Apple Machine Learning Research

8 models across 4 families · Latest: DCLM 7B (2024-07)

Researched 3d ago

On-device AI prioritizes user privacy

AgentsVisionJSON / Tool use

Apple Machine Learning Research's portfolio covers 8 active models across 4 current families, spanning agents, vision, and json / tool use. Open a model detail page to compare provider routes and sourced benchmarks.
Covers 3 workload areas across 8 active tracked models; last verified 2026-10-14.

Use it for

Teams evaluating agents, vision, and json / tool use across this lab's releases
Comparing model families before committing to a flagship
Migration and pricing follow-ups across 8 tracked models

Do not use it for

Choosing a hosting provider without opening a model page for price ladders

Active models

Current models from this lab, excluding deprecated ones

Active families

Current model families from this lab

Open catalog

2 open

2 open source / 0 open weights

Lowest output price

Not tracked

No provider output pricing linked yet

Latest dated release

2024-07-15

DCLM 7B

Freshness

2026-10-14

Researched 3d ago

fresh

Information

Founded1976

Cupertino, California, United States

Links

Website GitHub X / Twitter LinkedIn HuggingFace Crunchbase

Release cadence

Dated releases: 5
Latest model: DCLM 7B
Latest date: 2024-07-15

Where this lab wins

Agentic: 1 tracked model with BFCL, tau-bench, and SWE-bench tool-use coverage.
Vision: 1 tracked model with multimodal benchmark coverage.
JSON/tool-use: 1 tracked model with BFCL / Nexus strict-JSON routing coverage.

Flagship quality / price signal

Flagship: Apple On-Device
Selection source: Best sourced coding Q/$
Coding grade: unknown
Benchmark / output price: Not enough sourced benchmark and price coverage

About

Apple Machine Learning Research is at the forefront of technological advancements in the realm of generative AI and Large Language Models (LLMs). As a division of Apple Inc., which was founded in 1976, this team has been instrumental in driving innovation, with a particular emphasis on integrating AI functionalities directly onto devices to ensure enhanced user privacy and operational efficiency. Apple’s distinct approach to AI development is evident in its commitment to on-device processing, which minimizes reliance on cloud-based systems, thereby safeguarding user data and ensuring quicker response times.