Apple Foundation Models 3 vs gpt-oss-120b

Apple · US  |  OpenAI · US · Updated June 2026

Quick verdict

Pick Apple Foundation Models 3 for runs entirely on-device on iphone - no api call, no per-token cost, works offline or the 20b sparse 'core advanced' variant lifted on-device output quality from 23% to 46% user preference in apple's own testing versus the prior generation. Pick gpt-oss-120b for self-hostable on a single 80gb h100 gpu via mxfp4 or configurable reasoning depth (low/medium/high). Choose gpt-oss-120b if you need self-hosting or data privacy; Apple Foundation Models 3 if you want a managed API.

Apple Foundation Models 3 (Apple) and gpt-oss-120b (OpenAI) are two of the models people most often weigh against each other in 2026. Apple Foundation Models 3 is apple's on-device model family - genuinely small context and no API access, but private, offline, and free to run on every iPhone. gpt-oss-120b is openAI's open-weight 117B-parameter MoE reasoning model (5.1B active) that runs on a single 80GB GPU and approaches o4-mini on reasoning, coding, and tool use. They diverge most on context window and open vs. closed weights — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecApple Foundation Models 3gpt-oss-120b
ProviderApple (US) OpenAI (US)
ReleasedJune 2026 August 5, 2025
Context window4K (~6 pages) 131K (~197 pages)
Price (in/out)Not published Open weight (self-host / free)
Open weight?No — API only Yes — self-hostable
Modalitiestext, image text, code
SWE-Bench VerifiedNot published 62.4%
MRCR v2 @ 1MNot published Not published

Who wins what

Runs entirely on-device on iPhone - no API call, no per-token cost, works offline

Apple Foundation Models 3

Apple's on-device model family - genuinely small context and no API access, but private, offline, and free to run on every iPhone — and it is the newer of the two.

The 20B sparse 'Core Advanced' variant lifted on-device output quality from 23% to 46% user preference in Apple's own testing versus the prior generation

Apple Foundation Models 3

Apple Foundation Models 3 lists the 20B sparse 'Core Advanced' variant lifted on-device output quality from 23% to 46% user preference in Apple's own testing versus the prior generation among its strengths; gpt-oss-120b does not.

Deep integration with iOS apps via the Foundation Models framework, now open to third-party LLM providers too

Apple Foundation Models 3

gpt-oss-120b is comparatively weak here — 131K context and 5.1B active params trail the largest frontier closed models

Self-hostable on a single 80GB H100 GPU via MXFP4

gpt-oss-120b

Open weights make this possible at all — Apple Foundation Models 3 is API-only, so it cannot leave the vendor's servers.

Configurable reasoning depth (low/medium/high)

gpt-oss-120b

OpenAI's open-weight 117B-parameter MoE reasoning model (5.1B active) that runs on a single 80GB GPU and approaches o4-mini on reasoning, coding, and tool use — and it carries the larger 131K context.

Agentic tool use, function calling, and code execution

gpt-oss-120b

OpenAI's open-weight 117B-parameter MoE reasoning model (5.1B active) that runs on a single 80GB GPU and approaches o4-mini on reasoning, coding, and tool use — and its weights are open while Apple Foundation Models 3 is API-only.

Largest single-prompt input

gpt-oss-120b

Its 131K window is about 32× larger than Apple Foundation Models 3's 4K, fitting roughly 197 pages in one prompt.

Which should you pick?

Someone analysing very long documents or codebases

gpt-oss-120b

Larger 131K window fits more in one prompt.

A team with data-privacy or self-hosting needs

gpt-oss-120b

Open weights let you run it on your own hardware; Apple Foundation Models 3 is API-only.

Anyone whose priority is runs entirely on-device on iphone - no api call, no per-token cost, works offline

Apple Foundation Models 3

It is specifically built for that.

Anyone whose priority is self-hostable on a single 80gb h100 gpu via mxfp4

gpt-oss-120b

That is its strongest area.

Apple Foundation Models 3: where it fits

Apple's on-device model family - genuinely small context and no API access, but private, offline, and free to run on every iPhone. Released June 2026 by Apple, it is built for runs entirely on-device on iPhone - no API call, no per-token cost, works offline, the 20B sparse 'Core Advanced' variant lifted on-device output quality from 23% to 46% user preference in Apple's own testing versus the prior generation, deep integration with iOS apps via the Foundation Models framework, now open to third-party LLM providers too, and zero marginal cost and strong privacy - nothing leaves the device.

Its trade-offs are real: a genuinely small context window (about 4,096 tokens, shared between input and output) - far below cloud frontier models, not comparable in raw capability to frontier cloud models - built for efficiency and privacy, not benchmark leadership, and no public API pricing since it isn't sold per-token - only usable within Apple's own platforms.

gpt-oss-120b: where it fits

OpenAI's open-weight 117B-parameter MoE reasoning model (5.1B active) that runs on a single 80GB GPU and approaches o4-mini on reasoning, coding, and tool use. Released August 5, 2025 by OpenAI, it is built for self-hostable on a single 80GB H100 GPU via MXFP4, configurable reasoning depth (low/medium/high), agentic tool use, function calling, and code execution, and full chain-of-thought visibility for debugging.

Its trade-offs: text-only, no image, audio, or video input, and 131K context and 5.1B active params trail the largest frontier closed models. As an open-weight model, its running cost is your own hardware rather than a per-token fee.

The bottom line for this matchup

The defining split here is open vs. closed. gpt-oss-120b gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Apple Foundation Models 3 gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.

Want both Apple Foundation Models 3 and gpt-oss-120b without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.

See pricing

Frequently asked questions

Is Apple Foundation Models 3 or gpt-oss-120b better for coding?

Public SWE-Bench figures are not available for Apple Foundation Models 3, so the honest test is your own repository — run an identical real bug through both. By design, Apple Foundation Models 3 leans toward runs entirely on-device on iphone - no api call, no per-token cost, works offline while gpt-oss-120b leans toward self-hostable on a single 80gb h100 gpu via mxfp4, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Apple Foundation Models 3 or gpt-oss-120b?

gpt-oss-120b is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Apple Foundation Models 3 is API-metered at Not published. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.

Which has the bigger context window?

gpt-oss-120b — 131K vs 4K, about 32× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both Apple Foundation Models 3 and gpt-oss-120b together?

Yes — a multi-model platform like LumiChats gives you Apple Foundation Models 3, gpt-oss-120b and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.

Which is newer, Apple Foundation Models 3 or gpt-oss-120b?

Apple Foundation Models 3 — released June 2026, about 10 months after gpt-oss-120b.

Related comparisons

Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.