Apple unveils the third generation of Apple Foundation Models (AFM 3), a five-model family spanning on-device MoE architectures to high-performance cloud reasoning.

At WWDC26, Apple officially marked a historical milestone in the evolution of edge computing and private AI with the announcement of AFM 3. This third generation of Apple Foundation Models represents a massive leap forward in how intelligence is integrated into the Apple ecosystem, moving beyond simple text prediction to deeply integrated, multimodal, and agentic capabilities.
The AFM 3 family is not a single model but a strategic suite of five distinct models, custom-built in collaboration with Google, designed to scale from local silicon to massive cloud clusters. By optimizing for the entire hardware stack—from the iPhone in your pocket to Private Cloud Compute—Apple is setting a new industry standard for the intersection of high-performance AI and uncompromising user privacy.
The AFM 3 lineup is meticulously segmented to balance latency, power consumption, and reasoning depth. The on-device tier is led by AFM 3 Core, a 3-billion-parameter dense model that provides high-quality intelligence for standard tasks without draining battery life. For more demanding local workloads, Apple introduced AFM 3 Core Advanced, a breakthrough in sparse architecture.
AFM 3 Core Advanced utilizes a Mixture-of-Experts (MoE) approach with 20 billion total parameters, but intelligently activates only 1 to 4 billion parameters per request. This allows it to deliver high-tier intelligence while remaining optimized for Apple silicon. Furthermore, Core Advanced is natively multimodal, supporting expressive voice synthesis and high-accuracy dictation, making it a true sensory model for the device.
Early benchmarks indicate that AFM 3 is outperforming both its predecessors and key industry competitors in localized intelligence. Specifically, the AFM 3 Core model has demonstrated superior performance in language understanding tasks, outperforming Qwen-2.5-3B and Gemma-3-4B in MMLU and MMMLU scores. This suggests that Apple's vertical integration of software and silicon is yielding higher density of intelligence per parameter.
The server-side AFM 3 Cloud Pro is designed to compete at the highest level of reasoning. While specific SWE-bench scores are pending full public release, early internal testing shows significant improvements in agentic tool use and multi-step logical deduction. The models were evaluated using a rigorous methodology involving standard academic benchmarks (MMLU, HumanEval) alongside Apple-proprietary datasets focused on real-world device interaction and privacy-preserving data synthesis.
A defining characteristic of the AFM 3 release is the expansion of Private Cloud Compute (PCC). For users requiring the heavy lifting of AFM 3 Cloud Pro, Apple has extended its privacy guarantees to the cloud. In a landmark collaboration with Google and NVIDIA, Apple has enabled PCC to run on NVIDIA GPUs within Google Cloud environments.
This ensures that even when a request is processed on a remote server, the data remains cryptographically isolated. User data is never stored, never shared with Apple, and is processed in a secure enclave designed to prevent any unauthorized access, maintaining the same privacy standard as on-device processing.
As of the current release cycle, Apple's focus remains on ecosystem integration rather than a public developer API for the Cloud models. The AFM 3 models are primarily delivered through Apple Intelligence features within iOS, macOS, and iPadOS.
For developers building within the Apple ecosystem, access to these capabilities is provided through the Apple Intelligence SDK and integrated system frameworks. Direct API pricing for AFM 3 Cloud Pro is currently not listed for third-party developers outside the Apple ecosystem.
The AFM 3 family is purpose-built for a wide range of intelligent applications. The on-device models are ideal for low-latency, privacy-sensitive tasks such as smart replies, text summarization, and enhanced Siri interactions. Because Core Advanced is multimodal, it is uniquely suited for voice-driven assistants and real-time transcription services.
The Cloud Pro models are the engine for 'Agentic AI.' These are designed for complex workflows where the model must use tools—such as interacting with calendars, files, or third-party apps—to complete multi-step objectives. This makes AFM 3 a powerhouse for RAG (Retrieval-Augmented Generation) applications where the model needs to reason over large, private datasets securely.
Developers can begin integrating AFM 3 capabilities immediately through the latest updates to the Apple Intelligence SDK. The implementation is designed to be seamless, allowing developers to call specific model tiers based on the complexity of the task and the hardware capabilities of the user's device.
To learn more about implementing multimodal features or utilizing the new agentic capabilities, visit the Apple Machine Learning Research portal or the Apple Developer documentation.