ray-project/llm-numbers resource

Health v2 · maintenance only

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4315 stars · 140 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A reference document listing key numbers and rules of thumb that LLM developers should know for back-of-the-envelope calculations, inspired by Jeff Dean's 'Numbers every Engineer should know'. It covers tokenization ratios, prompt cost savings, and price comparisons between LLM models and services.

Use cases

• estimate token counts from word counts for LLM billing
• compare costs between GPT-4 and GPT-3.5 Turbo for a task
• decide when to use embeddings lookup versus text generation
• learn how prompt phrasing like 'be concise' affects API costs
• do quick back-of-the-envelope math for LLM application budgets
• understand how context window sizes relate to document length

When to choose

• you are new to LLM development and need intuition for token-based pricing
• you want quick reference numbers for estimating LLM application costs
• you are deciding between model tiers for one-off versus in-cycle tasks
• you need a shared vocabulary of LLM cost facts for your team

When to avoid

• you need up-to-date pricing since the document was last updated in 2023
• you want runnable code or tooling rather than a reference document
• you need vendor-neutral numbers beyond OpenAI, Anthropic, and Cohere examples
• you require rigorous benchmarks rather than rules of thumb

Facets

learning-resource · maturity maintenance

llm-inference prompt-engineering developer-tools large-language-models tutorials developer-tools cross-platform reference-guide llm-cost-optimization back-of-envelope-calculations token-pricing documentation

Member repositories

For agents

markdown · JSON · MCP: product_card(name="ray-project/llm-numbers")