Prices → Compare → GPT-5.6 Terra vs GPT-5.6 Luna
GPT-5.6 Terra vs GPT-5.6 Luna: API cost compared
GPT-5.6 Terra lists at $1.000 in / $6.000 out per 1M tokens. GPT-5.6 Luna lists at $0.100 in / $0.600. Sticker price only tells you so much — below is what each actually costs across eight production workloads.
Side by side
| GPT-5.6 Terra | GPT-5.6 Luna | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Input per 1M | $1.000 | $0.100 |
| Output per 1M | $6.000 | $0.600 |
| Cached input per 1M | $0.100 | $0.010 |
| Context window | 400k | 400k |
| Blended per 1M (3:1) | $2.250 | $0.225 |
Monthly cost by workload
Caching applied at each workload's typical hit rate. This is where the two models actually separate.
| Workload | GPT-5.6 Terra | GPT-5.6 Luna | Cheaper | Difference |
|---|---|---|---|---|
| Customer support chatbot | $146.40 | $14.64 | GPT-5.6 Luna | $131.76 (90%) |
| RAG document Q&A | $196.00 | $19.60 | GPT-5.6 Luna | $176.40 (90%) |
| Coding agent | $104.50 | $10.45 | GPT-5.6 Luna | $94.05 (90%) |
| Bulk summarization | $1,386 | $138.60 | GPT-5.6 Luna | $1,247 (90%) |
| Structured data extraction | $1,870 | $187.00 | GPT-5.6 Luna | $1,683 (90%) |
| Long-form content generation | $254.60 | $25.46 | GPT-5.6 Luna | $229.14 (90%) |
| Email triage agent | $432.50 | $43.25 | GPT-5.6 Luna | $389.25 (90%) |
| Translation pipeline | $1,645 | $164.47 | GPT-5.6 Luna | $1,480 (90%) |
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 Terra and 6.0 for GPT-5.6 Luna. 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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| Model | In /1M | Out /1M | Monthly | vs best |
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