Apple's new SpeechAnalyzer API, benchmarked against Whisper and its predecessor

The result, up front

Apple's new SpeechAnalyzer is the most accurate on-device speech engine we tested. It beat every Whisper model we ship, including Whisper Small, on both the clean and the noisy half of LibriSpeech, while running roughly three times faster than Small. And the API it replaces, SFSpeechRecognizer, came last on clean speech: behind even Whisper Tiny, a 40MB model.

Engine test-clean WER test-other WER Model size Apple SpeechAnalyzer (iOS/macOS 26) 2.12% 4.56% system Whisper Small (WhisperKit CoreML) 3.74% 7.95% ~460MB Whisper Base 5.42% 12.51% ~140MB Whisper Tiny 7.88% 17.04% ~40MB Apple SFSpeechRecognizer (legacy) 9.02% 16.25% system

Lower is better: WER is word error rate, the percentage of words an engine substitutes, drops, or invents. LibriSpeech test-clean is 2,620 utterances of clean read speech; test-other is 2,939 harder, noisier utterances. Every engine ran fully on-device on an Apple M2 Pro (32GB, macOS 26.5.1).

Apple SpeechAnalyzer 2.12% Whisper Small 3.74% Whisper Base 5.42% Whisper Tiny 7.88% SFSpeechRecognizer (legacy) 9.02%

Why we ran this

With iOS 26 and macOS 26, Apple replaced SFSpeechRecognizer with a new API, SpeechAnalyzer and SpeechTranscriber. It published no accuracy figures for either one. So every developer deciding whether to migrate, and everyone comparing Apple's built-in recognition against Whisper, has been guessing.

We ship both Apple engines and three Whisper models side by side in Inscribe, a private on-device AI workspace, which puts us in an unusual position: we can run all five through identical production code paths on the same machine and the same audio. So we did.

Should you migrate off SFSpeechRecognizer?

Yes. This is the clearest result in the data. The new API cuts word error rate by 3.5 to 4x on the same audio: from 9.02% to 2.12% on clean speech, and from 16.25% to 4.56% on noisy speech. There is no accuracy trade-off to weigh; the new API wins everywhere we measured, and it produces punctuated, cased text where the legacy engine's output is rougher.

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