Perplexity Deep Research
Perplexity
4.4
Multi-step research agent that runs dozens of searches and returns a cited report instead of an answer.
Research AgentsFreemiumCloudAutonomous
Gemini Deep Research
Google's planning-first research agent that drafts a search plan you approve before it starts working.
4.3
by GoogleUpdated Overview
Deep Research inside Gemini turns a question into an explicit research plan, waits for your edits, then browses and reads across many sources before writing a long structured report. The plan-approval step is the distinguishing feature: you steer scope before compute is spent rather than after the report disappoints.
Key capabilities
- Editable research plan
- Long-context report synthesis
- Source list with links
- Export to Google Docs
- Follow-up refinement
- Audio summary output
Strengths
- Approving the plan before execution prevents whole runs wasted on the wrong interpretation
- Very long context window supports reports that hold many sources together coherently
- Export into Workspace removes the copy-paste step for teams already in Google Docs
Limitations
- Reports run long and often need aggressive editing to reach a usable length
- Citation density is lower than rivals, so tracing a specific claim can be slow
- Plan editing is coarse — you can redirect scope but not pin specific required sources
The plan step is the design decision worth studying. Most research agents commit to an interpretation of your question silently, and you discover the misread only in the finished report. Gemini surfaces that interpretation as an editable list of sub-questions first.
Everything after that is a fairly standard browse-read-synthesize loop, distinguished mainly by context length. Because the model can hold a large volume of retrieved text at once, the synthesis stage compares sources against each other rather than summarizing them one at a time — which shows up as better handling of topics where sources conflict.
Where it fits: scoping an unfamiliar domain, literature-style surveys, and briefing documents destined for Google Docs. For claim-by-claim verifiability, a more citation-dense agent is the better tool. See Gemini vs Claude for how the underlying models compare.
Similar options
Claude Code
Anthropic
4.7
Terminal-native coding agent from Anthropic that reads, edits, and runs code directly inside your repository.
Coding AgentsPaidTerminalSupervisedMCP
LangGraph
LangChain
4.6
Graph-based orchestration framework for stateful agents with explicit control flow and durable execution.
Agent FrameworksOpen SourceHybridSupervisedMCP
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