Author here. I ship both Apple speech engines plus WhisperKit side by side in a transcription app, which made it possible to run all five through identical production code on the same audio: LibriSpeech test-clean and test-other, 5,559 utterances, fully on-device on an M2 Pro.

Apple published no accuracy numbers for SpeechAnalyzer (or for SFSpeechRecognizer, ever, as far as I can tell), so the migration question has been guesswork. Short version: the new API cuts WER 3.5-4x vs the old one (2.12% vs 9.02% on test-clean), and it also beat Whisper Small on both splits at about 3x the speed. The old API came in last on clean speech, behind even Whisper Tiny.

On "why should I trust a vendor benchmark": the Whisper column reproduces OpenAI's published LibriSpeech WERs within +0.11 to +0.42 on all six measurements (same corpus, same normalizer, same scorer for every engine), and the raw per-utterance transcripts are downloadable from the article if anyone wants to rescore with their own normalizer.

Limitations worth stating up front: English only, read speech rather than meeting audio, one machine. Precise per-engine timing isn't in the article yet because the accuracy runs shared the machine with a dev workload; WER is load-independent, timing isn't.

Two things that might interest people migrating: SFSpeechRecognizer sends audio to Apple's servers unless you set requiresOnDeviceRecognition, and with SpeechAnalyzer, finishing your input stream is not enough to end a session. If you never call finalizeAndFinishThroughEndOfInput(), the results sequence never terminates and your await hangs forever. I found that one because it was shipping in my own app.

Happy to answer questions about the harness or the normalizer.

At this point, I would not recommend ignoring Parakeet TDT 0.6b v2/v3 (english-only versus multilingual). Those models have been out for a year, give or take, and they are both accurate and fast. I would choose Parakeet over Whisper in almost all situations these days. Parakeet works great even on my several year old iPhone 15 Pro Max, so if an app is going to ship a dedicated model, I strongly recommend investigating Parakeet.

On the more cutting edge front, Granite Speech 4.1 has proven to be a reliable workhorse for me, but it is larger than Parakeet. Cohere Transcribe is interesting, but how strong it is seems to vary more from task to task.

Parakeet Unified 0.6B came out a few months ago, combining both online streaming and offline transcription into one model, and that is one that I need to test more, but it seems promising.

As others have mentioned macOS 27/iOS 27 is supposed to have a new model, particularly on devices with 12GB of RAM or more. I have not actually seen the option to enable that new model yet, though, despite being on the beta on a device that meets the requirements. Maybe a benchmark would reveal that it is already active?

Why do you not use Whisper large models when on macOS? They're still fast even when streaming and yield a much lower WER.

Also, just out of curiosity, seems like everyone and their mother is making Whisper wrappers, how is your app different?

Why use relatively ancient models like whisper and not e. g. parakeet?

Please run your benchmark on this new and very impressive model https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-Diarize in my testing it outperforms all mentioned especially on noisy audio

MOSS-Transcribe-Diarize