M
modelplan/modelplan
Which AI coding model, at which effort, on which plan? Recommended efforts, a leaderboard at those efforts…
View on GitHub ↗Link checked 2026-11-12 · HTTP 200 · opens the vendor's own site
Install & config
Client config (built from the install command this repo gives)
{
"mcpServers": {
"modelplan-modelplan": {
"command": "npx",
"args": ["skills"]
}
}
}Parsed from the README we fetched on 2026-11-04; we have not executed it.
command/args come from the install command the repository states. When it is ambiguous, the project README wins.
VS Code (.vscode/mcp.json - the key is servers)
{
"servers": {
"modelplan-modelplan": {
"command": "npx",
"args": ["skills"]
}
}
}Same server, but VS Code keeps it under <code>servers</code>.
Claude Code (command line)
claude mcp add --transport stdio modelplan-modelplan -- npx skills
Claude Code does not read a config file; add the server with this command.
Key names differ by client: Clients & CLI
- Star count
- 41 ★ · modelplan/modelplan · read from the GitHub search API · (checked 2026-11-13 20:00:50)
- Source
- read directly from its GitHub page · type: open-source project
- Harvest history
- first seen 2026-11-05 · last seen 2026-11-14 · seen 10× · listed by 1 sources
- Score
- score 103 · 41 ★ · 1 sources · repo
- Install
npx skills
FAQ (answers come from our records)
How do I install it?
The repository provides this install command:
npx skills. It comes from the repository itself; we cannot guarantee it runs as-is in your client.Who is it for?
We filed it under these tags: Agent 技能. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
repository
modelplan/modelplan；first catalogued 2026-11-05；last seen 2026-11-14。There is no official certification or quality rating here — we record sources and dates only.Which AI coding model should you use, at which reasoning effort, and is one more
以下摘录自该仓库的 README（片段） · 在 GitHub 读完整 README →
ModelPlan answers from live, checkable public data, on the web and inside Claude Code, Codex and your terminal.
Website · Quick start · Screenshots · How it works · FAQ · Methodology · 简体中文
## Why ModelPlan
Choose an entry point: Mac App, plugins or Skill.
Most leaderboards compare every model at its top setting. But inside one model, going from Low to Max often costs several times more and takes several times longer, for a few points of ability. What you actually run is a model at an effort.
★ Recommended effort. Every model gets its best-value effort, and you see exactly what one more step up buys (+4.8 ability) and costs (×1.37).
A leaderboard at that effort. Models are ranked at their own ★ effort, the setup you would really use, with cost per reference coding task and task time beside ability. Outclassed models (costlier and no stronger) are called out, and if you are on one, the CLI tells you once what to switch to: Fable 5.1 → Opus 5.5 (+2.5 points at ×0.52), GPT-6 Astra → GPT-6.1 Sol (about the same at ×0.18).
Your own quota. The Skill and CLI read your local Claude Code and Codex 5-hour and weekly windows (never uploaded). The CLI also reads ZCode's and OpenCode's (V2) local databases, read-only, for tokens per day, session and project. The CLI also keeps a local history of how much of each window you used and under which model and effort (modelplan history), the basis for suggesting a default effort from your own weeks, and measures your own speed per model, effort and Fast: output tok/s, time to first token, turn time and tokens, against ★ (modelplan effort, modelplan history; read from the harness logs' timestamps and token counts only). Plenty left before a reset? Step up, or turn on Codex Fast. Tight, or a recent burst that would use it up within three hours? Stay at ★ or step down, with the time it runs out. After a reset, it reminds you once to go back to the model and effort you used before (and to turn Codex Fast off).
Gemini CLI local usage (optional). modelplan usage gemini enable allows read-only tokens/model/day/session accounting from Gemini’s own local chat history. modelplan usage gemini displays it; disable stops it. No credentials or remote quota are read, and no host is called. Missing counters stay --.
Other plans (optional). modelplan connect zai reads Z.ai / GLM Coding Plan usage natively, with the API key you keep in ZAIAPIKEY (--region bigmodel-cn for open.bigmodel.cn), modelplan connect opencodego reads OpenCode Go with OPENCODEAPIKEY, and modelplan connect kiro asks Kiro's own CLI (kiro-cli acp, no prompt sent; Kiro keeps its sign-in). Each key is sent only to its vendor's usage endpoint and never stored; modelplan connections shows which reader serves each plan. For the rest, modelplan codexbar enable cursor zai reads Copilot, Cursor, Z.ai / GLM, Kiro or OpenCode Go usage through the open-source CodexBar CLI, fetched at a pinned, checksum-checked release (macOS and Linux only). CodexBar keeps the sign-ins; this client keeps only percentages and reset times. Tell it which plan pays (modelplan declare zhipu-global-pro legacy-v2) and advice follows how that plan counts: on a per-request plan it never says step down, and points to the next off-peak hours instead.
Website · Quick start · Screenshots · How it works · FAQ · Methodology · 简体中文
## Why ModelPlan
Choose an entry point: Mac App, plugins or Skill.
Most leaderboards compare every model at its top setting. But inside one model, going from Low to Max often costs several times more and takes several times longer, for a few points of ability. What you actually run is a model at an effort.
★ Recommended effort. Every model gets its best-value effort, and you see exactly what one more step up buys (+4.8 ability) and costs (×1.37).
A leaderboard at that effort. Models are ranked at their own ★ effort, the setup you would really use, with cost per reference coding task and task time beside ability. Outclassed models (costlier and no stronger) are called out, and if you are on one, the CLI tells you once what to switch to: Fable 5.1 → Opus 5.5 (+2.5 points at ×0.52), GPT-6 Astra → GPT-6.1 Sol (about the same at ×0.18).
Your own quota. The Skill and CLI read your local Claude Code and Codex 5-hour and weekly windows (never uploaded). The CLI also reads ZCode's and OpenCode's (V2) local databases, read-only, for tokens per day, session and project. The CLI also keeps a local history of how much of each window you used and under which model and effort (modelplan history), the basis for suggesting a default effort from your own weeks, and measures your own speed per model, effort and Fast: output tok/s, time to first token, turn time and tokens, against ★ (modelplan effort, modelplan history; read from the harness logs' timestamps and token counts only). Plenty left before a reset? Step up, or turn on Codex Fast. Tight, or a recent burst that would use it up within three hours? Stay at ★ or step down, with the time it runs out. After a reset, it reminds you once to go back to the model and effort you used before (and to turn Codex Fast off).
Gemini CLI local usage (optional). modelplan usage gemini enable allows read-only tokens/model/day/session accounting from Gemini’s own local chat history. modelplan usage gemini displays it; disable stops it. No credentials or remote quota are read, and no host is called. Missing counters stay --.
Other plans (optional). modelplan connect zai reads Z.ai / GLM Coding Plan usage natively, with the API key you keep in ZAIAPIKEY (--region bigmodel-cn for open.bigmodel.cn), modelplan connect opencodego reads OpenCode Go with OPENCODEAPIKEY, and modelplan connect kiro asks Kiro's own CLI (kiro-cli acp, no prompt sent; Kiro keeps its sign-in). Each key is sent only to its vendor's usage endpoint and never stored; modelplan connections shows which reader serves each plan. For the rest, modelplan codexbar enable cursor zai reads Copilot, Cursor, Z.ai / GLM, Kiro or OpenCode Go usage through the open-source CodexBar CLI, fetched at a pinned, checksum-checked release (macOS and Linux only). CodexBar keeps the sign-ins; this client keeps only percentages and reset times. Tell it which plan pays (modelplan declare zhipu-global-pro legacy-v2) and advice follows how that plan counts: on a per-request plan it never says step down, and points to the next off-peak hours instead.
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- Source
- github · https://github.com/modelplan/modelplan
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