Skip to main content
AI Choice Engine

Agentic harness comparison

Muse Code vs OpenAI Codex

Muse Code supports terminal, CI and SDK workflows with Meta subscriptions or separately billed API credentials; Codex supports ChatGPT-plan and API routes. Compare the actual OS/build, permission mode and billing credential. Muse's discounted Contributor terms exclude sensitive inputs, while Standard and subscription access have different data and usage terms.

Details last verified October 2, 2026

Meta

Muse Code

Terminal, CI, and session SDK · Agentic

vs

OpenAI

OpenAI Codex

Desktop, CLI, IDE and cloud · Agentic

AI harness specification comparison
SpecificationMuse CodeOpenAI Codex
VendorMetaOpenAI
Runs inTerminal, CI, and session SDKDesktop, CLI, IDE and cloud
Editor supportAny (editor-agnostic)Terminal beside any editor, VS Code / Cursor / Windsurf, JetBrains, Xcode, Codex desktop, Cloud
AutonomyAgenticAgentic
Pricing modelHybridHybrid
Requires changing editorNoNo

Dates refer to the linked source’s publication or capture unless it explicitly states when the benchmark was measured. Values we could not confirm are shown as “Not verified” rather than estimated.

AgenticHybrid

Muse Code

Muse Code is Meta's coding agent for the terminal and CI, with a session protocol and TypeScript SDK. It supports the Muse Spark family; the current overview and configuration docs still name muse-spark-1.2 as the default, separately from available Spark 1.3. macOS, Linux and Windows are documented, with platform-specific feature limits. Parallel workflows depend on the installed build and rollout.

Best for

  • Terminal and CI coding tasks
  • Piloting enabled multi-agent workflows
  • Teams using the Meta Model API

Watch out

October 2 sources list Everyday $5/month, High $15 (5x) and Power $50 (20x); regional benefits can vary. The subscription-linked CLI credential does not cover additional API keys, which bill pay-as-you-go. Standard Spark 1.3/1.2 token rates are $1.25 input / $0.15 cached / $4.25 output per MTok; Contributor rates are $0.10 / $0.002 / $0.20 with training permission and lower team quotas. Meta forbids confidential, sensitive or personal inputs on Discounted Services. Standard no-training is not zero retention. Auto-review and an OS sandbox are the defaults, with safe commands and workspace writes potentially proceeding automatically. Workflows are unavailable in the documented current Apple silicon macOS artifact; Windows lacks voice and session messaging. Changelog 1.4.2 is a client version with no release date recovered.

AgenticHybrid

OpenAI Codex

OpenAI's coding agent is available in desktop, terminal, supported IDE and cloud surfaces. ChatGPT sign-in uses plan credits and workspace policy; API-key sign-in uses separate Platform billing and organisation policy.

Best for

  • Delegated coding tasks
  • Existing OpenAI commitments

Watch out

ChatGPT sign-in uses Codex plan credits, whose rates differ from API prices. Do not estimate subscription task allowances from Platform token tariffs. Standard GPT-6.1 Sol rates are 50 input / 2.5 cached input / 250 output credits per million tokens; no separate cache-write charge. Plus is $20/month; Pro tiers are $100/$200/$500; Business Standard is $20/user/month with annual billing (two-user minimum), $25 monthly; Enterprise is negotiated. Pro has no five-hour limit, while Plus/Standard Business local-message estimates are not fixed caps; weekly limits and shared Work/Codex usage still apply. Fast mode bills at 2.5x included subscription credits or 2x purchased/pay-as-you-go credits; Astra Ultrafast is 8x or 6x respectively and requires eligible access such as Pro 500. These are billing multipliers, not speed guarantees (https://learn.chatgpt.com/docs/pricing; https://learn.chatgpt.com/codex/agent-configuration/speed). GPT-6.1 Sol access is rolling out on Plus/Pro/Business/Enterprise/Edu; Enterprise/Edu admins must enable it. Free/Go desktop Luna access is rollout-dependent. Model availability varies by account, surface and client version (https://learn.chatgpt.com/docs/models). API-key sign-in supports local desktop/CLI/IDE work and uses Platform billing; cloud features such as GitHub review and Slack require ChatGPT sign-in (https://learn.chatgpt.com/docs/auth). GPT-5.4/mini retired from ChatGPT-authenticated Codex on August 31; GPT-5.5 retirement is scheduled for October 14, 2026, with API availability unaffected. Pick a currently supported Sol/Luna replacement. Linux/WSL2 uses bubblewrap with a user-namespace-dependent bundled fallback; native Windows has its own sandbox and WSL1 is unsupported from CLI v0.115.0 (https://learn.chatgpt.com/docs/sandboxing).

Common questions

Muse Code vs OpenAI Codex

Answered from the verified figures on this page rather than general guidance.

Do I have to change editor to use Muse Code or OpenAI Codex?
Neither does. Muse Code runs in the terminal, ci, and session sdk alongside any editor and OpenAI Codex runs in the desktop, cli, ide and cloud alongside any editor, so people keep the setup they already have.
How are Muse Code and OpenAI Codex priced?
Both are hybrid. The headline seat or plan price is not the heavy-agent bill — included credits, shared pools, and overages dominate once you loop agents hard. Estimate cost per successful task and set spend caps.
Is Muse Code or OpenAI Codex better for large refactors?
Both carry our agentic classification. That label does not establish equal refactoring quality; compare correctness, tool reliability, review time and cost on representative tasks.
Will my project rules transfer between Muse Code and OpenAI Codex?
Usually not automatically. Cursor rules, CLAUDE.md, AGENTS.md, and extension-specific configs are different conventions. Keep one source of truth in the repository (commonly AGENTS.md) and copy or link into each tool’s native format so switching does not wipe months of project teaching.
Is Codex still billed per seat or per message?
No. ChatGPT plans include Codex with plan limits/credits, and API-key login bills Platform tokens separately. A single agentic task can burn many credits — do not budget as if each message were one flat charge.
Do coding agents replace code review?
No. Agents can draft patches faster, but they do not remove the need for human review of plans, tool calls, permissions, diffs, tests, and rollback paths. Treat autonomy as a reason for stronger review, not less — see the coding assistant rollout guide at /ai-harnesses/guides/coding-assistant-rollout-guide.
Should we harden MCP before rolling out a coding agent?
Yes for any agent that can install MCP servers or run tools. Maintain a short allowlist, review each server’s folder and network access before install, fail closed on unknown tools, and log tool calls. .cursorignore and similar rules do not block MCP or shell access once a command is approved.

Next step

Which one fits

Autonomy and editor constraints decide this far more reliably than a benchmark score.

If most of your week is small, frequent edits, an assistive workflow beats delegation and an agentic harness sits idle while billing. If you regularly hand over work spanning many files, the reverse is true.

Where one tool requires changing editor and the other does not, that difference usually outweighs any capability gap across a team — rollouts fail on adoption, not capability.

“Open vendor” links use a first-party redirect for click counting. They are not commissionable partner placements unless a programme is active for that vendor. Affiliate disclosure.