How to Prompt GPT-5.6 (Sol, Terra and Luna Compared)

TL;DR: GPT-5.6 ships as three priced model IDs, gpt-5.6-sol, gpt-5.6-terra and gpt-5.6-luna, with the bare "gpt-5.6" name aliased to Sol. OpenAI's own guidance is that they differ in cost, latency and reasoning depth, not in how you should structure a prompt, and the family shares one recommended prompt skeleton and two independent reasoning controls.

What are GPT-5.6 Sol, Terra and Luna?

They are three separate model IDs on OpenAI's current model list, not three settings on one model. Each has its own price, and the plain name most examples use, gpt-5.6, is listed as an alias that currently resolves to the flagship: OpenAI's model list gives Sol's model ID as gpt-5.6-sol with the alias gpt-5.6 attached directly to that entry. Calling gpt-5.6 by its short name is calling Sol.

All three share the same context window (1.05M tokens), the same knowledge cutoff (February 16, 2026), the same maximum output (128K tokens), and the same tool set: functions, web search, file search and computer use. All three also expose the same six reasoning effort levels, none, low, medium, high, xhigh and max. The differences that actually separate them are price and reasoning depth per dollar, not capability tier or available tools.

All three are also reasoning models first, which matters for how you prompt them at all. Reasoning models vs chat models: prompt them differently covers the general mechanics that distinguish this whole model class from a plain chat model; this page picks up from there and stays specific to the GPT-5.6 family.

Do the three variants actually need different prompts?

No, and this is the honest finding worth stating plainly rather than manufacturing a false distinction. OpenAI's own reasoning guidance describes when to reach for each model in terms of workload, not prompt syntax: "Start with gpt-5.6 for most reasoning workloads. If you need the highest-intelligence API option for more challenging problems that can tolerate more latency, use gpt-5.6-sol in the Responses API with reasoning.mode set to pro. For lower cost, consider gpt-5.6-terra, or gpt-5.6-luna for the lowest cost and latency."

Nothing in that guidance, or in OpenAI's dedicated prompting document for the family, tells you to phrase instructions differently for Terra than for Sol. The one genuine model-level lever is reasoning.mode, covered next, which OpenAI recommends turning on for the hardest problems regardless of which of the three you are calling. Beyond that, the honest answer is that these three differ mainly in cost and latency, not in how you prompt them, and the rest of this page is the prompting guidance that applies to all three.

What is the difference between reasoning mode and reasoning effort?

They are two independent dials, and conflating them is the most common mistake. OpenAI's docs are explicit: "GPT-5.6 models support standard and pro reasoning modes in the Responses API. standard is the default. Set reasoning.mode to pro for difficult tasks that need more model work and can tolerate higher latency and token usage. Reasoning mode and reasoning effort are independent. Mode selects standard or pro execution, while reasoning.effort controls how much reasoning the model applies within that mode. If you omit reasoning.effort, GPT-5.6 defaults to medium in both modes."

Pro mode is billed at the calling model's own standard token rates, so switching Terra into pro mode does not bill you at Sol's rate; it does more work at Terra's price. A minimal request that sets both looks like this:

curl https://api.openai.com/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "gpt-5.6",
    "reasoning": {"mode": "pro", "effort": "medium"},
    "input": "Review this database migration plan and identify potential failure modes."
  }'

Our reasoning effort settings reference covers the same effort scale across Anthropic and Google, if you are working across vendors rather than staying inside the GPT-5.6 family.

What prompt structure does OpenAI recommend for GPT-5.6?

An eight-part outline, published as a starting point rather than a rigid template: "Role: [the model's function and context] Personality: [tone and collaboration style] Goal: [user-visible outcome] Success criteria: [what must be true before the final answer] Constraints: [policy, safety, business, evidence, and side-effect limits] Tools: [which tools to use, when, and what not to use] Output: [sections, length, format, and tone] Stop rules: [when to retry, fallback, abstain, ask, or stop]". The guide adds: "Keep each section short. Add detail only where it changes behavior."

Two pieces of that structure are worth calling out on their own. First, success criteria and stopping conditions matter more on GPT-5.6 than a list of steps does, because the guide's central advice is outcome-first: describe the destination, since "GPT-5.6 can usually choose an efficient search, tool, or reasoning path when the prompt states what good looks like." That advice argues against prescribing every step in favor of stating the destination. Second, the guide separates personality (tone, warmth, formality) from collaboration style (when the model asks questions, takes initiative, or checks its own work), and recommends keeping both short rather than replacing clear goals with either one.

Post 201's breakdown of role, task, format and constraints covers the same general shape for chat-surface prompting if you want the consumer-facing version of this idea rather than the API one.

Should you simplify your prompt when migrating from GPT-5.5?

Test it before assuming either direction, but OpenAI's own migration data leans toward trimming. Its guidance reports: "In a sample of internal coding-agent eval runs, configurations with leaner system prompts improved evaluation scores by roughly 10–15% while reducing total tokens by 41–66% and cost by 33–67%." It immediately qualifies that: "Results will vary by workload, so treat these ranges as directional and validate changes on representative tasks from your own application." Treat that number as a lead worth checking on your own evals, not a guarantee.

The documented migration workflow is five steps: switch the model while preserving your current reasoning effort, run your existing evals before touching the prompt, then remove obsolete scaffolding and unused tools, add only the smallest instruction that fixes a measured regression, and re-run evals after each change. The guide is direct about why the order matters: "Do not rewrite a working prompt stack all at once. Otherwise you cannot tell whether a behavior change came from the model, reasoning setting, prompt, tool set, or runtime."

One specific migration note worth flagging: OpenAI's guide says "GPT-5.6 tends to be more concise by default than GPT-5.5." So a blanket "Be concise" instruction carried over from an old prompt may now be unnecessary, or may make responses too short. Check it against your own output rather than assuming it still earns its place. Post 324 on prompts that stop working after a model update covers the general version of this failure mode.

How do autonomy and tool-calling change on GPT-5.6?

GPT-5.6 is documented as more willing to act autonomously across multi-step tasks, which makes explicit boundaries more important, not less. OpenAI's guidance recommends a compact autonomy policy stated once: name what counts as a safe, in-scope action the model can take without asking, and separately name what requires confirmation, such as "external writes, destructive actions, purchases, or a material expansion of scope." Repeating instructions like "ask first" in multiple places, the guide notes, tends to produce unnecessary approval requests for actions that were already safe.

Tool descriptions matter more than they used to. The guidance asks for tool descriptions that state "what the tool does, when to use it, important return fields, and error behavior." It also asks to expose only the tools relevant to the current task rather than the full set by default.

The family also adds Programmatic Tool Calling, a mode for bounded workflows where "code can process several tool results or large intermediate outputs and return a much smaller structured result." OpenAI's guidance is careful to scope it: multiple or parallel tool calls alone do not justify it, and it fits filtering, deduplication and aggregation stages, not steps that need approval, citations, or semantic judgment between calls.

Does GPT-5.6 handle frontend and vision tasks differently?

Yes, and OpenAI calls this out as its own case rather than folding it into general prompting advice: "GPT-5.6 has stronger layout, visual hierarchy, and design judgment." Even so, the recommendation for incremental frontend work is to work within what already exists rather than let the model redesign freely: preserve the existing design tokens, components and patterns, avoid adding features or decorative UI that were not requested, keep responsive behavior and expected states intact, and render and inspect the result before calling it finished.

For vision, computer use, localization or OCR tasks where exact pixel or coordinate precision matters, the guidance recommends choosing image detail deliberately rather than leaving it on a default: "Use original detail for large, dense, or coordinate-sensitive images when the extra input cost and latency are justified." That clause is a cost note as much as a quality one: higher image detail is a real token and latency cost, worth reserving for tasks where the extra precision actually changes the answer.

How should you handle grounding, citations and retrieval?

For any task that needs supported answers rather than fluent guesses, citation behavior belongs in the prompt itself, not left to the model's judgment. OpenAI's guidance is specific about what that means: "Define what needs support, what counts as enough evidence, and how to behave when evidence is missing. Absence of evidence should not automatically become a factual “no.”"

The same guidance gives a concrete search pattern rather than a vague search-when-needed instruction: "For ordinary Q&A, start with one broad search using short, discriminative keywords. If the top results contain enough support for the core request, answer from those results." A second retrieval call is reserved for specific gaps, not general thoroughness: "Make another retrieval call only when a required fact, owner, date, ID, or source is missing; the user asked for exhaustive coverage or comparison; a specific artifact must be read; or an important claim would otherwise be unsupported."

For research and synthesis work specifically, the guidance lists five separate disciplines worth writing into a prompt as distinct instructions rather than one blended one: cite only retrieved sources, attach citations to the claims they actually support, label inference separately from directly supported facts, state conflicts between sources rather than silently picking one, and narrow the answer or report missing evidence instead of guessing. Each of those is a different failure mode if omitted, so collapsing them into one generic accuracy instruction loses the specificity that makes it actually work.

How do long-running workflows preserve state and reasoning?

For multi-step or tool-heavy tasks, OpenAI recommends prompting for a short visible preamble before the first tool call, then infrequent updates rather than a running narration: "During the task, update only when a major phase begins or a finding changes the plan. Each update should state one concrete outcome and the next step." The guidance is explicit that narrating routine tool calls is not the goal.

Compaction, the process of summarizing older context to make room for new work, is documented as something to trigger deliberately rather than every turn: "Compact after major milestones rather than every turn. Keep the prompt functionally consistent after compaction and treat compacted items as opaque state." Persisted reasoning across turns gets a specific caution worth prompting around directly: "Do not treat persisted reasoning as an always-on optimization: stale reasoning can add tokens, increase latency, and anchor the model to an outdated approach." In other words, carrying reasoning forward is a choice to make per task, not a default to leave switched on, because it has its own token cost on top of whatever the current turn needs.

Does reasoning carry over between Sol, Terra and Luna?

Within the family, yes. OpenAI's docs state it directly: "Persisted reasoning can be reused only within the same model family. For example, gpt-5.6-sol, gpt-5.6-terra, and gpt-5.6-luna can reuse each other’s reasoning, but reasoning does not carry between the GPT-5.6 and GPT-5.5 families. When you switch model families, the API omits incompatible reasoning from the model’s context".

That makes switching between Sol, Terra and Luna mid-conversation, using reasoning.context in the Responses API, a genuinely supported pattern rather than something you have to work around. Switching from GPT-5.5 to GPT-5.6 is a harder boundary: the API will not carry the old reasoning forward, so treat that specific switch as a fresh start for context management purposes, even if the rest of your conversation history persists normally.

Which variant should you actually use?

Route by task difficulty and latency tolerance, not by a sense that one variant is "better" at prompting. For the hardest problems where quality matters more than speed or cost, use the base gpt-5.6 alias (which is Sol) and add reasoning.mode: "pro" when the task can tolerate the extra latency. For everyday work where you want most of that intelligence without Sol's price, Terra is the documented middle tier, at roughly half Sol's input and output cost. For high-volume, cost-sensitive or latency-sensitive workloads, such as classification or routing steps inside a larger pipeline, Luna is built for exactly that, at a fraction of Sol's per-token price.

None of this changes what you write in the prompt itself. Write one well-structured prompt using the eight-part skeleton above, decide your reasoning mode and effort level based on how hard the task actually is, and then choose the model ID based on how much that intelligence is worth to you for this specific call. That is the real answer to which GPT-5.6 model you should use, and it is a cost and latency decision dressed up as a model choice.