Sulat.com
AI models
$10 off the fastest DeepSeek V4.1 Flash, Kimi K3 and GLM 5.3 from Synthetic
Poe logo

Model details

gemini-deep-research

Gemini Deep Research functioned as an agentic assistant engineered to handle complex, multi-step research tasks by autonomously browsing the web and integrating personal data from Gmail, Drive, and Chat. Its design intent focused on transforming broad prompts into structured, multi-point research plans, allowing the system to iteratively reason through gathered information. By breaking down intricate inquiries, the model aimed to synthesize findings into comprehensive, multi-page reports that could be delivered in various formats, including audio overviews and interactive visual content.

Built upon the Gemini 3 model architecture, the system leveraged advanced reasoning capabilities to plan and execute research workflows. It evolved to support the generation of custom charts, diagrams, and dynamic simulations, enabling users to forecast outcomes and visualize complex data directly within their reports. The platform was designed to support long-horizon research, with later iterations incorporating native visualizations and improved analytical quality to assist in tasks ranging from budget allocation to scientific exploration.

Poegoogle/gemini-deep-researchdeprecated

Quick Info

Powered by
Provider
Poe
Model key
google/gemini-deep-research
Release date
Dec 11, 2025
Last updated
Dec 11, 2025
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$1.60
Output token cost
$9.60

Limits

Output tokens
0 tokens
Context window
1,048,576 tokens

Latest news about gemini-deep-research

Poe

Coverage

EdTech Innovation Hub's coverage confirms the April 22, 2026 public preview launch of Gemini 3.1 Pro-powered Deep Research and Deep Research Max agents via the Gemini API, emphasizing their role as autonomous research tools that search the open web, user-uploaded files, and connected data sources through MCP servers. T The article reiterates that Deep Research Max runs approximately 160 search queries per task using extended test-time compute, iteratively refining its output, while the standard agent prioritizes speed and cost for interactive products. Named financial data partners FactSet, S&P Global, and PitchBook are building MCP

Videos about gemini-deep-research