Levelmodel
Classmultimodal LLM
Parametersnot in the notes
Contextnot in the notes
ReleasedApr 2025
Opennessopen weights restricted
Runs onmulti-GPU node, single datacentre GPU
Statusactive
Benchmark Results
maintainer-run
SEA-HELMSoutheast Asian and Indonesian
Checkpoint Llama-4-Scout-17B-16E-Instruct as listed; release view (data last updated 18 Sep 2026); score is the average of 2000 bootstrapsmaintainer-run
67.86 95% CI [-1.58, +1.56]
04080
Score in score. Higher is better.
SEA-HELM leaderboard, 58 models, Indonesian (ID)
Checkpoint Llama-4-Scout-17B-16E-Instruct as listed; release view (data last updated 18 Sep 2026); score is the average of 2000 bootstrapsmaintainer-run
63.43 95% CI [-0.78, +0.79]
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
upcycle prune expand
Llama Guard 4 12B is a child (upcycle prune expand)high confidencemodel cardWhere 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, 2 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 MoE (16 experts)
- Total parameters
- – sources
- Active parameters
- –
- Checkpoint parameters
- –
- Context length
- not in the notes
- Input
- text, image
- 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 restricted
- Licence
- Llama 4 Community License sources
- Access routes
- open weights download
Where this comes from
The licence file governs over registry tags and aggregator labels.
Deployment
- Runs on
- multi-GPU node, single datacentre GPU
- 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
DeveloperMetabig tech, USA
Metabig tech, USAModelLlama 4 ScoutMeta, multimodal LLM
Llama 4 ScoutMeta, multimodal LLMR&D TopicsEfficiency Architectures (MoE, Sparse Attention, Long Context)Open-Weight Frontier Releases as Research Substrate
Use CasesClinical LLM (Open Weights)HealthcareProcess Optimisation and Advanced Process ControlIndustrialTelecom Domain LLMs and BenchmarksTelecomUse cases of models in the same R&D topic; none is recorded for this model itself.
Who Runs It
PT Amman Mineral Nusa Tenggara (AMMAN)In production, 8 Jul 2026MIND IDResearch, 14 Oct 2025
Conch CementIn production, 23 Jun 2025
PT Kalbe Morinaga IndonesiaIn production, Oct 2024+2 moreDeployments of these use cases; none names this model.
PT Amman Mineral Nusa Tenggara (AMMAN)In production, 8 Jul 2026MIND IDResearch, 14 Oct 2025Conch CementIn production, 23 Jun 2025PT Kalbe Morinaga IndonesiaIn production, Oct 2024+2 moreDeployments of these use cases; none names this model.Organisations 1
Sources
- Open-Source LLMs 2026: Which Are Actually Open-Licensed codersera.com https://codersera.com/blog/open-source-llms-landscape-2026/
- huggingface.co/meta-llama https://huggingface.co/meta-llama
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "Llama 4 Scout", https://aurion.semai.id/models/meta-llama-4-scout/, records of October 2026.