Model Selection & Cost Optimization

Choosing the right model and optimizing costs can reduce your bill by 10x without sacrificing quality. This page covers model cascading, prompt caching, and the TOON format.

Model Selection Decision Framework

The Model Landscape (January 2025):

Model	Context	Cost (input/output per 1M tokens)	Best For
GPT-4 Turbo	128K	$10 / $30	Complex reasoning, structured output
GPT-3.5 Turbo	16K	$0.50 / $1.50	Simple tasks, high volume
Claude Sonnet 4.5	200K	$3 / $15	Long documents, nuanced analysis
Claude Haiku	200K	$0.25 / $1.25	Fast classification, simple extraction
Gemini Pro 1.5	2M	$1.25 / $5	Massive context, multimodal
Llama 3 70B	Varies	Self-hosted	On-premise requirements

Decision Tree:

Is the task simple (classification, basic extraction)?
├─ Yes → Use fastest/cheapest (Haiku, GPT-3.5)
└─ No → Continue

Do you need >100K tokens of context?
├─ Yes → Claude Sonnet or Gemini Pro
└─ No → Continue

Do you need structured JSON output?
├─ Yes → GPT-4
└─ No → Claude Sonnet (better prose)

Is cost critical (high volume)?
├─ Yes → Consider model cascading (section 5.3)
└─ No → Use best model for quality

As of publication. Verify latest pricing/context on vendor pages: OpenAI pricing, Anthropic pricing, and Google Gemini pricing/models.

Prompt Caching: 50-90% Cost Reduction

The Problem: You’re sending the same 50K token knowledge base with EVERY request.

// Without caching
for (const query of queries) {  // 10,000 queries
    const prompt = `${knowledgeBase}\n\nQuery: ${query}`;  // 50K + 100 tokens
    const response = await llm.generate(prompt);
}

// Cost: 10,000 * 50,100 tokens = 501M input tokens
// At \$3/1M: \$1,503

The Solution: Prompt Caching Mark reusable parts of your prompt for caching: Production Economics:

// Scenario: Customer support chatbot
// - Knowledge base: 50K tokens
// - Avg conversation: 5 queries
// - Cache hit rate: 80% (users ask follow-ups quickly)

without_caching = 10_000 queries * 50K tokens * \$3/1M = \$1,500
with_caching = (
    (10_000 * 0.2) * 50K   // Cache misses (20%)
    + (10_000 * 0.8) * 5K  // Cache hits (90% reduction)
) * \$3/1M = \$450

savings = \$1,050 (70% reduction)

Best Practices:

Cache static content (knowledge bases, system prompts)
Don’t cache user input (changes every request)
Structure prompts with cacheable parts first
Monitor cache hit rates
Adjust query patterns to maximize cache hits

Model Cascading: Using Cheap Models First

The Strategy:

Try cheap/fast model first
If uncertain, escalate to expensive/smart model
Can reduce cost while maintaining quality when confidence gating is reliable

Implementation: When Cascading Works:

High-volume, similar tasks
Clear confidence signals (some models provide log probabilities)
Cost pressure but quality requirements

When to Avoid:

Low latency requirements (cascading adds delay)
Tasks where confidence is hard to measure
Low volume (not worth complexity)

In Production:

Factor	Single model	Cascading
Accuracy	Stable, predictable	Depends on routing quality
Latency	Lower (one call)	Higher (fallback adds calls)
Cost	Higher per call	Lower on average if many low-cost wins
Complexity	Lower	Higher (routing, monitoring)

Choose cascading when cost pressure is high and confidence signals are trustworthy; otherwise prefer simplicity.

TOON for Token-Efficient Context

Why: For uniform arrays of objects with primitive fields, TOON reduces token usage (often 30-60% vs JSON) and is easy for LLMs to parse. When TOON excels:

Uniform tabular arrays (same keys, primitive values).
Large lists where repeated JSON keys dominate cost.

When to prefer JSON:

Mixed/nested structures, varying field sets, or complex types.

Example (input as TOON): Full runnable TOON notebook

items[2]{sku,name,qty,price}:
  A1,Widget,2,9.99
  B2,Gadget,1,14.5

Prompting the model to output TOON:

Data is in TOON (2-space indent; arrays show [N]{fields}).
Return ONLY TOON with the same header; set [N] to match rows.

Practical exercise: Convert your JSON examples to TOON and compare input token counts and task accuracy. See: TOON repository and official site/spec

Documentation Index

​Model Selection Decision Framework

​Prompt Caching: 50-90% Cost Reduction

​Model Cascading: Using Cheap Models First

​TOON for Token-Efficient Context

Model Selection Decision Framework

Prompt Caching: 50-90% Cost Reduction

Model Cascading: Using Cheap Models First

TOON for Token-Efficient Context