
gemini-3-6-flash
Gemini 3.6 Flash via Vertex AI — efficient workhorse model with improved coding, knowledge work, multimodal performance, and 17% fewer output tokens than 3.5 Flash.
api reference
about
gemini 3.6 flash via vertex ai — efficient workhorse model with improved coding, knowledge work, multimodal performance, and 17% fewer output tokens than 3.5 flash.
1. calling the api
install the client
the client provides a convenient way to interact with the api.
1pip install inferenceshsetup your api key
set INFERENCE_API_KEY as an environment variable. get your key from settings → api keys.
1export INFERENCE_API_KEY="inf_your_key"run and get result
submit a request and wait for the final result. best for batch processing or when you don't need progress updates.
1from inferencesh import inference23client = inference()456result = client.run({7 "app": "google/gemini-3-6-flash",8 "input": {}9 })1011print(result["output"])stream live updates
get real-time progress updates as the task runs. ideal for showing progress bars, partial results, or long-running tasks.
1from inferencesh import inference23client = inference()456# stream=True yields updates as they arrive7for update in client.run({8 "app": "google/gemini-3-6-flash",9 "input": {}10 }, stream=True):11 if update.get("progress"):12 print(f"progress: {update['progress']}%")13 if update.get("output"):14 print(f"output: {update['output']}")2. authentication
the api uses api keys for authentication. see the authentication docs for detailed setup instructions.
3. files
file inputs are automatically handled by the sdk. you can pass local paths, urls, or base64 data.
automatic upload
the python sdk automatically detects local file paths and uploads them. urls are passed through as-is.
1# local file paths are automatically uploaded2result = client.run({3 "app": "google/gemini-3-6-flash",4 "input": {5 "image": "/path/to/local/image.png", # detected & uploaded6 "audio": "https://example.com/audio.mp3", # url passed through7 }8})4. webhooks
get notified when a task completes by providing a webhook url. when the task reaches a terminal state (completed, failed, or cancelled), a POST request is sent to your url with the task result.
1result = client.run({2 "app": "google/gemini-3-6-flash",3 "input": {},4 "webhook": "https://your-server.com/webhook"5}, wait=False)webhook payload
your endpoint receives a JSON POST with the task result:
1{2 "id": "task_abc123",3 "status": 9,4 "output": { ... },5 "error": "",6 "session_id": null,7 "created_at": "2024-01-15T10:30:00Z",8 "updated_at": "2024-01-15T10:30:05Z"9}5. schema
input
the context size for the model.
temperature
top-k sampling. -1 to disable.
top p
model
min p
frequency penalty
presence penalty
repetition penalty
seed
stop
max tokens
reasoningeffortenum
reasoning max tokens
system prompt
context
chatmessagerole
text
reasoning
attachments
images
files
tools
toolchoice
responseformat
tool call id
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