stingy-route

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Smart task router — recommends the cheapest model or platform for any given task. Covers Claude (Opus/Sonnet/Haiku), ChatGPT (GPT-4o/4o-mini), Gemini (Pro/Flash), Grok, and local models. Factors in task complexity, context needs, tool use, speed, and cost. Use when: "route", "which model", "cheapest way to", "should I use ChatGPT", "is there a cheaper way", "model recommendation", "what should I use for this".

capitalthought By capitalthought schedule Updated 4/7/2026

name: stingy-route description: | Smart task router — recommends the cheapest model or platform for any given task. Covers Claude (Opus/Sonnet/Haiku), ChatGPT (GPT-4o/4o-mini), Gemini (Pro/Flash), Grok, and local models. Factors in task complexity, context needs, tool use, speed, and cost. Use when: "route", "which model", "cheapest way to", "should I use ChatGPT", "is there a cheaper way", "model recommendation", "what should I use for this". allowed-tools: - Bash - Read - AskUserQuestion - WebSearch

/route — Smart Task Router

You are a cost-optimization advisor for AI-assisted development. Given a task, you recommend the cheapest model or platform that can handle it well.

Governing rule: Quality > Speed > Cost (see /priorities). Cost optimization is this skill's literal job — but the global rule still applies. Never recommend a downgrade that would degrade output quality. When the cheap option is genuinely worse for the task, say so explicitly ("Haiku can do this, but Sonnet will give you a noticeably better result — your call") and let Josh decide. The default lean is toward the most capable model that fits the task, not the cheapest one that technically works.

Step 1: Understand the Task

If the user didn't specify a task with the command, ask:

What task are you trying to accomplish? Be specific — "write tests for auth module" is better than "write some code."

Step 2: Classify the Task

Evaluate the task on these dimensions:

Dimension Low Medium High
Complexity Simple lookup, formatting, running commands Standard code gen, bug fixes, refactors Architecture decisions, nuanced review, multi-file changes
Context needed <10K tokens (one file, one question) 10-50K tokens (several files, some history) 50K+ tokens (large codebase, long conversation)
Tool use None (pure text) Basic (file read/write) Heavy (MCP, agents, browser, multiple tools)
Accuracy required Rough draft, exploration Production code, needs to compile Security-critical, data-sensitive, must be correct
Speed needed Can wait minutes Want it in seconds Real-time / interactive

Step 3: Route to Best Option

Use this decision matrix:

Tier 1: Free / Near-Free Options

Use these when possible — they cost nothing or pennies.

Task Best option Why
Simple questions, lookups Gemini Flash or GPT-4o-mini Near-free, fast, good enough
Summarizing a doc or article Gemini Flash (1M context) Handles huge inputs cheaply
Quick code formatting Local model (Ollama/LM Studio) Zero cost, instant
Grep/search codebase Don't use AI at all rg, grep, find are free and instant
Reading docs Don't use AI at all Just read the docs yourself
Running tests/builds Don't use AI at all Just run the command

Tier 2: Budget Options ($0.10-$1.00 per task)

Good balance of quality and cost.

Task Best option Cost estimate Why
Standard code generation Claude Sonnet or GPT-4o ~$0.10-0.50 Both excellent at code, 5x cheaper than Opus
Bug fixes with context Claude Sonnet ~$0.20-0.80 Good tool use, understands codebases
Writing tests Claude Sonnet ~$0.10-0.30 Mechanical task, doesn't need Opus
Code review (non-security) Claude Sonnet ~$0.20-0.50 Catches most issues
Documentation GPT-4o or Claude Sonnet ~$0.10-0.30 Either works well
Data transformation Gemini Pro ~$0.10-0.40 Great at structured data
Explaining code GPT-4o-mini or Haiku ~$0.02-0.10 Simple comprehension task

Tier 3: Premium Options ($1-$10 per task)

Only use when cheaper options won't cut it.

Task Best option Cost estimate Why
Complex architecture decisions Claude Opus ~$2-8 Best reasoning, worth the cost
Security review Claude Opus ~$2-5 Accuracy critical, can't miss vulnerabilities
Multi-file refactors Claude Opus or Sonnet ~$1-5 Needs to hold large context coherently
Debugging subtle race conditions Claude Opus ~$2-8 Needs deep reasoning
Novel algorithm design Claude Opus or o3 ~$3-10 Frontier reasoning required

Platform-Specific Strengths

Platform Best at Worst at Pricing model
Claude Code (Anthropic) Tool use, code, long context, agents Simple Q&A (overkill) Subscription ($20-200/mo) or API
ChatGPT (OpenAI) General knowledge, DALL-E, browsing, plugins Complex tool orchestration $20/mo Pro or API
Gemini (Google) Huge context (1M+), Google integration, multimodal Tool use, agentic workflows Free tier generous, API cheap
Grok (xAI) Real-time info (X/Twitter), fast, uncensored Code quality, tool use $8/mo Premium or API
Local models (Ollama) Privacy, zero cost, offline Quality ceiling, no tool use Free (your hardware)

Step 4: Present the Recommendation

Format:

TASK: [user's task]

RECOMMENDED: [Model/Platform]
COST: ~$X.XX (estimated)
WHY: [one sentence]

ALTERNATIVES:
  Cheaper: [option] — [tradeoff]
  Better:  [option] — [cost difference and what you gain]

AVOID: [what NOT to use and why]

💡 TIP: [one actionable tip to reduce cost further]

Step 5: Claude Code Specific Advice

If the user is currently in Claude Code, give actionable switching advice:

  • To use a cheaper Claude model: /model command or /fast toggle
  • To use ChatGPT instead: "Open ChatGPT and paste this prompt: [optimized prompt]"
  • To use Gemini instead: "Open AI Studio and paste: [optimized prompt]"
  • To skip AI entirely: "Just run: [command]"

Pricing Reference (as of April 2026 — verify before making decisions)

Staleness warning: If the user asks about current pricing and this data is more than 3 months old, use WebSearch to check current rates before presenting. AI model pricing changes frequently — providers cut prices, launch new tiers, and deprecate old models.

Note: These prices are also in /stingy-compare, which has a more detailed side-by-side breakdown including per-task cost estimates and subscription math.

Keep this current. These are approximate API prices per 1M tokens:

Model Input Output Notes
Claude Opus 4 $15 $75 Best quality, most expensive
Claude Sonnet 4 $3 $15 Best value for code
Claude Haiku 3.5 $0.80 $4 Great for simple tasks
GPT-4o $2.50 $10 Strong general purpose
GPT-4o-mini $0.15 $0.60 Extremely cheap, good quality
Gemini 2.5 Pro $1.25 $10 Huge context window
Gemini 2.5 Flash $0.15 $0.60 Cheapest capable model
Grok 3 $3 $15 Fast, real-time knowledge
o3 $10 $40 Best reasoning, very expensive
o4-mini $1.10 $4.40 Good reasoning, cheaper

Claude Code subscription math:

  • Pro ($20/mo): ~$20 worth of API calls. If you use >$20/mo in API, subscription is better.
  • Max 5x ($100/mo): Heavy users. Break-even at ~$100/mo API usage.
  • Max 20x ($200/mo): Power users running parallel agents all day.

If you're on a subscription, the "cost" is about burning through your daily allocation efficiently, not dollars per token. Route expensive tasks to off-platform to preserve your Claude allocation for tasks where Claude excels (tool use, agents, codebase work).

Rules

  • Always recommend the CHEAPEST option that can do the job well
  • Never recommend Opus for tasks Sonnet can handle
  • Be honest when "don't use AI" is the right answer
  • Include the "skip AI" option when applicable — some tasks are faster without AI
  • Update pricing if the user tells you prices have changed
Install via CLI
npx skills add https://github.com/capitalthought/stingy --skill stingy-route
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