/model-cost-compare
Starter: compare model costs for a described task — maps task shape to the cheapest adequate seat and shows the price spread
Claude Code
$ npx -y skills add nyldn/claude-octopus --skill model-cost-compare --agent claude-code
Codex
$ npx skills add nyldn/claude-octopus --skill model-cost-compare -a codex -y
Cursor
$ npx skills add nyldn/claude-octopus --skill model-cost-compare -a cursor -y
Gemini CLI
$ npx skills add nyldn/claude-octopus --skill model-cost-compare -a gemini-cli -y
GitHub Copilot
$ npx skills add nyldn/claude-octopus --skill model-cost-compare -a github-copilot -y
OpenCode
$ npx skills add nyldn/claude-octopus --skill model-cost-compare -a opencode -y
Windsurf
$ npx skills add nyldn/claude-octopus --skill model-cost-compare -a windsurf -y
SKILL.md
model-cost-compare.SKILL.mdname: model-cost-compare
disable-model-invocation: true
description: "Starter: compare model costs for a described task — maps task shape to the cheapest adequate seat and shows the price spread"
Model Cost Comparison (Starter Pack)
Answer "which model should I use for this, and what will it cost?" with numbers instead of vibes.
When to use
The user describes a task (bulk refactor, deep review, quick lookup, long-context analysis) and wants the cheapest seat that is still adequate.
Steps
1. **Classify the task.** Bucket it: mechanical (rename, format), standard coding, hard reasoning (architecture, security review), long-context (>200K tokens input), or web research. 2. **Estimate volume.** Rough input/output token estimate from the described scope (files touched × average size; state the assumption). 3. **Price the roster.** Using the cost table in CLAUDE.md ($/MTok input/output), compute the estimated cost for each plausible seat: Claude Opus 5.5 (4/20 USD), Claude Opus 5 (5/25 USD), Claude Sonnet 5 (2/10 USD), Fable 5.1 (10/50 USD, 1M context, explicit-only), Codex GPT-5.6 Sol (4/20 USD), Terra (2/12 USD), Luna (0.20/1.20 USD), GPT-6 Astra (10/50 USD, explicit-only), Perplexity Sonar Pro (3/15 USD), and the included-cost seats (agy, copilot, ollama, cursor-agent) at 0 USD. For Astra requests above 272K input tokens, apply 2x input and 1.5x output pricing to the full request. 4. **Recommend one seat.** Pick the cheapest adequate option and defend it in two sentences. Mechanical work goes to included or budget seats; hard reasoning justifies the current Opus default at `high` effort; only a bounded judgment-class call (ambiguous architecture, API design, product tradeoffs) justifies Fable 5.1 at 10/50 USD per MTok. 5. **Check risk surfaces.** Regardless of the classification, escalate specifically to the current Opus default—Opus 5.5 on Claude Code v2.1.280+, otherwise Opus 5—when the task touches API or schema contracts, security-sensitive code or CI configuration, release artifacts, user-facing UI, a new module, or a breaking change. Fable 5.1 remains limited to bounded judgment-class calls and is never the security-audit seat. Astra is also explicit-only and does not provide independence from GPT-5.6. Cheap-seat agreement never settles a judgment-class decision. 6. **Show the spread.** A three-row table: recommended seat, one cheaper-but-riskier option, one premium option, each with estimated dollars for this task.
Guardrails
- Never recommend Fable 5.1, Astra, or fast-mode Opus by default; these premium seats are opt-in only.
- Never recommend Fable 5.1 for security audits; its safety classifiers can refuse offensive-security phrasing. Security review goes to Opus 5 (see `skills/blocks/fable5-prompting.md`).
- If the estimate exceeds 1 USD, say so explicitly before any dispatch happens.
Read more
name: model-cost-compare disable-model-invocation: true description: "Starter: compare model costs for a described task — maps task shape to the cheapest adequate seat and shows the price spread"
Model Cost Comparison (Starter Pack)
Answer "which model should I use for this, and what will it cost?" with numbers instead of vibes.
When to use
The user describes a task (bulk refactor, deep review, quick lookup, long-context analysis) and wants the cheapest seat that is still adequate.
Steps
1. **Classify the task.** Bucket it: mechanical (rename, format), standard coding, hard reasoning (architecture, security review), long-context (>200K tokens input), or web research. 2. **Estimate volume.** Rough input/output token estimate from the described scope (files touched × average size; state the assumption). 3. **Price the roster.** Using the cost table in CLAUDE.md ($/MTok input/output), compute the estimated cost for each plausible seat: Claude Opus 5.5 (4/20 USD), Claude Opus 5 (5/25 USD), Claude Sonnet 5 (2/10 USD), Fable 5.1 (10/50 USD, 1M context, explicit-only), Codex GPT-5.6 Sol (4/20 USD), Terra (2/12 USD), Luna (0.20/1.20 USD), GPT-6 Astra (10/50 USD, explicit-only), Perplexity Sonar Pro (3/15 USD), and the included-cost seats (agy, copilot, ollama, cursor-agent) at 0 USD. For Astra requests above 272K input tokens, apply 2x input and 1.5x output pricing to the full request. 4. **Recommend one seat.** Pick the cheapest adequate option and defend it in two sentences. Mechanical work goes to included or budget seats; hard reasoning justifies the current Opus default at `high` effort; only a bounded judgment-class call (ambiguous architecture, API design, product tradeoffs) justifies Fable 5.1 at 10/50 USD per MTok. 5. **Check risk surfaces.** Regardless of the classification, escalate specifically to the current Opus default—Opus 5.5 on Claude Code v2.1.280+, otherwise Opus 5—when the task touches API or schema contracts, security-sensitive code or CI configuration, release artifacts, user-facing UI, a new module, or a breaking change. Fable 5.1 remains limited to bounded judgment-class calls and is never the security-audit seat. Astra is also explicit-only and does not provide independence from GPT-5.6. Cheap-seat agreement never settles a judgment-class decision. 6. **Show the spread.** A three-row table: recommended seat, one cheaper-but-riskier option, one premium option, each with estimated dollars for this task.
Guardrails
- Never recommend Fable 5.1, Astra, or fast-mode Opus by default; these premium seats are opt-in only.
- Never recommend Fable 5.1 for security audits; its safety classifiers can refuse offensive-security phrasing. Security review goes to Opus 5 (see `skills/blocks/fable5-prompting.md`).
- If the estimate exceeds 1 USD, say so explicitly before any dispatch happens.
Every AI model has blind spots. Claude Octopus supports twelve external provider integrations — Codex, Antigravity CLI, Copilot, Qwen, Ollama, Perplexity, OpenRouter, OrcaRouter, OpenCode, Cursor CLI, Grok, and Kimi Code — alongside the built-in Claude Code
Other skills on octo.
More skills from other plugins.
browser-use
Direct browser control via CDP for web interaction: automation, scraping, testing, screenshots, and site/app work. Also covers…
agent-browser
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling…
Questions about model-cost-compare.
- Which coding agents can use model-cost-compare?
- model-cost-compare installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.

