99languages
Multilingual foundation
Whisper is built for automatic speech recognition and translation to English output.
Local dictation for Mac
Speed up writing, prompting, and AI work with local speech-to-text.
KimiTalk turns spoken thoughts into text directly on your Mac, so you can move faster through notes, prompts, emails, and everyday writing without breaking flow.
Currently v0.9.19 · See all changes →
Time saved calculator
Measure or estimate your typing speed and see how much time speech input can save compared with desktop keyboard entry. The baseline values are backed by published keyboard and speech-rate research.
Your typing speed
Estimate it, enter it, or measure it in a short test.Type freely for 30 seconds. The estimate uses the common WPM standard: 5 characters count as 1 word.
Workflow presets
Basis: 52 words/min typing + 150 words/min speech
Estimated saved time
2.1 hrs saved per weekYour advantage
2.9× faster Speech compared with your typingPrivacy mode
KimiTalk turns speech into text directly on your Mac. The message is intentionally simple: less data movement, more control, and less trust required in external servers.
Why it convinces
Dictation often contains rough thoughts, customer names, prompts, or internal notes. That is exactly why recognition should happen locally before speech becomes text.
KimiTalk is not a cloud voice assistant with a wake word or third-party skills. Recognition runs locally on your Mac, without an external assistant backend path.
Focus
Raw audio Your speech becomes text directly on the Mac, without sending the recording to external servers for recognition.System recommendation
KimiTalk automatically recommends the right transcription model on first launch. For smooth everyday work alongside dictation, we recommend Apple silicon with 16 GB RAM or more. 8 GB RAM is the minimum.
Translation here means Whisper Translate to English output. Turbo models do not support this Translate mode.
Model basis
KimiTalk loads a Whisper model locally on your Mac. The app chooses the variant that fits your device and workflow.
99languages
Whisper is built for automatic speech recognition and translation to English output.
32→4decoder layers
Large-v3-Turbo greatly reduces the decoder stack, making it optimized for fast local transcription.
16 GBrecommended
For smooth dictation while other apps are running, we recommend Apple silicon with 16 GB RAM or more.
Sources: OpenAI Whisper, large-v3-turbo model card.
Basic vs Pro
Basic covers the local dictation and file-transcription workflow. Pro adds history, speaker-aware transcripts, long-form capture, and the local AI layer for shaping text after you speak.
Basic
For users who want private speech-to-text, app switching, mixed-language dictation, file transcription, and cleanup.
€24.90 lifetime licensePro
For users who turn spoken thoughts and long-form audio into structured transcripts, projects, prompts, documents, and multilingual output.
from €6.99/monthTranslation to English output requires a Large-v3 model with Whisper Translate support, such as quantized Large-v3 (~626 MB). Turbo models are optimized for fast transcription and do not support this Translate mode.
Buying options
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€24.90 lifetime license
Local dictation, cleanup style, and updates until the next major version.
€6.99 /month
Basic plus history, 6 Pro rewrite styles, and all future Pro features.
€99 one-time
Pay once and keep Pro. Pays off after roughly two years.
FAQ
Yes. After onboarding, you can test KimiTalk with 1,000 free words. After those words are used, settings and history remain available, while dictation is locked until you activate a license.
Pro includes everything in Basic plus six additional rewrite styles for shaping dictated text after transcription. These styles are meant for turning rough speech into cleaner writing for different contexts, while keeping the local KimiTalk workflow. Future Pro features are included as they ship.
One license is intended for up to three Macs. The app validates signed license tokens locally and only refreshes them occasionally online.