How I Built a Credit Optimizer That Saves 30-75% on AI Agent Costs (Open Architecture)

A developer built an open-architecture Credit Optimizer that reduces AI agent costs by 30-75% by analyzing task complexity before routing to the appropriate model tier. The system scores tasks on a 1-10 scale using factors like token count, domain keywords, and output requirements, then assigns cheap models like Flash for simple tasks and premium models like Opus for complex ones. After implementation, the developer reported that monthly credit usage extended from 14 to over 30 days, simple task costs dropped 70%, and average response times decreased by 40%.

If you're using AI agents like Manus AI, Claude, or ChatGPT with API access, you've probably noticed something frustrating: every task gets the same expensive model , regardless of complexity. A simple "rename this variable" task burns the same credits as "analyze this 50-page legal document." That's like hiring a senior architect to hang a picture frame. After burning through my monthly Manus credits in just 2 weeks, I decided to build a solution. The core idea is simple: analyze task complexity BEFORE execution, then route to the appropriate model tier. Here's the decision tree: Task Input → Complexity Analyzer → Score 1-10 ↓ Score = 8 → Opus/GPT-4 expensive, high quality Score 4-7 → Sonnet/GPT-4o balanced Score <= 3 → Flash/GPT-4o-mini cheap, fast The scoring considers multiple factors: | Factor | Weight | Examples | |---|---|---| | Token count | 20% | Long prompts = higher complexity | | Domain keywords | 25% | "analyze", "research", "compare" = high | | Output requirements | 25% | Code generation, multi-step = high | | Context dependency | 15% | References previous work = higher | | Creativity demand | 15% | "brainstorm", "innovate" = high | php def route task task description: str - str: score = 0 Token analysis tokens = count tokens task description if tokens 2000: score += 2 elif tokens 500: score += 1 Domain complexity high complexity keywords = "analyze", "research", "compare", "synthesize", "architect", "design system", "debug complex" low complexity keywords = "rename", "format", "list", "simple", "quick" for kw in high complexity keywords: if kw in task description.lower : score += 2 for kw in low complexity keywords: if kw in task description.lower : score -= 1 Route based on score score = max 1, min 10, score if score = 8: return "opus" Most expensive, highest quality elif score = 4: return "sonnet" Balanced else: return "flash" Cheapest, fastest After implementing this system on my Manus AI workflow: | Metric | Before | After | Improvement | |---|---|---|---| | Monthly credit usage | 100% in 14 days | 100% in 30+ days | 2x+ duration | | Simple task cost | Same as complex | 70% cheaper | -70% | | Complex task quality | Baseline | Same or better | No degradation | | Average response time | 8-12s | 3-8s simple tasks faster | -40% | The key insight: ~60% of daily tasks are simple enough for the cheapest model tier , but without routing, they all consume premium credits. I packaged this into a skill called Credit Optimizer that works as a pre-processing layer: The architecture is model-agnostic — it works with any AI service that offers multiple model tiers: Because quality matters. Complex tasks genuinely need powerful models. The optimizer ensures you get the RIGHT model for each task — not always the cheapest, not always the most expensive. The Credit Optimizer is available at creditopt.ai https://creditopt.ai — it includes: I'm working on: Have you built something similar? I'd love to hear about different approaches to AI cost optimization. Drop a comment below or find me on creditopt.ai.