Local vs Cloud Dictation: Trace Where Your Audio Goes

The label "local dictation" can describe one processing stage while another stage still uses a hosted service. Compare the route taken by audio and text rather than choosing from a slogan. This is a documentation-based comparison method, not a speed, accuracy or privacy benchmark.

List speech recognition, which turns audio into text, and any language-model rewriting, which edits that text. For each selected model, write down local or cloud execution, network needs and the provider. Superwhisper supports local and cloud model choices; inspect the specific speech and language models selected in your configuration.

A local path may suit offline practice after required downloads, while cloud processing may support models your device cannot run comfortably. Hardware, language and plan conditions affect the choice. If speech recognition is local but rewriting is cloud-based, the resulting text still leaves the device. Do not describe that mixed path as fully offline.

Use the same fictional 30-second paragraph containing an unusual name, one amount and a negation. Compare the resulting words manually, and record which settings were active. An offline check can reveal a network dependency, but success without a network connection is not proof that every telemetry or retention path has been audited.

Columns: speech route; rewrite route; offline result; wrong words; device suitability; account conditions. Fill values from settings and actual sample observations. Leave unknown retention behavior as unknown until you read the applicable provider policy. Do not infer cloud cost from the local software license or publish an unmeasured accuracy percentage.

Changing a model or mode can change the route. Save your chosen settings with the sample result and review them after updates. For everyday nonsensitive drafting, pick the route whose behavior you understand. For data you cannot send to a provider, avoid cloud stages instead of relying on a prompt asking the service to keep it local.

Reviewed on 11 October 2026. Worked examples are fictional; these workflows are based on documentation, not product benchmarks.

• Superwhisper documentation: local and cloud models
• Superwhisper introduction