A short Claude Code session costs about $0.31 median on Opus 5.5 and $0.16 on Sonnet 5.5, mostly from cached context windowThe maximum amount of text a model can consider at once — its working memory for the current conversation or task.Full definition →, so keeping sessions short and routing sub-agents to Haiku cuts spend.
Key takeaways · AI-distilled
In OpenRouter's test, a one-turn Opus 5.5 session replying 'ok' wrote 17,047 tokenThe chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.Full definition → of Claude Code's own system promptThe standing instructions a model receives before any user input — defining its role, rules, tools, and tone for the whole conversation.Full definition → and tool definitions to the prompt cache, about $0.085, before any project files were read.
Cache reads on Opus 5.5 cost $0.20 per million tokens, 25 times less than cache writes, so later turns add less than the first; costs climb again as each newly read file is written to the cache once and re-read every turn.
In OpenRouter's 30-day session data, model choice outweighs session length: a one-turn Fable 5.1 session at a $0.342 median costs more than a 10-to-49-turn Haiku 5.5 session at $0.053.
In a controlled three-bug fix test, Opus, Sonnet and Haiku 5.5 all fixed every bug; Sonnet cost about two thirds of Opus and Haiku about a twelfth of Sonnet, though back-to-back runs shared a warm cache.
OpenRouter warns that Claude Code's own total_cost_usd guesses prices for unrecognized model names: it reported $0.16 to $0.31 for Haiku sessions that cost about half a cent and overstated Opus sessions by about 50%.
Terms in this piece · Glossary
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
system prompt — The standing instructions a model receives before any user input — defining its role, rules, tools, and tone for the whole conversation.
