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Free AI Visibility AuditGPT-5 Mini is OpenAI's compact version of GPT-5, built for lighter-weight reasoning tasks at lower latency and lower cost than the full model.
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
$0.25
field median $0.30
Output / 1M tokens
$2.00
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 Mini is a smaller sibling of GPT-5 that keeps the same instruction-following behaviour and safety tuning while trimming cost and response time. It is built for the high-volume, lighter reasoning work that sits underneath a product: classification, extraction, summarising, routing, drafting. It is not the model to reach for when a task needs the deepest reasoning GPT-5 can offer.
Where it sits in its line, and when a sibling is the better pick.
GPT-5 Mini first appeared in August 2025 alongside the GPT-5 family, positioned below the flagship. It is the right pick when a job is well defined and runs many times a day, and the wrong pick when a single answer carries real weight and you would rather pay for the full model to get it right.
The API, the apps it powers, and what its limits let you do.
GPT-5 Mini is reached through the OpenAI API and sits behind the lighter tiers of products built on the GPT-5 family. It takes text, images and files, so it can read a screenshot, a PDF or an uploaded document rather than only a prompt. The context window is large enough to hold long documents, full transcripts or a sizeable chunk of a codebase in one pass, and the output ceiling allows long structured returns rather than short replies.
What changes because this exists, or why it does not.
GPT-5 Mini makes it reasonable to put a GPT-5-class model in places where the economics previously ruled it out, such as every inbound support ticket, every document in an ingest pipeline, or every row in a backlog. Because it inherits GPT-5's instruction-following, prompts written for the flagship usually transfer without a rewrite, which makes it straightforward to run cheap work on Mini and escalate the hard cases. On its own it is unremarkable, the point is the price per unit of competence.
Follows GPT-5. Superseded by GPT-5 Nano. 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 | $0.25 | per 1M tokens | 2026-09-24 | Check |
| Output | $2.00 | per 1M tokens | 2026-09-24 | Check |
GPT-5 Mini costs a fraction of the flagship to run, which is the main reason to choose it, and output is the side of the bill that moves when you ask for long structured responses. It sits in the same bracket as the other compact models from the major labs, so the deciding factor is usually behaviour and context size rather than the rate itself.
| Workload | Input / month | Output / month | Cost |
|---|---|---|---|
| A small product team | 20M tokens | 5M tokens | $15.00 |
| A busy support assistant | 200M tokens | 40M tokens | $130.00 |
| A document pipeline | 1000M tokens | 100M tokens | $450.00 |
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-mini |
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-5Came after
GPT-5 Nano →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
Cheap capable models mean AI answer surfaces can afford to read more before they respond, so more of your pages get fetched, parsed and weighed rather than skimmed. That rewards content that is easy to extract from, with clear claims, named products and self-contained sections, because a compact model reading at speed will take the plain statement over the implied one. If your positioning only becomes clear after three paragraphs of build-up, a model like this will miss it.
Most buyers meet GPT-5 Mini without knowing it, as the model serving faster or free tiers of ChatGPT-style experiences and the assistant features embedded in the SaaS tools they already pay for. Developers meet it directly in the OpenAI API, usually as the default choice for anything running at volume. It also sits underneath retrieval and answer features in third-party products where the vendor needed GPT-5 behaviour at a workable unit cost.
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.