How to Cut AI Costs 80% With Smart Model Selection

The 80/20 of AI model usage

Most production AI applications follow a predictable pattern: roughly 80% of requests are simple tasks (classification, extraction, short responses) and 20% are complex tasks (long-form generation, reasoning, code review).

The problem: most teams use one model for everything. If that model is Claude Opus 4.6 at 24 credits per request, you're paying Opus prices for tasks that a 1-credit DeepSeek call could handle.

Task complexity matrix

Here's how to think about model selection:

Practical example: a document processing pipeline

Imagine a pipeline that processes uploaded documents:

1. Classify document type (invoice, contract, report) → DeepSeek (1 cr) 2. Extract key fields (dates, amounts, parties) → Gemini Flash (2 cr) 3. Summarize if > 10 pages → Sonnet (15 cr, only for long docs) 4. Flag anomalies for human review → Sonnet (15 cr, only on flagged docs)

For a typical batch of 1,000 documents: - Without optimization: 1,000 × 24 cr = 24,000 credits (¥240) - With optimization: ~2,000 cr for classification + extraction + ~150 cr for summarization = ~2,150 credits (¥21.50)

That's a 91% cost reduction.

The coming `model="auto"` parameter

We're building smart routing into clawfeeder.ai. Pass model="auto" and we'll analyze your prompt and route it to the cheapest model that can handle it correctly.

The routing is based on: - Prompt complexity (length, reasoning requirements) - Requested capabilities (function calling, vision, long context) - Your quality preference (you can set a quality hint: "fast", "balanced", "best")

More details when we launch.