Google: Gemini 3.5 TranscribeWork with me

Google: Gemini 3.5 Transcribe

byGoogle

Gemini 3.5 Transcribe is a speech to text model from Google released on Aug 26, 2026. It reads audio and writes text.

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It costs $2 per million input tokens and $12 per million output tokens.

Modalities
In / out price
$2 / $12 per 1M
Context
n/a
Max output
n/a
Released
Aug 26, 2026
Knowledge cutoff
n/a
  • Tool calling
  • Structured output
  • Reasoning
  • Prompt caching
  • Open weights
  • Free to use

Gemini 3.5 Transcribe pricing

USD, as listed on Oct 15, 2026. Most bills are input heavy, so the cached input rate matters as much as the headline price.

Input$2 / 1M tokens
Output$12 / 1M tokens

What Gemini 3.5 Transcribe costs per month

Pick a workload close to yours or type your own volumes.

Gemini 3.5 Transcribe per month
$276
Without caching
$276
Per request
$0.0092

Alternatives to Gemini 3.5 Transcribe

Models that take the same inputs, closest in price, cheaper ones first.

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  • Turns text into spoken audio.

    by googleSep 23, 2026$0.50/M input tokens$6/M output tokens

  • Turns text into spoken audio.

    by googleSep 23, 2026$0.50/M input tokens$9/M output tokens

  • Reads text. Reasoning.

    by googleSep 19, 2026$0.05/M input tokens$0.15/M output tokens

  • Reads text and images. Tool calling, reasoning.

    by googleSep 19, 2026$0.1056/M input tokens$0.3344/M output tokens

All Google models

Gemini 3.5 Transcribe questions

How much does the Gemini 3.5 Transcribe API cost?

$2 per million input tokens and $12 per million output tokens. The estimator on this page turns that into a monthly bill for your own workload.

Is Gemini 3.5 Transcribe open source?

No. Google serves it through an API only, so you cannot download the weights or host it yourself.

When was Gemini 3.5 Transcribe released?

Aug 26, 2026.

Catalog data from models.dev (MIT licensed), refreshed daily, last on Oct 15, 2026. The summary is written from these facts.

Building on Gemini 3.5 Transcribe?

I test your real tasks on it and the two closest alternatives, then ship the agent on whichever wins on cost and accuracy.

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