Cursor vs OpenCode: Choosing the Right AI Tool for Daily Coding

The choice between Cursor and OpenCode comes down to two foundational questions: where do you want to review code diffs, and how much direct control does your team need over models, inference routing, and API expenses? The common distinction labeling Cursor as “just an editor” and OpenCode as “just a terminal CLI” fails to capture their actual architectures. According to the Cursor documentation, the platform encompasses not only an editor-centric IDE environment but also a command-line interface and cloud-based agent workflows. Conversely, the open-source OpenCode ecosystem is accessible from the terminal, as a standalone desktop application, and through IDE extensions.

The real technical divide centers on request routing models, command execution mechanisms, and configuration portability.

Request Routing and API Key Control

In Cursor, interactions with external LLMs follow strict operational boundaries. As outlined in the Cursor API key documentation, adding a custom provider API key applies strictly to Chat conversations. In contrast, inline Tab code completion continues to run on Cursor’s proprietary hosted models and will not route through custom user tokens. Furthermore, custom API key support cannot be assumed across all Agent workflows: compatibility between specific agent models and underlying toolsets must be verified individually rather than treated as a blanket feature.

Configuring a custom API key inside Cursor does not establish a direct client-to-provider network connection. Outgoing requests are proxied through Cursor’s infrastructure, where context ingestion and system prompt assembly take place. Additionally, Cursor’s Zero Data Retention policy does not apply when using custom API keys—data handling and retention boundaries are governed by your agreement with the downstream model provider.

OpenCode follows a fundamentally different design. As detailed in the OpenCode providers guide, the tool connects directly to upstream inference APIs using client libraries (such as @ai-sdk/openai-compatible) or local endpoints. Credential storage is cleanly decoupled from configuration: API keys provisioned via the /connect command reside in ~/.local/share/opencode/auth.json, whereas provider definitions are stored in ~/.config/opencode/opencode.json or a project-scoped opencode.json file. While configurations can resolve environment variables dynamically, storing plain-text secrets inside version-controlled project JSON files is strongly discouraged.

When connecting an independent compatible endpoint—such as BetterToken at https://www.bettertoken.ai/v1—the integration workflows diverge significantly:

1. In Cursor (refer to the BetterToken guide for Cursor), the Override OpenAI Base URL toggle is global. It redirects all OpenAI-compatible requests to the specified endpoint, requiring manual toggling whenever you switch back to default upstream endpoints.
2. In OpenCode (refer to the BetterToken guide for OpenCode), a third-party provider is configured either as an isolated block under the provider configuration section/object in your configuration file or interactively using the /connect command.

There is no unified cross-tool subscription: each client demands its own authentication setup, and configuring a custom Base URL in Cursor will not activate Tab code completion.

Project Rules: From .cursorrules to AGENTS.md

Both tools allow engineering teams to codify project conventions directly in source repositories, but their rule structures serve different execution environments.

Cursor relies on rule files (.cursorrules or modular files within .cursor/rules) alongside the MCP protocol. These instructions guide the model to respect repository architecture, code styling guidelines, and the active editor tab context when synthesizing code diffs.

In OpenCode, running /init scans the workspace structure and produces an AGENTS.md file. Because OpenCode’s agent executes commands directly in the shell terminal, AGENTS.md captures concrete operational instructions: build scripts, test suite invocations, and linter commands.

Simply renaming .cursorrules to AGENTS.md is rarely effective. An autonomous terminal agent does not benefit from vague suggestions about code cleanliness; it requires explicit verification criteria, such as the exact test commands needed to validate modifications and a list of protected directories that must not be altered.

Single-Task Benchmark Protocol

To evaluate Cursor and OpenCode objectively for your engineering workflow, conduct a head-to-head evaluation on an actual project task rather than relying on synthetic public benchmarks. To keep the comparison rigorous, establish strict baseline conditions:

• A single, compact repository starting from the exact same Git commit.
• One self-contained task backed by automated tests (for example, implementing a new endpoint complete with input validation).
• Comparable model families and an identical time ceiling per implementation loop.

Track your evaluation metrics using the following comparison matrix:

Evaluation Metric	Cursor	OpenCode
Initial environment setup time (minutes)	recorded during test	recorded during test
Model retry iterations before passing tests	recorded during test	recorded during test
Final diff accepted without manual edits (yes/no)	recorded during test	recorded during test
Manual code edits required (line count)	recorded during test	recorded during test
Total session cost or token expenditure	recorded during test	recorded during test

Running this protocol reveals which tool reliably converges on passing, production-ready code with the least friction inside your existing infrastructure.

Migration Checklist and Total Cost of Ownership

When migrating between Cursor and OpenCode or running them in tandem, follow this technical checklist:

1. Secrets Audit: Ensure local configuration files storing credentials (opencode.json) are added to .gitignore and never committed to source control.
2. Semantic Rule Adaptation: Review the generated AGENTS.md created by /init, stripping away GUI-specific instructions tailored to editor tabs.
3. Environment Security and MCP: Inspect external MCP server permissions inside Cursor settings; when running OpenCode, ensure agent shell execution is constrained to a safe, sandboxed environment.
4. Rollback Baseline: Preserve known-good editor settings and environment variables so your team can revert immediately to the baseline workflow if needed.

Total cost of ownership (TCO) follows distinct commercial models. Cursor pairs a fixed subscription tier with request pools and rate limits, detailed on the Cursor pricing page. OpenCode is open source, and its true cost extends beyond raw API token invoices. Because OpenCode supports local inference, calculating total expenditure depends on your routing choices: costs encompass both external API rates per the OpenCode providers documentation and internal overhead such as local workstation hardware, cloud GPU hosting, electricity, configuration, and ongoing maintenance.

If your team requires a turnkey development environment with interactive visual diff inspection and ambient background code completion, Cursor remains the natural choice. If your priorities center on scriptable workflows, terminal-first autonomy, and transparent, unmediated control over model network traffic, OpenCode provides the more extensible foundation.