Speech to text on a Mac used to mean one of two things: Apple's built-in dictation or Dragon NaturallySpeaking. Apple's version was limited but free. Dragon was powerful but expensive, and then Nuance killed the Mac version entirely. For years after that, the options were bleak.

That's no longer the case. Apple Silicon changed the game by putting a Neural Engine in every Mac — dedicated hardware for running machine learning models locally. A new wave of speech-to-text tools has emerged that takes advantage of this hardware, and some of them are genuinely good. Here's a practical breakdown of what works, what doesn't, and what to use depending on your needs.

Apple Dictation: free but frustrating

Apple's built-in dictation is the obvious starting point. It's free, it's pre-installed, and on Apple Silicon Macs it runs partially on-device. Go to System Settings > Keyboard > Dictation, toggle it on, and double-tap the Fn key to start speaking.

For short dictation — a quick search, a one-line reply — it works well enough. But the problems emerge fast once you try to use it for real work:

Apple Dictation is a baseline, not a solution. It proves speech-to-text works on your Mac. It just doesn't work well enough for the people who need it most. For a deeper look at what you're missing, see our breakdown of how to dictate on Mac without Siri.

Cloud tools: powerful but problematic

Cloud-based speech-to-text apps send your audio to remote servers for processing. The appeal is straightforward: bigger models, more compute power, more features. Tools like Wispr Flow and Otter.ai fall into this category.

Wispr Flow is the most polished of the bunch. It transcribes speech and can rewrite output to match the context of the app you're typing in — more formal in email, more casual in Slack. Otter.ai focuses on meeting transcription and offers AI-generated summaries.

The tradeoffs are real, though:

For teams that need meeting transcription and don't handle sensitive data, cloud tools can make sense. For individual dictation — the "hold a key and talk" workflow — the privacy and cost tradeoffs rarely justify the benefits.

Local speech-to-text: the new default

The most significant shift in speech-to-text on Mac is the move to fully local processing. Thanks to Apple Silicon's Neural Engine and frameworks like CoreML, speech recognition models can now run entirely on your Mac with quality that rivals cloud services.

Several apps have emerged in this category. Here's how they differ:

SuperWhisper

Built on OpenAI's Whisper model, SuperWhisper offers local transcription with multiple model sizes. Larger models are more accurate but slower. It's a capable tool with a solid community. The subscription pricing ($10/month or $100/year) and the need to configure model sizes make it better suited for technical users who want fine-grained control.

MacWhisper

Also Whisper-based, but designed primarily for file transcription rather than live dictation. Excellent for transcribing recorded meetings, interviews, and voice memos. Not ideal if what you want is system-wide "talk and it types" dictation.

Voiced

Voiced is a speech-to-text app for Mac built for live dictation. It uses Apple's CoreML framework to process speech on-device, so there's no cloud dependency, no model configuration, and no account required. The key differentiators:

For a full comparison of all the options, see our guide to the best voice-to-text apps for Mac in 2026.

Which approach should you use?

It depends on what you're doing:

The speed advantage is real: most people speak at 150 words per minute but type at 80. Switching to voice for your first drafts, emails, and messages can genuinely cut your writing time in half. The only question is which tool you use to get there.

Try speech-to-text that actually works.

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