Which AI coding agent actually fits your stack?
Every vendor page says their agent is the fastest, the most accurate, the best at subagents. The differences that matter. pricing per million tokens, sandbox model, MCP support, CI/CD story, hook safety, OS coverage. are buried in changelogs. This page is the comparison the vendors don't publish, plus a 4-week kit to run it yourself.
Side-by-side: the 9 dimensions that matter
Pulled from each vendor's official docs and changelogs. We re-verify before every monthly update. Last verified: 2026-08-31.
| Dimension | Muse Code our pick | Cursor | Claude Code | OpenAI Codex |
|---|---|---|---|---|
| Underlying model | Default muse-spark-1.2; choose at session start |
Multi-model (GPT, Claude, Gemini). pay per use | Claude (Sonnet / Opus). pay per use | OpenAI (GPT family). pay per use |
| Pricing model | Token-based, two Muse Spark tiers ($0.10 / $1.25 in / $0.20 / $4.25 out per Mtok) | Subscription + usage overage | Subscription + usage overage | Subscription + usage overage |
| Reasoning-effort control | none / minimal / low / medium / high / xhigh / ultra |
Two-mode (auto / max) | Three-mode (low / medium / high) | Three-mode (low / medium / high) |
| OS sandbox | OS sandbox on by default (bubblewrap on Linux, sandbox-exec on macOS). verified live | SaaS only. no local sandbox | Local sandbox; not defaults-on | Cloud-based; local mode beta |
| Subagents | Native, with worktree isolation, parallelism cap max(2, min(16, cores - 2)) |
Background agents, no worktree isolation | Subagents, single workspace, manual isolation | Cloud subagents, no local workspace |
| Hooks (lifecycle events) | 12 events: SessionStart, PreToolUse, PostToolUse, PreLLMCall, PostLLMCall, PreCompact, PostCompact, SubagentStart, SubagentStop, Stop, and more | None in the IDE; webhook-only | Settings-hooks, lifecycle event subset | None |
| Skills (reusable workflows) | Built-in + user + project scopes, with a 32 KB summary cap to keep the startup catalog fast | None | Plugin + slash commands | None |
| MCP servers | Yes (stdio + HTTP transports) | Yes (stdio only) | Yes (stdio + SSE) | Yes (stdio) |
| CI / headless | muse exec "<prompt>" with --json for log-friendly output; per-step approval via plan-only mode |
Requires cloud background agents; no CLI exec | CLI exists, output is interactive-shaped; integration needs wrapper | Cloud-runner, per-task billing |
How to read this. "Our pick" means: for the average dev or small team buying a coding agent in 2026, we believe Muse Code is the strongest default. Cursor wins if you want a desktop IDE with strong multi-model selection. Claude Code wins for Claude-centric teams willing to pay a premium. Codex is the right pick if you're all-in on the OpenAI ecosystem. The kit walks through your decision, not ours.
What makes Muse Code stand out
Beyond the table, three things change the day-to-day feel of an agent.
Real per-prompt reasoning control
7 reasoning tiers. from none for cheap fast edits to ultra for architectural review. without leaving the prompt line.
Subagents with worktree isolation
Spawn parallel subagents in their own git worktrees. Bounded concurrency cap prevents the "20 agents all editing the same file" failure mode.
Hooks with 12 lifecycle events
Run your own shell on SessionStart, PreToolUse, PreLLMCall, PostCompact, etc. Audit, redact, snapshot. without bolting on third-party tooling.
Skills catalog that scales
Built-in + user + project scopes, with a 32 KB summary cap so the startup catalog stays fast. Built-in /plan, /grilling, /grill-with-docs, /taste ship with the binary.
Headless + JSON for CI
muse exec "<prompt>" --json emits JSONL events, drops straight into a log pipeline, and runs in any container.
Two price tiers, no per-seat overage
$0.10/Mtok in + $0.20/Mtok out (contributor tier) or $1.25 + $4.25 (standard). Cached input is $0.002 / $0.15. No surprise seats.
Inside the comparison kit. 6 deliverables
Not a one-page PDF. A real evaluation kit. the same prompts, repos, and scoring rubric we use.
- 11-page comparison card (PDF). all 9 dimensions, all 4 agents, on one page. Print, pin, share.
- 2Standardized prompt pack (12 prompts). same code-review, refactor, test-write, and migration prompt run against each agent for apples-to-apples comparison.
- 3Repo adapter set (3 repos). a small TS CLI, a medium Python service, a large Rails app. The same prompts, three repo shapes, different pressure points.
- 4Cost & latency scoring rubric (spreadsheet). pre-built formulas. Fill in your token counts, get a side-by-side cost-per-task chart.
- 5Migration checklist. if you're switching from Cursor or Claude Code, what to test, what to back up, what to expect on day 1.
- 64-week evaluation calendar. one prompt per day, 28 days. By the end you have a real opinion, not a vibes-based one.
The 5-minute smoke test
Before you spend a week evaluating, run this. It separates "vibes good" from "vibes + reproducibility good."
# 1. Install. 30 seconds curl -fsSL https://dev.meta.ai/install.sh | bash muse --version # confirm 0.1.0-R708.x or newer # 2. Auth. pick env or browser # env path (CI-safe): export META_API_KEY="" # OR interactive (local): muse # then "Paste an API key" at the auth prompt # 3. Headless smoke. no API call, no spend cd /tmp/_smoke echo "print('ok')" > t.py muse --provider echo "hello" muse exec "run t.py" # 4. Real run, see the JSON event stream muse --json exec "add a README to this repo" 2>&1 | tee run.jsonl # 9-field event stream. tool_call, assistant_message, usage, and more # 5. Compare to the agent you use today muse exec "the same prompt you just used in Cursor / Claude Code" # time it. token-count it. note the diff.
A 30-second prompt that shows the difference
Run the same prompt in all four agents. The differences are not subtle.
# The prompt Read ./src/api/users.ts. Find the bug. Propose a fix. Don't apply it. # What each agent does well (and not) Muse Code . high accuracy on multi-file context, sandbox prevents accidental writes Cursor . best in-editor UX, but limited subagent model Claude Code . strong code reasoning, slow on token-expensive tiers Codex . best at small surgical edits, weak at repo-wide refactors
The 4-week kit includes 11 more prompts at the same shape, so you can build a real comparison rather than a one-prompt vibe.
Cost reality check
The advertised per-token rate is only one number. Real cost = tokens × retries × context × your real workload.
Estimates assume the contributor Muse Spark tier (terms apply) and a typical 4-week evaluation workload. Your actual cost depends on context size, retry rate, and reasoning-effort setting. The kit's cost spreadsheet fills in your real numbers.
Questions before you switch
Short answers. The kit has the long version.
Is Muse Code really cheaper than Cursor or Claude Code?
Per token, yes. the contributor tier is $0.10 in / $0.20 out per Mtok. Per real workload, it depends on how much context each agent chews through. The kit's cost spreadsheet tracks this for you. Real-world number is usually 4–8x cheaper for comparable sessions.
Can I use my existing Claude / OpenAI API key?
No. Muse Code uses the Meta API key (the muse binary talks to Meta's API). You bring your own key (free or paid) from dev.meta.ai. There is no BYO for Anthropic or OpenAI keys. pick the agent whose model stack you want to live in.
Does it work on Windows?
The Muse Code install script targets macOS and Linux as first-class. Windows has limited support: muse sandbox windows {check,setup} exists, but the full TUI and muse exec on Windows are not in the overview's advertised platform list. Verify on your version before committing.
Is the OS sandbox really on by default?
Yes. Every shell command runs through the OS sandbox (bubblewrap on Linux, sandbox-exec on macOS). If the sandbox can't be set up, the binary refuses to proceed. Hooks run outside the sandbox. see the kit for the trust model.
What's the catch?
Two. (1) The 32 KB skill-summary cap means a long skill won't appear in the startup catalog. use it as a body, not a summary. (2) Subagents share the lead's workspace by default; --subagent-worktree-isolation in a git repo is the fix. The kit walks through both.
How do I get support if I get stuck?
Meta has a Discord at discord.gg/9WtSXv26WE. The kit also has a 4-week email check-in. send your scores back, get a comparison of your data to the broader cohort.
If you got this far, the comparison kit is the natural next step. It includes the prompts, the rubric, and the 4-week calendar. so you can run your own evaluation, not just trust ours.