OpenAI Codex vs Continue

Detailed side-by-side comparison to help you choose the right tool

OpenAI Codex

🔴Developer

Developer Tools

OpenAI Codex is a coding agent from OpenAI for local CLI work, IDE workflows, cloud tasks, code generation, debugging, and pull-request support.

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Starting Price

Included with eligible ChatGPT plans; exact limits need verification
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Continue

🔴Developer

Developer Tools

Open-source AI coding assistant for VS Code and JetBrains — bring any model, any MCP server, and share reusable rules and agents across your team via Continue Hub.

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Starting Price

Free
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Feature Comparison

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FeatureOpenAI CodexContinue
CategoryDeveloper ToolsDeveloper Tools
Pricing Plans2 listed plans4 listed plans
Starting PriceIncluded with eligible ChatGPT plans; exact limits need verificationFree
Key Features
  • • Local Codex CLI coding agent that runs on the developer’s computer
  • • Install options documented for Mac, Linux, Windows, npm, Homebrew, and GitHub release binaries
  • • IDE path for VS Code, Cursor, and Windsurf plus a Codex Web path for cloud-based agent work
  • • Multi-model AI support including OpenAI, Claude, Gemini, and local models
  • • Native IDE extensions for VS Code and JetBrains with smooth workflow integration
  • • MCP server connectivity for development toolchain integration

OpenAI Codex - Pros & Cons

Pros

  • ✓Official README confirms local CLI, IDE, desktop-style, and Codex Web workflow options
  • ✓Fits teams already using ChatGPT plans or OpenAI APIs for engineering work
  • ✓Strong candidate for testable, issue-sized tasks where CI and human review can catch mistakes

Cons

  • ✗OpenAI homepage and pricing page were blocked by JavaScript/cookie challenge, so plan limits and prices require manual verification
  • ✗Generated code still needs review, tests, and security checks before merge
  • ✗Broad repository permissions or deployment access would be risky without admin controls and audit policy

Continue - Pros & Cons

Pros

  • ✓Open-source assistant for VS Code and JetBrains. This directly supports the primary workflow.
  • ✓Bring-your-own-model: Anthropic, OpenAI, Ollama, local, any provider.
  • ✓First-class MCP client — any MCP server becomes callable tools.
  • ✓Continue Hub for sharing assistants, rules, prompts across a team.

Cons

  • ✗At least one tier requires a sales quote, preventing a complete public budget calculation.
  • ✗Features and limits can change by plan, billing interval, region, and release; verify current documentation.
  • ✗MCP connectivity expands the permission surface and requires narrow scopes, approvals, logs, and credential rotation.
  • ✗A representative pilot is necessary because a feature list does not establish reliability or operational fit.

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