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Free AI Visibility Auditgpt-oss-120b is OpenAI's open-weight text model, a 117B-parameter Mixture-of-Experts system built for reasoning, agentic work and general production use. Because the weights are published, it can be run on your own infrastructure or through a hosting provider rather than only via OpenAI's API.
OpenAIVerified 24/09/2026Released 05/08/202501 / 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
$0.15
field median $0.30
Output / 1M tokens
$0.60
field median $1.25
Context
131K
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-oss-120b is a text-only language model aimed at high-reasoning and agentic workloads, the kind where a model plans, calls tools and works through a task in steps. It is a general-purpose production model rather than a research curiosity, and it is not multimodal, so images, audio and video are out of scope.
Where it sits in its line, and when a sibling is the better pick.
gpt-oss-120b first appeared in August 2025 as the larger of OpenAI's open-weight releases. It is the right pick when you need to own the deployment, run the model inside your own environment, or avoid per-call dependency on a hosted API. It is the wrong pick when your work involves images or audio, or when you want the frontier-grade reasoning that OpenAI keeps in its closed flagship line.
The API, the apps it powers, and what its limits let you do.
You reach gpt-oss-120b either by downloading the weights and serving them yourself, or through the inference providers that host it. The context window is large enough to hold a long codebase section, a set of support transcripts or a full policy document alongside the instructions, and the output ceiling is generous enough for long structured responses, extended agent traces and full file rewrites without chunking.
What changes because this exists, or why it does not.
An open-weight model at this capability level means reasoning and agent work no longer has to leave your infrastructure, which matters for teams with data residency rules or unpredictable volume. Because it activates only a small fraction of its parameters per forward pass, it runs far more cheaply than its total size suggests, so high-volume background jobs that were awkward to justify on a hosted frontier model become routine.
Superseded by gpt-oss-20b. 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 | $0.15 | per 1M tokens | 2026-09-24 | Check |
| Output | $0.60 | per 1M tokens | 2026-09-24 | Check |
Hosted access to gpt-oss-120b sits at the cheap end of the market, closer to a small model than to a frontier reasoning system, and output costs only a few times more than input. Self-hosting shifts the cost from per-token to hardware, which usually pays off once volume is steady rather than spiky.
| Workload | Input / month | Output / month | Cost |
|---|---|---|---|
| A small product team | 20M tokens | 5M tokens | $6.00 |
| A busy support assistant | 200M tokens | 40M tokens | $54.00 |
| A document pipeline | 1000M tokens | 100M tokens | $210.00 |
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 | 131,072 tokens |
|---|---|
| Maximum output | 65,536 tokens |
| Modalities | text |
| Released | 05/08/2025 |
| Status | Current |
| Catalogue identifier | openai/gpt-oss-120b |
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
The first we track in this line.
Came after
gpt-oss-20b →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
Open weights mean your category can be answered by a model running on someone else's server with no analytics trail back to you, so citation visibility gets harder to measure and more important to earn. The practical response is the same as for any reasoning model, publish clear, factual, well-structured pages that survive being retrieved and summarised out of context. Assume a slice of your buyers are asking a model nobody is monitoring.
Buyers rarely meet gpt-oss-120b in a branded consumer chat app. They encounter it inside products built on top of it, self-hosted assistants, internal copilots and agent tooling, and through the inference platforms that serve the open weights, which means your brand can surface in answers generated by software you will never see a logo for.
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.