CHAPTER 8.5 · INFERENCE TOOLS

Inference Cost Calculator

Compare costs and performance for Dense vs Sparse Attention Inference

The Inference Cost Calculator shows the real costs of LLM API calls: Input/Output token pricing, Sparse vs. Dense Attention savings, and how context length affects your bill.

📖 Learning Context ▼
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Learning Objectives

  • Understand token-based pricing models
  • Compare Dense vs. Sparse Attention costs
  • Plan budgets for LLM projects
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Context: Where are we?

Step 3/5 Chapter 8: Tools & Glossary

Calculators (3/5) help understand the practical costs of LLM usage.

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Why It Matters

LLM costs can explode quickly. The calculator shows: Sparse Attention saves 30-70% on long contexts – a game-changer for production systems.

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Key Takeaways

  • Output costs more: Generation costs 3-5x more than input
  • Sparse Attention: 30-70% cheaper for long contexts
  • Batch pricing: Volume discounts from ~1M tokens/day
128K tokens
500 tokens
Level 5 (Balanced)
Off (Dense Mode)
💾 Dense Attention Cost
Input Cost $0.384
Output Cost $0.007
Thinking Cost $0.000
Total Cost $0.391
Est. Time 15.2s
⚡ Sparse Attention Cost
Input Cost $0.154
Output Cost $0.003
Thinking Cost $0.000
Total Cost $0.157
Est. Time 4.3s

💰 Cost Comparison Over Context Length

Model Pricing Strategy

Input tokens cheaper than output (context is "one-time", output is generated). Claude 4.5: 1:5 ratio. GPT-5.1: 1:3. DeepSeek: nearly equal (training cost-intensive).

Sparse Attention Saves 60%

Sparse Attention reduces KV-Cache compute by 60%, but costs extra for Lightning Indexer. Net: 40-60% cost savings on long contexts (256K+).

Long Context Trade-offs

Under 32K: Sparse not worth it (overhead). 32K-256K: Sparse wins. Over 256K: Sparse is a MUST (Dense becomes prohibitively expensive).

Effort / Thinking Budget

Effort parameter multiplies thinking tokens: Effort 1 = 100 tokens, Effort 10 = 1000 tokens. Linear cost relationship. Per level: +10% cost, +5% quality.

Cost per Quality Metric

DeepSeek-V3.2: Cheaper, but less reasoning. Claude 4.5: More expensive, but better effort control. GPT-5.1: Adaptive (auto-selects thinking). ROI depends on task.

Production Optimization

Smart caching: Request 1: full cost. Request 2 (same context): only output tokens. Hybrid: Sparse + Dense (rerank top-10). Prompt caching: -50% input cost.