#AI Summarization Cost Feasibility Assessment

Assessment Date: 2026-01-08 Assessed By: Claude Code Baseline: 4,000 I/O tokens per summary (3,700 input / 300 output) Models Compared: Claude Haiku 4.5, Claude Sonnet 4.5, GPT-4.1-mini

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#Executive Summary

Model	Cost/Summary	Daily (100)	Daily (1K)	Monthly (3K)	Monthly (30K)	Feasibility
Claude Haiku 4.5	$0.0052	$0.52	$5.20	$15.60	$156.00	✅ Recommended
Claude Sonnet 4.5	$0.0156	$1.56	$15.60	$46.80	$468.00	⚠️ High-value only
GPT-4.1-mini	$0.00196	$0.20	$1.96	$5.88	$58.80	✅ Budget option

Recommendation: Continue with Claude Haiku 4.5 as default. Reserve Sonnet for high-complexity edge cases. GPT-4.1-mini viable as cost-saving fallback.

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#Model Pricing (as of January 2026)

#Claude Haiku 4.5 (Current Default)

Token Type	Rate (per 1M tokens)
Input	$1.00
Output	$5.00

#Claude Sonnet 4.5 (High-Complexity Option)

Token Type	Rate (per 1M tokens)
Input	$3.00
Output	$15.00

#GPT-4.1-mini (Fallback)

Token Type	Rate (per 1M tokens)
Input	$0.40
Output	$1.60

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#Per-Summary Cost Breakdown

Based on 4,000 I/O tokens (3,700 input / 300 output):

Model	Input Cost	Output Cost	Total	vs Haiku
Claude Haiku 4.5	$0.0037	$0.0015	$0.0052	baseline
Claude Sonnet 4.5	$0.0111	$0.0045	$0.0156	3.0× more
GPT-4.1-mini	$0.00148	$0.00048	$0.00196	2.7× less

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#Daily Cost Projections

Summaries/Day	Haiku 4.5	Sonnet 4.5	GPT-4.1-mini
10	$0.05	$0.16	$0.02
50	$0.26	$0.78	$0.10
100	$0.52	$1.56	$0.20
250	$1.30	$3.90	$0.49
500	$2.60	$7.80	$0.98
1,000	$5.20	$15.60	$1.96
2,500	$13.00	$39.00	$4.90
5,000	$26.00	$78.00	$9.80

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#Monthly Cost Projections (30 days)

Summaries/Month	Haiku 4.5	Sonnet 4.5	GPT-4.1-mini
300	$1.56	$4.68	$0.59
1,000	$5.20	$15.60	$1.96
3,000	$15.60	$46.80	$5.88
5,000	$26.00	$78.00	$9.80
10,000	$52.00	$156.00	$19.60
30,000	$156.00	$468.00	$58.80
50,000	$260.00	$780.00	$98.00
100,000	$520.00	$1,560.00	$196.00

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#Annual Cost Projections

Usage Tier	Daily Volume	Haiku 4.5/yr	Sonnet 4.5/yr	GPT-4.1-mini/yr
Starter	100/day	$189.80	$569.40	$71.54
Growth	500/day	$949.00	$2,847.00	$357.70
Scale	1,000/day	$1,898.00	$5,694.00	$715.40
Enterprise	5,000/day	$9,490.00	$28,470.00	$3,577.00

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#Feasibility Analysis

#Claude Haiku 4.5 ✅ RECOMMENDED

Pros:

• Optimal quality-to-cost ratio for delta-first summaries
• Fast inference latency (~200-400ms typical)
• Strong instruction-following for structured JSON output
• Native Anthropic ecosystem (Vercel AI SDK integration)

Cons:

• 3× more expensive than GPT-4.1-mini
• May occasionally miss nuance in complex technical content

Best For: Default production use, all standard page types

Break-even: Cost-effective at any volume for Eko's use case

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#Claude Sonnet 4.5 ⚠️ CONDITIONAL

Pros:

• Superior reasoning for complex multi-section diffs
• Better handling of ambiguous or technical content
• Higher confidence scores on edge cases

Cons:

• 3× more expensive than Haiku
• Marginal quality improvement for simple summaries
• Higher latency (~500-800ms typical)

Best For:

• High-complexity page types (legal, financial, scientific)
• When Haiku returns low-confidence scores (<0.6)
• User-requested "detailed analysis" mode

Recommendation: Implement as escalation tier, not default

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#GPT-4.1-mini ✅ VIABLE FALLBACK

Pros:

• 2.7× cheaper than Haiku
• Fast inference
• Good general-purpose summarization

Cons:

• Less reliable JSON schema adherence
• May require more prompt engineering
• Different output style/tone
• Separate API key management

Best For:

• Fallback when Anthropic is unavailable
• Budget-constrained deployments
• Batch processing non-critical updates

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#Cost Optimization Strategies

#1. Batch Processing Discount (Anthropic)

• 50% discount on async batch API
• Suitable for non-real-time check processing
• Could reduce Haiku costs to ~$0.0026/summary

#2. Tiered Model Selection

if page_type in ['legal', 'scientific', 'financial']:
    model = 'sonnet'  # Higher complexity
elif confidence_score < 0.6:
    model = 'sonnet'  # Escalation
else:
    model = 'haiku'   # Default

#3. Token Optimization

• Current: 4,000 I/O tokens average
• Target: 3,000 I/O tokens with content trimming
• Potential savings: 25% cost reduction

#4. Caching Strategy

• Cache summaries for unchanged content
• Skip AI for minor formatting changes
• Estimated reduction: 20-40% API calls

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#Comparison Matrix

Criteria	Haiku 4.5	Sonnet 4.5	GPT-4.1-mini
Cost	⭐⭐⭐	⭐	⭐⭐⭐⭐⭐
Quality	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐
Latency	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐
JSON Reliability	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐
Integration	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐
Batch Support	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐

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#Recommendations

#Immediate Actions

1. Keep Haiku 4.5 as default — best value for standard summaries
2. Document Sonnet escalation criteria — when to use higher-tier model
3. Monitor token usage — validate 4,000 token assumption with production data

#Future Optimizations

1. Implement batch API for scheduled checks (50% savings)
2. Add content-length-based model selection
3. Cache identical content to skip redundant API calls
4. Consider GPT-4.1-mini for non-critical batch jobs

#Budget Planning

Growth Scenario	Monthly Summaries	Monthly Cost (Haiku)
MVP Launch	1,000	$5.20
Early Traction	5,000	$26.00
Growth Phase	20,000	$104.00
Scale	100,000	$520.00

Conclusion: AI summarization costs are highly feasible at all projected scales. Haiku 4.5 provides excellent quality at manageable costs, with clear escalation path to Sonnet for edge cases.

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#Appendix: Token Assumptions

Component	Tokens	Notes
System prompt	~500	Fixed overhead
User prompt + context	~200	URL, title, tracking note
Page content (diff)	~3,000	Variable, compressed
Total Input	~3,700
Summary output	~300	max_tokens=300
Total I/O	~4,000

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Generated by architect-steward assessment process