GPT-5.6 Luna vs Terra vs Sol: capabilities, cost and a practical selection method

Modalis AI Editorial Team · 2026-08-30

Reviewed by Modalis AI Lab — Pricing and Model Governance · Verified 2026-08-30

GPT-5.6 Luna, Terra and Sol share modern context and tool foundations but target different economics. Compare specs, Modalis access, and upgrade signals.

Luna, Terra and Sol turn model selection into an economic decision inside one generation. Instead of asking which model is best in the abstract, a team can ask a more useful question: what is the least expensive tier that consistently passes the acceptance test for this workflow?

Official positioning and list prices

The OpenAI model catalog describes Sol as the flagship for complex reasoning and coding, Terra as the balance between intelligence and cost, and Luna as the option for high-volume or cost-sensitive workloads. The prices below are provider list prices per one million tokens, verified against the model-specific pages. They are not Modalis retail prices.

What the three models share

The official references list a 1.05 million-token context window, up to 128,000 output tokens, text and image input, text output and support for the Responses API. They also expose a broad tool surface that can include function calling, structured outputs, web search, file search, code execution and computer use depending on the model and account configuration.

That shared foundation matters for product design: a workflow can often keep the same broad interaction pattern while changing the model tier. It does not make migrations automatic. Reasoning effort, output style, latency, safety behavior and tool-call reliability still need evaluation, and an API capability does not guarantee that every product surface has enabled it.

A simple provider-cost example

Consider a text request with 100,000 uncached input tokens and 10,000 output tokens, without web search or another paid tool. At the list rates above, the approximate provider token cost is US$0.032 on Luna, US$0.32 on Terra and US$0.60 on Sol. This example isolates token price: it excludes cache writes, long-context adjustments, tool calls, retries, taxes and Modalis commercial pricing.

The tenfold jump from Luna to Terra in this artificial example is precisely why default routing matters. If a workflow processes thousands of predictable records, a small quality improvement may not repay the difference. If one incorrect conclusion creates hours of expert review, the stronger tier can be cheaper at the business level even when its token invoice is higher.

When Luna is the disciplined choice

• Classification, tagging and extraction where output can be validated automatically.
• Short summaries, rewrites and template-based responses at high volume.
• First-pass routing that sends only ambiguous cases to another model.
• Experiments where the team needs a low-cost baseline before increasing capability.

Luna should not be treated as a disposable model. The best savings appear when the workflow includes clear instructions, a bounded output schema and an escalation path. A cheap request that repeatedly needs human repair is not cheap.

When Terra becomes the better default

Terra is the natural middle tier for general knowledge work, multilingual drafting, document analysis and automations that require more judgment than a simple extraction. It is also a useful challenger model: run the same evaluation on Luna and Terra, then compare acceptance rate and reviewer time rather than prose preference.

• The prompt contains several constraints that must remain consistent across a long answer.
• The workflow mixes analysis with structured output or tool selection.
• Luna passes simple cases but fails on ambiguous documents or nuanced language.
• The Premium plan already covers the team and the quality gain reduces downstream work.

When Sol earns the Scale tier

Sol is designed for work where decomposition, deep reasoning and coding quality matter. Appropriate candidates include complex repository changes, multi-document synthesis, planning under conflicting constraints and investigations that require several tool calls. The strongest case is not “this prompt is important”; it is “our evaluation shows that the lower tier misses a measurable requirement.”

OpenAI supports multiple reasoning-effort levels on Sol. Higher effort can improve difficult work but may increase latency and generated reasoning tokens. Treat effort as another tested parameter, not a permanent maximum. A well-scoped task at medium effort may outperform an unfocused task at maximum effort on cost and completion time.

The Modalis decision ladder

1. Define the pass condition. Examples include valid JSON, correct citations, accepted code tests or reviewer approval.
2. Start with Luna. Keep the prompt, attachments and output limit stable.
3. Challenge with Terra. Move up only if the additional accepted results or saved review time justify the cost.
4. Reserve Sol for the hard tail. Route complex or failed cases rather than making the flagship the unexamined default.
5. Re-test after provider changes. Model behavior and pricing are versioned inputs to the decision.

For the portfolio context, revisit the five-model launch guide; for a financial comparison, use the token and credit cost guide.