OpenAI ships open weights for the first time since GPT-2

OpenAI released gpt-oss-120b and gpt-oss-20b today under Apache 2.0. These are the company's first open weights models since GPT-2 in 2019.

the specs#

Both are mixture-of-experts with configurable reasoning effort:

• gpt-oss-120b — 117B total parameters, ~5.1B active. Fits on a single 80 GB accelerator with the provided MXFP4 quantization.
• gpt-oss-20b — 21B total, ~3.6B active. Runs on a machine with 16 GB of memory.

That second one is the important number. 16 GB is a well-specified laptop. A reasoning model with tool use and a configurable thinking budget that runs on a laptop, under Apache 2.0, from OpenAI, is a sentence that would have been implausible eighteen months ago.

Reasoning effort is set in the system prompt — low, medium, or high — which is a cruder interface than a token budget but works.

the format#

The models use a "harmony" response format with structured channels separating analysis, commentary, and final output. You have to render it correctly or the model behaves poorly. The reference implementations handle it; if you are writing your own serving path, read the format spec first rather than debugging it later.

They support tool use — browsing and Python execution — natively, and function calling in the standard shape.

why now#

Three reasons, in descending order of how much anyone will admit them.

Competitive pressure. The open weights frontier is currently defined by DeepSeek, Qwen, Moonshot, and Mistral. A company named OpenAI having no open models had become a running joke and, more importantly, a strategic gap — the developers building on open weights were building on someone else's ecosystem.

Policy positioning. There is an active regulatory conversation about open models. Being a participant with skin in the game is worth more than commenting from the sidelines.

The capability gap has closed enough to be safe and stayed wide enough to be commercial. Releasing a model at roughly o3-mini level costs OpenAI little in API revenue — the customers who need frontier capability still need it — and buys a lot of ecosystem.

how good are they#

Genuinely competitive in their size class. The 120b is roughly comparable to o3-mini on reasoning benchmarks; the 20b is close to o3-mini on several and weaker on others.

The caveats are the usual ones for open weights: they hallucinate more than frontier models, the safety tuning is more easily removed by fine-tuning (OpenAI published research on this specifically), and benchmark performance overstates real-world reliability.

what to do with them#

The 20b is the interesting one for most developers. Concretely:

• Local coding assistance with no data leaving the machine. This clears the legal review that blocks hosted models at a lot of companies.
• Batch processing where you have a lot of documents and API costs add up. Run it on a spot instance overnight.
• Fine-tuning for a narrow domain. Apache 2.0 means you can, and a fine-tuned 20b on a specific task frequently beats a general frontier model on that task, for a fraction of the cost.

ollama run gpt-oss:20b

That is the whole setup. The ecosystem picked it up within hours of release, which is itself a demonstration of why open weights are worth releasing.

the broader point#

Two years ago the argument was "open models are dangerously behind or dangerously capable, pick one." The answer turned out to be: they trail the frontier by roughly six to twelve months, that gap is stable, and the world has not ended.

The gap being stable is the interesting finding. It means open weights are permanently a viable option for anything that does not need the absolute frontier — which is most things.