Comparisons

PlanToCode vsCursor Agents

Architectural planning vs editor-first AI

$5 free credits • Pay-as-you-go • Works with your existing tools

Why developers are switching

Cursor agents work file-by-file without architectural view

Full project context with dependency mapping

No review process before changes are made

Generate, review, approve, then execute plans

Limited to editor context and workflows

Terminal-native with broader system integration

Feature	PlanToCode	Indexed codebase context (Codebase Indexing), multi-file edits, Agent Terminal and Shell Mode with approvals/allowlists.
Context Awareness	Full project architecture mapping	Indexed codebase context (Codebase Indexing), multi-file edits, Agent Terminal and Shell Mode with approvals/allowlists.
Execution Control	Plan review and approval workflow	Autonomous edits and terminal execution with review/diff flow and optional approvals/allowlists.
Plan Review	Multi-step plan generation and review	Review/diff UI, checkpoints, and apply workflow for multi-file changes
Cross-File Changes	Coordinated multi-file planning	Agent/tool-driven multi-file edits
Terminal Integration	Native terminal with AI plans	Shared agent terminal and Shell/Agent modes; background agents can run in remote environments (tmux). Persistence behavior depends on agent/editor context.

Compare architectural vs editor-first approach

Show cross-file coordination benefits

Demonstrate plan review workflow

Highlight terminal integration

Hierarchical folder selection, pattern filtering, and AI relevance assessment

Generate multiple implementation approaches using different AI models, then synthesize the best solution

OpenAI GPT‑5 family (GPT‑5 and GPT‑5 Thinking/Pro), historical o‑series (e.g., o3 variants); Anthropic Claude Sonnet 4 and Opus 4.1; Google Gemini 2.5 Pro - availability and features vary by plan and endpoint (ChatGPT vs API).
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Use official vendor docs to confirm features like streaming, function calling, and background mode for each model.