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Antigravity vs Codex CLI After Six Months in Production — Measured Comparison from Running Six Sites in Parallel

I ran Antigravity and OpenAI's Codex CLI in parallel for six months across six auto-publishing pipelines at Dolice. This is the measured comparison — not benchmark numbers, but the real decision axes that mattered: concurrency, fix cost, recovery behavior, and monthly bill — with the production data behind each.

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Picking between Antigravity and OpenAI's Codex CLI is, in my circles at least, the central agent-selection question of 2026. Benchmark comparisons are everywhere. What I actually wanted to know was different: what happens if you run both for six months on real projects?

I do, as an indie developer at Dolice. Six auto-publishing pipelines across the four AI labs and two long-running content sites (Claude Lab, Gemini Lab, Antigravity Lab, Rork Lab, Lacrima, Mystery). For the past six months I have run Antigravity on some repositories and Codex CLI on others, swapping monthly to see the same task handled by both. This is what came out.

The short version: there is a clear "pick one" axis, and there is a clear "use both" pattern. The article walks through both.

The four repositories and the conditions

  • Site A: Next.js 16 + Cloudflare Workers (one of the AI labs) — TypeScript, ~45,000 lines
  • Site B: WordPress theme + Python auto-publishing pipeline — Python ~12,000 lines, PHP ~6,000 lines
  • Site C: storage-server .htaccess files + image-optimization scripts — Bash/Apache ~2,000 lines
  • Site D: app-business API backend (PHP/JSON delivery) — PHP ~8,000 lines

December 2025 through May 2026. Sites A and C primarily on Antigravity, B and D primarily on Codex CLI, with one swap per month so the same agent saw every repository at least a few times. Repository size, task type, and human-intervention rate kept roughly comparable.

Four indicators I measured

I avoided benchmark-shaped metrics on purpose. I wanted what running them feels like, not pass/fail on synthetic tests:

  1. Task completion rate: I asked for a PR; did I actually get one I could review?
  2. Fix cost per PR: how many human edits did I have to make to land it?
  3. Recovery on failure: when something timed out or errored, how often did the agent recover itself?
  4. Monthly operating cost: actual dollars charged (subscription + API)

Six months of measured values:

| Indicator                | Antigravity                | Codex CLI                |
|--------------------------|----------------------------|--------------------------|
| Task completion rate     | 87%                        | 92%                      |
| Avg edits/PR (manual fix)| 18 lines                   | 11 lines                 |
| Self-recovery rate       | 71% (planning re-plans)    | 54%                      |
| Monthly cost (my usage)  | $40 (subscription)         | $58 (usage-based)        |
| Practical parallelism    | 4 agents max               | 6 agents max             |
| Long-task stability      | strong (30+ min holds up)  | weaker (15+ min needs resume) |

Codex CLI wins task completion by 5 points and PR cleanliness by ~40%. Antigravity wins recovery rate and long-task stability — its planning step seems to act like a self-checking layer that keeps it from going off the rails on multi-step refactors.

Thank you for reading this far.

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WHAT YOU'LL LEARN
See measured numbers from six months of real-project parallel runs: task completion rate, edit-lines-per-PR, self-recovery rate, and monthly cost — laid out side by side
Adopt the decision flowchart for the 'pick one' situation, based on average task length, cost-shape preference, and team size
Take home the shared MCP server design that lets the two agents share tools so you can swap or run both — with the exact tool inventory I run on a single Cloudflare Worker
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