Levelfamily
ClassLLM
Parametersnot in the notes
Contextnot in the notes
Released5 Aug 2025
Opennessopen weights permissive
Runs onnot in the notes
Statusactive
Benchmark Results
maintainer-run
SEA-HELMSoutheast Asian and Indonesian
gpt-oss-120bmaintainer-run
72.28 95% CI [-1.35, +1.33]
rank 7, SEA-HELM Indonesian, open-weight models, average of 2,000 bootstraps
gpt-oss-20bCheckpoint gpt-oss-20b as listed, quantization mxfp4; release view (data last updated 18 Sep 2026); score is the average of 2000 bootstrapsmaintainer-run
68.38 95% CI [-1.34, +1.35]
SEA-HELM leaderboard, 58 models, Indonesian (ID)
gpt-oss-20bCheckpoint gpt-oss-20b as listed, quantization mxfp4; release view (data last updated 18 Sep 2026); score is the average of 2000 bootstrapsmaintainer-run
60.64 95% CI [-0.64, +0.63]
04080
Score in score. Higher is better.
SEA-HELM leaderboard, 58 models, SEA average
Where this comes from
Every score names who ran it: vendor-reported, maintainer-run or independent. A rank is shown with the population it was ranked in (gap rule 3).
Lineage
continued pretrainfinetuneproduct of
GPT-OSS-Swallow is a child (continued pretrain)medium confidencenotesgpt-oss-20b is a child (product of)high confidencenotesgpt-oss-safeguard-20b belongs to this familyguardJan 202621Bgpt-oss-safeguard-120b belongs to this familyguardOct 2025117Bgpt-oss-120b belongs to this familyLLM5 Aug 2025117Bgpt-oss-20b belongs to this familyLLM5 Aug 202521BWhere this comes from
Parent and child relations from model cards, Hugging Face base_model fields and the genealogy tables of the research notes. The tree shows two generations each way, 5 models. Confidence and evidence are the notes' own.
R&D Topics
Identity
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.
Architecture and Size
- Architecture
- MoE
- Total parameters
- – sources
- Active parameters
- –
- Checkpoint parameters
- –
- Context length
- not in the notes
- Input
- text
- Output
- text
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).
Openness and Access
- Openness
- open weights permissive
- Licence
- Apache License 2.0 sources
- Access routes
- open weights download
Where this comes from
The licence file governs over registry tags and aggregator labels.
Deployment
- Runs on
- not in the notes
- Footprint
- not in the notes
- Runtimes
- not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.
Indonesia
- Indonesian support
- not stated
- Regional languages
- not in the notes
- Jakarta hosting
- unknown
- Languages
- not in the notes
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.
Industry Use Cases
No industry use case links this model.
Links
DeveloperOpenAIfrontier lab, USA
OpenAIfrontier lab, USAModelgpt-ossOpenAI, LLM
gpt-ossOpenAI, LLMR&D TopicsEfficiency Architectures (MoE, Sparse Attention, Long Context)Open-Weight Frontier Releases as Research SubstrateQuantization and Edge Inference
Use CasesClinical LLM (Open Weights)HealthcareProcess Optimisation and Advanced Process ControlIndustrialTelecom Domain LLMs and BenchmarksTelecomIndonesian and Regional-Language LLMsPublic Sector+1 moreUse cases of models in the same R&D topic; none is recorded for this model itself.
Who Runs It
PT Pertamina Geothermal EnergyIn production, 14 Jul 2026
PT Amman Mineral Nusa Tenggara (AMMAN)In production, 8 Jul 2026
Siemens Erlangen electronics factoryPlanned, 6 Jan 2026MIND IDResearch, 14 Oct 2025+16 moreDeployments of these use cases; none names this model.
PT Pertamina Geothermal EnergyIn production, 14 Jul 2026PT Amman Mineral Nusa Tenggara (AMMAN)In production, 8 Jul 2026Siemens Erlangen electronics factoryPlanned, 6 Jan 2026MIND IDResearch, 14 Oct 2025+16 moreDeployments of these use cases; none names this model.Models 5
Organisations 1
R&D topics 3
Open-Weight Frontier Releases as Research SubstrateUsing released weights as the base for downstream research.exemplarEfficiency Architectures (MoE, Sparse Attention, Long Context)Reducing training and inference cost per capability.exemplarQuantization and Edge InferenceLower-bit weights and on-device serving.exemplar
Sources
- Models | OpenAI API developers.openai.com https://developers.openai.com/api/docs/models
- openai/gpt-oss-120b , Hugging Face huggingface.co https://huggingface.co/openai/gpt-oss-120b
- huggingface.co/openai https://huggingface.co/openai
- openai/gpt-oss-safeguard-20b , Hugging Face huggingface.co https://huggingface.co/openai/gpt-oss-safeguard-20b
- openai.com/index/introducing-gpt-oss https://openai.com/index/introducing-gpt-oss/
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "Gpt-Oss", https://aurion.semai.id/models/openai-gpt-oss/, records of October 2026.