modelplan
Optimizes development costs and output quality by matching specific coding tasks to the most efficient model and effort level.
Which AI coding model, at which effort, on which plan? Recommended efforts, a leaderboard at those efforts and plan advice from modelplan.ai, in Claude Code, Codex and the terminal (plugins, Skill, CLI).
- Compare different AI coding models based on specific effort levels
- Get recommendations for the best coding model for your current project plan
- View a leaderboard of coding models to find the most effective option
Optimizes development costs and output quality by matching specific coding tasks to the most efficient model and effort level. This prevents over-provisioning expensive compute while ensuring high-complexity tasks receive sufficient reasoning capabilities.
Engineering managers and DevOps leads optimizing LLM spend and performance across multi-model development workflows.
https://github.com/modelplan/modelplan
By modelplan
How to Get It
claude plugins install modelplan/modelplan
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Help me compare different AI coding models based on specific effort levels
Trust Signals Auto-scanned
Community Pulse Growing
Discussed on Hacker News
- Modelplane – The Open Source Control Plane for AI Inference — Hacker News · 29 pts
- Modelplane — Hacker News · 19 pts
- Modelplane — Hacker News · 4 pts
3 mentions across 1 sources
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 42 GitHub stars; contributors unknown; last commit 0d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
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Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.
Evaluation
Scored from trust signals (evidence-eval-v1): 42 GitHub stars; contributors unknown; last commit 0d ago; license MIT.