Last refreshed 2026-09-02. Next refresh: weekly.
Why use Gemini 3.5 Transcribe on GCP Vertex AI?
GCP Vertex AI offers Gemini 3.5 Transcribe with pay-as-you-go pricing at $12.00/1M output tokens. Vertex AI is Google Cloud's managed AI platform, offering access to Gemini models and hundreds of partner models alongside tools for fine-tuning, grounding, vector search, and end-to-end MLOps pipelines.
Compare Gemini 3.5 Transcribe across 2 providers to find the best fit for your use caseSetup recipe
Python + curlpip install google-cloud-aiplatformexport GOOGLE_CLOUD_PROJECT=...import os
import vertexai
from vertexai.generative_models import GenerativeModel
vertexai.init(project=os.environ["GOOGLE_CLOUD_PROJECT"], location="us-central1")gemini-3.5-transcribeRequest example
import os
import vertexai
from vertexai.generative_models import GenerativeModel
# Reads GOOGLE_CLOUD_PROJECT from env; authenticates via Application Default Credentials
vertexai.init(project=os.environ["GOOGLE_CLOUD_PROJECT"], location="us-central1")
model = GenerativeModel("gemini-3.5-transcribe")
response = model.generate_content("Hello")
print(response.text)Gotchas
- For Google-published models use the model name directly, e.g. "gemini-2.0-flash-001". For third-party publishers (Anthropic, Meta, etc.) use the full publisher path, e.g. "publishers/anthropic/models/claude-3-5-sonnet-v2@20241022".
- The examples expect GOOGLE_CLOUD_PROJECT; rename it only if your application config maps the new variable.
Compare Gemini 3.5 Transcribe Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Google AI Studio | — | $12.00 |
| GCP Vertex AI | — | $12.00 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Output tokens | $12.00 |
| Audio input | $2.50 |
Capabilities
About Gemini 3.5 Transcribe
Gemini 3.5 Transcribe is Google DeepMind's speech-to-text model for audio files. Use gemini-3.5-transcribe for pre-recorded recordings when speaker diarization and word-level timestamps matter; use its documented gemini-3.5-transcribe-live access endpoint through the Live API for continuous, bidirectional streaming transcription. The model accepts audio and text within a 96K-token context and returns text with up to 32K output tokens.