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Free AI Visibility Audito3 is OpenAI's reasoning model, released in April 2025, built for maths, science, coding and visual reasoning, and available through the OpenAI API and ChatGPT.
OpenAIVerified 24/09/2026Released 16/04/202501 / 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
$2.00
field median $0.30
Output / 1M tokens
$8.00
field median $1.25
Context
200K
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.
o3 is a general reasoning model rather than a chat model tuned for speed. OpenAI positions it as its strongest all-rounder across domains, with maths, science, coding and visual reasoning as the headline jobs, and technical writing and instruction-following close behind. It is not the right tool for high-volume, low-stakes work where a lighter model would answer just as well for less.
Where it sits in its line, and when a sibling is the better pick.
o3 first appeared in April 2025 as the senior member of OpenAI's o-series reasoning line. Reach for it when a task genuinely needs working-through, a technical spec, a proof, a debugging session across a large codebase, or a diagram that has to be read properly. When the job is summarising, classifying or drafting at volume, a cheaper sibling will do the same work without the reasoning overhead.
The API, the apps it powers, and what its limits let you do.
o3 is reached through the OpenAI API and sits behind the model picker in ChatGPT. It takes text, images and files in the same request, so you can hand it a screenshot, a PDF spec and a question together, and its context window is large enough to hold a long codebase or a full document set in one pass. The generous output ceiling means it can return a complete technical document or a long reasoning trace without being cut short.
What changes because this exists, or why it does not.
o3 made deep reasoning over mixed inputs a normal API call rather than a workaround. Teams that used to split a task into a vision step, a retrieval step and a reasoning step can now send the diagram, the document and the question together and get one worked answer, which removes a lot of orchestration code from technical and scientific pipelines.
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 | $8.00 | per 1M tokens | 2026-09-24 | Check |
o3 costs meaningfully more per call than the lightweight chat models, and output is charged at several times the input rate, which matters because reasoning models produce long answers. For a frontier reasoning model it is priced to be used routinely rather than saved for special occasions, but it is still worth routing simple requests elsewhere.
| Workload | Input / month | Output / month | Cost |
|---|---|---|---|
| A small product team | 20M tokens | 5M tokens | $80.00 |
| A busy support assistant | 200M tokens | 40M tokens | $720.00 |
| A document pipeline | 1000M tokens | 100M tokens | $2,800.00 |
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 | 200,000 tokens |
|---|---|
| Maximum output | 100,000 tokens |
| Modalities | image, text, file |
| Released | 16/04/2025 |
| Status | Current |
| Catalogue identifier | openai/o3 |
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.
Nothing else in this line yet.
A line is worked out from the naming and the release dates across every model page we hold. It fills in as the provider ships successors, or as we pick up the models that came before this one.
How we decide what counts as evidence10 / 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, technical question about your category, a reasoning model like o3 will work through the comparison rather than repeat the first source it finds, so thin marketing pages carry less weight than documentation, pricing detail and clear specification. Because it reads images and files alongside text, the material in your PDFs, architecture diagrams and spec sheets is now part of what gets cited, not just your web copy. The practical move is to make the technical substance of your product legible and consistent everywhere it appears.
Buyers meet o3 inside ChatGPT when they select a reasoning model for a harder question, and engineering teams meet it through the OpenAI API when they build research, coding or document-analysis features. It also sits under third-party products that route technical queries to a stronger model, so a buyer may be reading an o3 answer inside a vendor's own interface without ever seeing the name.
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.