Skip to content
GoogleVoice

Gemini 3.5 Transcribe

gemini-3.5-transcribe

Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.

Context
98.3K tokens
Endpoint

Get API KeyCompare

Pricing

Input—
Output—
Cache Write (5m)Not applicable
Cache Write (1h)Not applicable
Cache ReadNot applicable
Web Search$0 / 1M

Customer audio pricing

USD

Base rates shown at GroupRatio=1. Your account's group multiplier may change the final charge.

/v1/audio/transcriptionsSpeech to text
RateUSD per unit
InputAudio$0.000002 USD per token
OutputText$0.000012 USD per token

Cached input tokens are charged at the full configured customer input rate.

Actual settled usage is deducted from your Apertis credit quota.

Quick Start

Select an endpoint and copy a working example for this model.

Endpoint
python
from openai import OpenAI client = OpenAI(    api_key="YOUR_API_KEY",    base_url="https://api.apertis.ai/v1") response = client.chat.completions.create(    model="gemini-3.5-transcribe",    messages=[        {"role": "user", "content": "Hello!"}    ],    max_tokens=1024,    temperature=0.7) print(response.choices[0].message.content) # Optional: Enable context compression to reduce token usage# response = client.chat.completions.create(#     model="gemini-3.5-transcribe",#     messages=[{"role": "user", "content": "Hello!"}],#     extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )

Supported Parameters

API docs
Common5 params
modelfilelanguagepromptresponse_format
Extended2 params
temperaturetimestamp_granularities

Cursor IDE Model IDs

Use these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.

gemini-3.5-transcribe

Questions about Gemini 3.5 Transcribe

What is Gemini 3.5 Transcribe's context window?

Gemini 3.5 Transcribe has a context window of 98.3K tokens.

Which company develops Gemini 3.5 Transcribe?

Gemini 3.5 Transcribe is developed by Google.

Which model ID do I use to call Gemini 3.5 Transcribe?

Use the model ID gemini-3.5-transcribe to call Gemini 3.5 Transcribe.

Compare with Other Models

See how this model compares to others from the same provider.

Gemini 4 Hydrogen

Gemini 4 Hydrogen is Google's high-efficiency multimodal model in the Gemini 4 family, optimized for high-throughput agentic workflows, coding, and multi-model routing. It delivers strong performance at lower cost and latency than Gemini 4 Carbon, supporting text, image, audio, video, and file inputs for versatile multimodal applications. Designed for scalable production workloads and long-horizon execution, Gemini 4 Hydrogen supports up to 1M output tokens through continuation and configurable reasoning effort levels (low, medium, high, and xhigh) to balance reasoning depth, speed, and cost. It is well suited for coding assistants, autonomous agents, complex workflow automation, and large-scale AI systems.

Context
1M
Input
$1.50/M
Output
$7.50/M

Gemini 4 Carbon

Gemini 4 Carbon is Google's frontier multimodal reasoning model, designed for advanced coding, agentic workflows, and complex multi-step problem solving. It supports text, image, audio, video, and file inputs, enabling integrated reasoning and analysis across diverse modalities. Optimized for long-horizon execution and demanding professional workloads, Gemini 4 Carbon supports up to 1M output tokens through continuation and configurable reasoning effort levels (low, medium, high) to balance reasoning depth, latency, and cost. It is well suited for autonomous agents, software engineering, multimodal analysis, and large-scale workflow automation.

Context
1M
Input
$2.00/M
Output
$10.00/M

Gemini Nano Banana 2.1 (Google AI Studio)

Gemini Nano Banana 2.1 served from Google AI Studio. Billed per image at US$0.0336 (up to 1K).

Context
N/A
Input
— (Not priced per input token)
Output
— (Not priced per output token)

Gemini 4 Argon

Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.

Context
1M
Input
$2.00/M
Output
$10.00/M