibling is the better pick.
GPT-5 Nano arrived in August 2025 as part of the GPT-5 launch, sitting at the bottom of the range below its larger siblings. It is the right pick when you are running the same call thousands of times a day and every millisecond and fraction of a cent compounds. It is the wrong pick for anything a user will read closely as a final answer, or for multi-step work where a weak intermediate step ruins the output.
The API, the apps it powers, and what its limits let you do.
GPT-5 Nano is reached through the OpenAI API and is not the model behind the ChatGPT consumer experience. Its context window is large enough to hold long documents, transcripts or whole codebases in a single call, and the generous output ceiling means it can return bulk structured data rather than short snippets. Text, image and file inputs mean it can be pointed at a PDF or a screenshot in the same pipeline as plain prompts.
What changes because this exists, or why it does not.
GPT-5 Nano makes a class of work economically sensible that previously was not: tagging every support ticket, scoring every page, parsing every uploaded file, running a first-pass filter before a more expensive model sees the request. Teams that were batching or sampling because inference cost too much can now process everything. The interesting part is not what it can do that bigger models cannot, it is the volume at which you can afford to run it.
Follows GPT-5 Mini. Superseded by GPT-5 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 | $0.05 | per 1M tokens | 2026-09-24 | Check |
| Output | $0.40 | per 1M tokens | 2026-09-24 | Check |
GPT-5 Nano is the cheapest way into the GPT-5 family by a wide margin, cheap enough that per-call cost stops being the thing you design around. Compared with frontier models from any provider it is an order of magnitude less expensive to run, which is the whole point of it, and the trade is reasoning depth rather than context or modality.
| Workload | Input / month | Output / month | Cost |
|---|---|---|---|
| A small product team | 20M tokens | 5M tokens | $3.00 |
| A busy support assistant | 200M tokens | 40M tokens | $26.00 |
| A document pipeline | 1000M tokens | 100M tokens | $90.00 |
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-nano |
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 MiniCame after
GPT-5 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
GPT-5 Nano does not change where your brand gets cited, because it is not the model writing answers for buyers. What it does change is the economics of the pipelines around AI search: monitoring mentions, classifying prompts, parsing competitor pages and scoring content at scale all become cheap enough to run continuously rather than as periodic audits. Treat it as infrastructure for your own measurement, not as a surface to optimise for.
Buyers rarely meet GPT-5 Nano head on. It shows up inside products as the thing doing the fast bit, autocomplete, suggestions, instant summaries and categorisation, and through the OpenAI API where developers pick it as the default for high-frequency calls. It is not the model answering questions in ChatGPT, so it is not a surface where your brand gets recommended.
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.