Model datasheet · gpt-oss
gpt-oss-120b
The big sibling of gpt-oss-20b, and the same bargain at four times the size: shipped natively in MXFP4, so there is no quant ladder to choose from — one 63GB file is the model. Reasoning and tool use are excellent; world knowledge is not. It was built to look things up.
- Vendor
- OpenAI
- Architecture
- MoE · 117B total / ~5.1B active
- Context
- 131,072 tokens
- License
- Apache-2.0
- Released
- 2025-08-05
- Vision
- No
§1 Characteristics by quantization band
data checked Aug 2026
- vendor-model-card — AIME 2024 (high reasoning, no tools) = 95.8 @ mxfp4 · vendor-reported · 2026-08-31
96.6 with tools. GPQA Diamond 80.1, MMLU 90.0. More than double the 20b's 42.1 on the same benchmark — the clearest size effect in this family.
data checked Aug 2026
- vendor-model-card — SWE-bench Verified = 62.4 @ mxfp4 · vendor-reported · 2026-08-31
Codeforces Elo 2463, Aider Polyglot 44.4. Only 1.7 points above the 20b despite 5.6x the parameters; the Aider figure is the more sober read of everyday editing.
data checked Aug 2026
- vendor-model-card — Tau-Bench Retail = 67.8 @ mxfp4 · vendor-reported · 2026-08-31
Against the 20b's 54.8. Tool use is the family's organizing principle — browsing, python and functions are first-class in the harmony format.
data checked Aug 2026
- vendor-model-card — SimpleQA = 16.8 @ mxfp4 · vendor-reported · 2026-08-31
PersonQA hallucination rate 49.1% — OpenAI's own number. Better than the 20b's 6.7 / 53.2% and still poor in absolute terms. HealthBench 57.6.
§2 Known issues & what fixes them
Hallucinates confidently on world knowledge. SimpleQA 16.8 and a 49.1% PersonQA hallucination rate are OpenAI's own published figures — better than the 20b's, and still bad enough that this model should not be trusted as a source of facts. It is built to reason and call tools, not to remember.
Give it documents to read. Retrieval turns the weakness into a non-issue for document work.
Wire up search. The model is explicitly designed to look things up rather than recall them.
Evidence · 2 sources · community-consensus
- gpt-oss model card: SimpleQA and PersonQA hallucination figures (paper, 2026-08-31)
- gpt-oss-120b model card (vendor, 2026-08-31)
There is no quant choice to make: the MoE weights were post-trained in MXFP4, so the 63GB native file is the model rather than a compression of it. Convenient — no band to agonise over — but the usual escape hatch is missing. If 63GB does not fit, there is no smaller official build, and requantizing an already-4-bit model is not the same operation as quantizing from bf16.
Use gpt-oss-20b instead if 63GB is out of reach; same architecture, 12GB.
Evidence · 2 sources · community-consensus
- gpt-oss-120b: MoE weights post-trained in MXFP4 (vendor, 2026-08-31)
- ggml-org GGUF: a single 63.4GB MXFP4 file (other, 2026-08-31)