Does Deep Research Pro Preview support zero data retention?

With abuse monitoring, yes. On Google Cloud through the Opper gateway, prompts and outputs are not used for training and are kept only for abuse monitoring.

Where is Deep Research Pro Preview hosted?

Deep Research Pro Preview runs on US hosting routes through the Opper gateway.

Is Deep Research Pro Preview trained on prompts and outputs?

No. Every hosting route for Deep Research Pro Preview is configured so customer prompts and outputs are not used to train models by default.

Is a GDPR DPA available for Deep Research Pro Preview?

Yes. A GDPR Data Processing Agreement is available on at least one route serving Deep Research Pro Preview. Contact hello@opper.ai to receive it.

Deep Research Pro Preview

by Google

Deep Research Pro Preview is an earlier iteration of Google's research-focused agentic line, pairing extended thinking with web search and source synthesis. It runs multi-step research automatically, assesses the quality of what it finds, and assembles structured reports that cite the sources it used. The model works across text and images in addition to documents, letting visual material join a research workflow. Its extended thinking capability supports deeper reasoning about how to plan a search, judge sources, and combine findings, backed by a 128k context window for the underlying material.

Call Deep Research Pro Preview on Opper with the OpenAI SDK. Sign up without a credit card. Get started

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Key info

Input

Output

Features

Context window

131K

Max output

66K

Input price

$2.00 /1M$0.20 cache read

Output price

$12.00 /1M

US residency available
Zero data retention with abuse monitoring
No training by default
GDPR DPA available

Available routes

Deep Research Pro Preview runs on 1 route through the Opper gateway. Compare residency, zero data retention and training posture at a glance, with full data-handling detail per route below.

Provider	Region	Zero data retention	Training	Input	Output	Cache read
Google Cloud	US		No	$2.00	$12.00	$0.20

Uptime and availability

Deep Research Pro Preview runs on a single route through the Opper gateway today, so its availability is that provider's availability.

99.40%route uptime, last 30 days

Single route, no failover target within this model.

Name a second model in the same request and the gateway tries it on retriable errors, so a busy hour never has to reach your users. Set up a fallback chain.

Measured over the last 30 days from each provider's official status feed via StatusGator. Refreshed hourly. See uptime for every provider Opper monitors.

Data handling per route

Each route hosting Deep Research Pro Preview has its own privacy posture, residency, and GDPR terms. Postures are maintained by Opper with a last-verification timestamp.

Google Cloud — United States🇺🇸

Zero data retention with abuse monitoring: prompts and outputs are kept for abuse monitoring only, for a window the provider does not state, and are not used for training. No training on customer data. US; SCCs; DPA available.

Zero data retention: With abuse monitoring, window not stated.
Training: No training on customer data.
Logging: Abuse monitoring
Abuse monitoring: On by default, holds flagged content, 55 days, a person can view samples
GDPR DPA: DPA available
Transfer mechanism: SCCs

Get started

Call Deep Research Pro Preview through the Opper gateway with one API key. Opper is drop-in compatible with the OpenAI, Anthropic and Google AI SDKs.

Deep Research Pro Preview is a premium model on Opper. Sign up needs no credit card: you get an API key straight away and the free models work in the playground and the API. Add a card to use premium models, pay-as-you-go with no minimum.

Set it up with your agent

Copy this and paste it into a coding agent like Claude Code, Cursor or Codex and it'll wire up Opper for you.

Or call it directly

1import OpenAI from "openai";
2
3const client = new OpenAI({
4  apiKey: process.env.OPPER_API_KEY,
5  baseURL: "https://api.opper.ai/v3/compat",
6});
7
8const completion = await client.chat.completions.create({
9  model: "gemini/deep-research-pro-preview-12-2025",
10  messages: [{ role: "user", content: "Hello" }],
11});
12console.log(completion.choices[0].message.content);