Codex CLI vs Medley: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Codex CLI and Medley — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Codex CLI

OpenAI

Freemium

Codex CLI is OpenAI's lightweight coding agent that runs locally in your terminal to write, edit, and execute code.

Local Terminal Agent: Runs on your own machine and edits files, executes shell commands, and iterates on tasks without leaving the terminal.
ChatGPT Plan Sign-In: Uses your existing ChatGPT Plus/Pro/Business/Edu/Enterprise subscription for auth, so you don't need a separate API key.
Approval-Gated Execution: Prompts you before running shell commands or file writes, giving you control over what the agent touches.
Cross-Platform Binaries: Ships prebuilt binaries for macOS (arm64/x86_64), Linux (x86_64/arm64), and Windows via multiple package managers.
Companion Products: Interoperates with Codex Web (chatgpt.com/codex), the Codex desktop app, and the Codex IDE extensions for VS Code, Cursor, and Windsurf.
Open Source Under Apache 2.0: Fully open source on GitHub so teams can inspect, fork, and self-host the CLI.
API Key Fallback: Optional OpenAI API key authentication for users outside a ChatGPT plan or for CI environments.

Terminal-native Pair Programming: Delegate refactors, bug fixes, and small features to Codex while staying in your existing shell workflow.
Test and Fix Loops: Have Codex run your test suite, read failures, and iterate on patches until green.
Repository Onboarding: Ask Codex to explore an unfamiliar codebase, summarize modules, and answer 'where is X handled' questions.
Shell and DevOps Automation: Draft and run shell scripts, containers, and CI recipes with an agent that can execute them locally.
Cross-Surface Continuation: Start a task in Codex Web or the Codex IDE extension and finish it from the terminal on the same ChatGPT account.

Free

Claude Code plugin that decomposes prompts into coordinated multi-agent plans and visualizes the plan at a shareable URL.

Slash-Command Integration: Activates directly inside Claude Code via the /mission command to produce a plan without leaving the chat interface.
Prompt Decomposition: Breaks a single user prompt into discrete subtasks with clear dependencies to turn vague requests into actionable steps.
Multi-Agent Coordination: Generates a coordinated plan that assigns roles or agent responsibilities and sequences work across multiple agents to tackle complex tasks.
Plan Visualization URL: Renders the produced plan structure at a shareable URL so users can inspect, review, and share the full task graph and execution plan.
Task Assignment & Sequencing: Determines ordering and handoffs between subtasks so parallel and dependent work is organized for execution by different agents.
Shareable Workflow Export: Enables distribution of the decomposed plan via link for collaboration, review, or external execution tracking.
Decomposes a single prompt into a coordinated multi-agent plan
Invoked within Claude Code via the /mission command
Generates a structured plan view accessible at a shareable URL
Orchestrates multiple agents/subtasks rather than relying on a single model
Focus on readable plan structure for inspection and collaboration

Complex Project Breakdown: Converting a high-level product or research brief into a multi-step plan with assigned agent roles and dependencies for coordinated execution.
Multi-step Code Development: Decomposing a feature request into design, implementation, testing, and deployment tasks that can be executed or reviewed by specialized agents.
Data Analysis Pipelines: Breaking down an analysis prompt into data-cleaning, transformation, modeling, and visualization subtasks that are assigned and sequenced.
Content Creation Workflows: Orchestrating ideation, drafting, editing, fact-checking, and formatting steps across different agents to produce polished content.
Collaborative Review & Handoff: Sharing the generated plan URL with teammates or stakeholders to review responsibilities, timelines, and handoffs before execution.
Experiment Orchestration: Designing and coordinating multi-step experiments or research tasks where different agents perform measurements, aggregation, and interpretation.
Breaking complex prompts into executable subtasks for multi-agent workflows
Orchestrating LLM agents to collaborate on a single objective
Sharing and reviewing decomposition and task assignments via a URL
Improving reliability and coverage by distributing work across multiple agents
Prompt engineering for complex, multi-step automation tasks