GPT-5.6 Sol, Terra and Luna: A Developer's Guide
OpenAI's GPT-5.6 family is now generally available. Here is what Sol, Terra and Luna cost, how the tiers differ and what changes for developers.
OpenAI's GPT-5.6 model family reached general availability on July 9, 2026, and it splits the lineup into three clearly priced tiers: Sol (flagship), Terra (balanced) and Luna (fast and cheap). For developers the practical headline is simple — Terra targets GPT-5.5-level results at roughly half the price, Sol takes over the hard reasoning and agentic work, and a new cache-write fee changes how you should think about prompt caching.
The three tiers at a glance
GPT-5.6 first appeared in a limited preview on June 25–26, 2026, and went GA across ChatGPT, Codex and the API two weeks later. The family replaces the single-flagship model with a tiered lineup:
| Tier | Positioning | Input / Output (per 1M tokens) |
|---|---|---|
| GPT-5.6 Sol | Flagship: reasoning, agentic workflows, hard coding | $5 / $30 |
| GPT-5.6 Terra | Balanced: ~GPT-5.5 performance at ~2x lower cost | $2.50 / $15 |
| GPT-5.6 Luna | Fastest and cheapest: high-volume, low-latency tasks | $1 / $6 |
Sol additionally exposes an Ultra mode — a high-effort reasoning setting for the hardest problems. On OpenAI's own Terminal-Bench 2.1 agentic-coding numbers, Sol Ultra scores 91.9% and base Sol 88.8%, against 88.0% for Anthropic's Claude Mythos 5. Treat those figures as vendor-reported until independent evaluations land; first-party benchmark tables have a history of flattering their authors.
The cache-write change matters more than it looks
Prompt caching keeps its 90% discount on cache reads, but GPT-5.6 introduces a charge OpenAI has not billed before: cache writes now cost 1.25x the uncached input rate. Under GPT-5.5, populating the cache was free, so teams cached aggressively by default.
The new math changes that default. Caching a large system prompt now only pays off when it is actually reused — as a rule of thumb, a cached prefix needs to be read again at least once within its lifetime before the 25% write premium is recovered. Pipelines that cache long, rarely repeated contexts (one-off document analysis, for example) will quietly cost more under GPT-5.6, while chat products and agent loops that replay the same prefix hundreds of times still come out far ahead.
Where you can use it today
The rollout is unusually broad for day one:
- API: all three tiers, plus programmatic tool calling and the Sol Ultra effort setting.
- ChatGPT and Codex: GPT-5.6 replaces GPT-5.5 as the default flagship experience.
- GitHub Copilot: GitHub added the full GPT-5.6 family the same week, so teams can compare tiers directly inside their existing editor workflow.
If you run AI coding agents, the tier split maps neatly onto agent architecture: Luna for cheap mechanical steps like classifying files or summarizing diffs, Terra as the general-purpose driver, and Sol or Sol Ultra for planning and the failure cases the cheaper tiers cannot crack.
How to choose a tier without guessing
A tiered lineup rewards measurement over brand loyalty. A pragmatic migration path from GPT-5.5:
- Rebaseline on Terra first. It is the price-performance story of this release; if your evals hold at half the cost, you are done.
- Escalate per task, not per product. Route only the tasks that measurably fail on Terra up to Sol. Model-routing logic is a few lines of code and typically saves more than any prompt optimization.
- Audit your caching. Flag any cached prefix with a low reuse rate — under the 1.25x write premium it now costs more than not caching at all.
- Re-run your evals on Luna for bulk work. Batch classification, extraction and summarization jobs often lose nothing at $1 per million input tokens — a fifth of Sol's price.
The bigger picture
GPT-5.6 landed in the middle of the most crowded release week of the year — xAI's coding-focused Grok 4.5 (July 8) and Meta's first paid model API with Muse Spark 1.1 (July 9) shipped within a day of it, all competing on price as much as on headline capability. That is good news for developers: the direction of travel is cheaper tokens, clearer tiers and more interchangeable providers. The teams that benefit most will be the ones with their own evaluation suites, because when models change this fast, "which tier is right" is a question you want your test harness — not a launch blog post — to answer.
Frequently asked questions
What is the difference between GPT-5.6 Sol, Terra and Luna?
Sol is the flagship tier aimed at reasoning, agentic workflows and hard coding tasks, with an optional high-effort Ultra mode. Terra is the balanced mid-tier that OpenAI positions at roughly half the cost of GPT-5.5 for similar performance. Luna is the fastest and cheapest tier for high-volume, latency-sensitive work.
How much does the GPT-5.6 API cost?
Per million tokens: Sol costs $5 input / $30 output, Terra $2.50 / $15, and Luna $1 / $6. Cached input keeps its 90% discount, but starting with GPT-5.6 writing to the prompt cache is billed at 1.25x the uncached input rate.
Which GPT-5.6 tier should I use for coding agents?
Start with Terra and escalate to Sol only where results measurably improve — long agentic sessions, large refactors, or tasks that fail on the mid-tier. Reserve Sol Ultra for the hardest problems, since high-effort reasoning multiplies output tokens, and use Luna for mechanical high-volume steps like classification or summarization.