The short decision
Muse Code and Codex both assist with software-engineering work, but their current public product shapes are not identical. Muse Code launches as a Meta terminal agent with direct dollar-per-token rates, persistent background agents, and a replay-safe local event log. OpenAI documents Codex across desktop, web, CLI, IDE extension, and cloud surfaces, with current ChatGPT plans generally consuming credits mapped to token types.
| Decision factor | Muse Code | Codex |
|---|---|---|
| Primary documented surfaces | Terminal agent | Desktop, web, CLI, IDE, cloud |
| Current billing shape | Dollar rates per token type | Plan credits by token type; API-key path also exists |
| Parallel behavior | Persistent background agents documented | Model/mode and agent activity can affect credit use |
| Crash recovery evidence | Replay-safe local event log documented | Check current Codex docs for the chosen surface |
| Best fit signal | Direct cost math + documented runtime design | Broader OpenAI work surfaces + plan integration |
Billing is not one number
Meta currently lists Standard and Contributor rates in dollars per million input, cached-input, and output tokens. OpenAI’s current Codex rate card expresses most plan usage as credits per million token types, with model-specific rows, shared agentic usage pools, and a small legacy Enterprise exception.
Choose Muse Code if…
- You want direct workload estimates from published Muse Code token rates.
- Meta’s documented persistent background-agent pattern suits long, multi-step repository work.
- A local append-only event history and explicit crash-resume design are important to your evaluation.
- You are prepared to evaluate Standard versus Contributor as both a price and data-use choice.
Choose Codex if…
- You want a product documented across desktop, web, terminal, IDE, and cloud contexts.
- Your team already manages OpenAI plan credits, workspace controls, or Codex usage.
- You need current OpenAI integrations such as its IDE or cloud work surface rather than a terminal-only evaluation.
- You prefer to choose among current Codex models and modes inside the OpenAI ecosystem.
What to test side by side
Use the same repository snapshot and acceptance criteria. Track successful change completion, review effort, tool calls, elapsed time, input/output mix, retries, and any manual recovery. Do not reuse vendor benchmark headlines as a substitute for your own workload and policy checks.
For Muse Code cost inputs, use the calculator. For its runtime claims, read subagents and event log and resume. Re-check OpenAI’s linked rate card before presenting current Codex credit figures internally.