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PricesCompare → GPT-5.6 Luna vs Claude Opus 5

GPT-5.6 Luna vs Claude Opus 5: API cost compared

GPT-5.6 Luna lists at $0.100 in / $0.600 out per 1M tokens. Claude Opus 5 lists at $5.000 in / $25.00. Sticker price only tells you so much — below is what each actually costs across eight production workloads.

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Cheaper on more workloads
GPT-5.6 Luna
wins 8 of 8
Blended rate gap
44.4x
at 3:1 in:out

Side by side

GPT-5.6 LunaClaude Opus 5
ProviderOpenAIAnthropic
Input per 1M$0.100$5.000
Output per 1M$0.600$25.00
Cached input per 1M$0.010$0.500
Context window400k200k
Blended per 1M (3:1)$0.225$10.00

Monthly cost by workload

Caching applied at each workload's typical hit rate. This is where the two models actually separate.

WorkloadGPT-5.6 LunaClaude Opus 5CheaperDifference
Customer support chatbot$14.64$644.50GPT-5.6 Luna$629.86 (98%)
RAG document Q&A$19.60$920.00GPT-5.6 Luna$900.40 (98%)
Coding agent$10.45$462.50GPT-5.6 Luna$452.05 (98%)
Bulk summarization$138.60$6,730GPT-5.6 Luna$6,591 (98%)
Structured data extraction$187.00$8,725GPT-5.6 Luna$8,538 (98%)
Long-form content generation$25.46$1,073GPT-5.6 Luna$1,048 (98%)
Email triage agent$43.25$2,012GPT-5.6 Luna$1,969 (98%)
Translation pipeline$164.47$7,024GPT-5.6 Luna$6,859 (98%)
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How to read this

GPT-5.6 Luna is cheaper on the majority of these workloads, but "majority" is not the decision. The output:input price ratio is 6.0 for GPT-5.6 Luna and 5.0 for Claude Opus 5. If your traffic is output-heavy — long-form drafting, translation — the model with the lower output price wins regardless of what the input price suggests. If you are input-heavy — RAG, bulk summarization, agents re-reading a codebase — the cached input rate matters more than either headline number.

Cost is also not the only axis. This site does not benchmark quality, latency, or rate limits, and a model that is 3x cheaper but needs two attempts per task is not cheaper. Use these numbers to size a bill, then validate on your own evals.

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