GPT-5.6 Sol vs Terra vs Luna: Which to Use in 2026

OpenAI released GPT-5.6 Sol, Terra, and Luna for general availability on July 9, 2026. They are not three modes of the same model. They are separate models built for different tradeoffs between capability, speed, and cost.

The mistake is assuming the flagship should be your default. Sol is the strongest model, but it is also twice the price of Terra and five times the price of Luna on both input and output tokens. For most builders, Terra is the practical starting point.

GPT-5.6 Sol vs Terra vs Luna at a glance

All three models are available through the OpenAI API. OpenAI describes Sol as its flagship, Terra as its balanced everyday model, and Luna as its most cost-efficient model.

The model names also work directly in the Responses API:

• gpt-5.6-sol
• gpt-5.6-terra
• gpt-5.6-luna

The gpt-5.6 alias points to Sol, according to OpenAI's model guidance. If you care about controlling cost, specify Terra or Luna instead of relying on the family alias.

GPT-5.6 pricing compared

OpenAI prices all three models per million tokens. Cached input is 90% cheaper than standard input, while a cache write costs 25% more than standard input.

Prices are from the official OpenAI API pricing page and are accurate as of July 11, 2026.

The ratios are unusually clean:

• Terra costs 50% less than Sol.
• Luna costs 80% less than Sol.
• Luna costs 60% less than Terra.

Imagine a monthly workflow that uses 20 million input tokens and 5 million output tokens, without caching:

That is a $200 monthly gap between Sol and Luna for the same token volume. The right question is not which model is smartest. It is how much intelligence the task actually needs.

💡 Tip: Route by confidence, not prestige. Use Luna for tasks with clear inputs and machine-checkable outputs. Use Terra when judgment matters. Reserve Sol for tasks where a wrong answer is expensive or a weaker model repeatedly gets stuck.

Where GPT-5.6 Sol wins

Sol is the flagship. It is the model to use when the task is genuinely hard and the quality difference can justify the extra cost.

Complex coding and terminal work

OpenAI reports that Sol scores 88.8% on Terminal-Bench 2.1 and 91.9% with its ultra setting. Terminal-Bench evaluates multi-step command-line tasks that require planning, tool use, debugging, and recovery from errors.

Sol is the safer choice for:

• Large repository changes
• Difficult debugging across multiple services
• Security reviews and vulnerability analysis
• Architectural decisions with several constraints
• Long-running coding agents that must recover from mistakes

If you use an AI coding environment, compare Sol with the alternatives inside the same project. Our Claude Fable 5 vs GPT-5.6 Sol comparison covers the closest frontier-model matchup. You can also use several model providers through Cursor.

Multi-agent work with ultra

Sol supports ultra, OpenAI's highest-capability setting. It coordinates multiple agents across parallel workstreams and combines their results. This is useful for tasks that split naturally, such as researching a market while analyzing data and building a presentation.

Ultra is not a reason to use Sol for every prompt. Parallel agents increase compute and can create redundant work. Save it for projects where multiple independent workstreams will actually shorten the path to a better result.

Research, computer use, and polished deliverables

OpenAI reports that Sol reaches 92.2% on BrowseComp and 62.6% on OSWorld 2.0. It also emphasizes improvements in presentations, documents, spreadsheets, and frontend design.

Use Sol when the final deliverable needs both deep analysis and a polished output. A board presentation built from several source documents is a good Sol task. Extracting invoice totals from a fixed template is not.

Where GPT-5.6 Terra wins

Terra is the model most people should try first. OpenAI positions it as the balanced option for everyday work, and its pricing is exactly half of Sol's.

Terra makes sense for:

• Daily coding and feature development
• Product research and competitive analysis
• Drafting and editing business documents
• Spreadsheet analysis
• Customer support workflows that require judgment
• AI agents that use tools but do not need frontier-level reasoning on every step

OpenAI says Terra surpasses GPT-5.5 on several agentic evaluations at a lower cost. Those results are vendor-reported, so test them on your own workflow. Still, Terra's position in the lineup is clear: it is meant to deliver near-frontier performance without flagship pricing.

🔑 Key insight: Terra is not the "medium" option you settle for. It is the economic default. If Terra completes 95% of your tasks successfully, paying twice as much for Sol across every request is difficult to justify.

For app builders, Terra is a sensible default model behind features where users expect thoughtful output but not maximum intelligence. If you are learning to build those products, see our AI SaaS Course or our guide to the best AI app builders for vibe coding.

Where GPT-5.6 Luna wins

Luna is the fastest and cheapest member of the family. It costs $1 per million input tokens and $6 per million output tokens.

Use Luna for repeatable, high-volume work:

• Classifying support tickets
• Extracting fields from structured text
• Tagging and routing content
• Summarizing short documents
• Reformatting data into a known schema
• Generating first drafts that a stronger model or human will review

OpenAI says Luna nearly matches GPT-5.5's peak performance on some agentic evaluations at less than half the estimated cost. That does not mean Luna is interchangeable with Sol. It means smaller models are now capable enough to handle work that previously required a flagship.

The best Luna workflows include a validator. Check a JSON schema, verify a database lookup, compare a calculated total, or send low-confidence cases to Terra. Cheap inference becomes much more useful when failure triggers an automatic escalation.

The best model for coding

For difficult coding, pick Sol. For normal product development, start with Terra. Use Luna for narrow coding tasks with strong tests.

A useful workflow is to plan with Sol, implement with Terra, and delegate repetitive changes to Luna. You do not need one model to perform every step.

If you are working inside Cursor, our vibe coding with Lovable and Cursor guide explains how model choice fits into a broader building workflow.

How to route tasks across all three models

You can build a simple escalation ladder:

1. Send predictable, low-risk tasks to Luna.
2. Send ambiguous or judgment-heavy tasks to Terra.
3. Send high-stakes or unusually complex tasks to Sol.
4. Escalate automatically when the first model fails validation or reports low confidence.

For API applications, call each model by its explicit model ID. OpenAI recommends starting from your existing GPT-5 settings, then comparing one lower reasoning level on representative tasks. The family supports reasoning effort controls, so model choice is only one cost lever. A lower reasoning setting can also reduce latency and token use.

Do not route solely by prompt length. A 5,000-word classification task may be easy for Luna, while a three-sentence production bug can require Sol. Route based on ambiguity, consequence, and how easily you can verify the result.

⚠️ Heads up: OpenAI's benchmark numbers are vendor-reported. They are useful directional evidence, not proof that one model will win your workload. Run a small evaluation set using your real prompts, expected outputs, and failure criteria before changing production routing.

Which GPT-5.6 model should you use?

Choose GPT-5.6 Sol if:

• The task is difficult enough that Terra has failed
• Errors could create meaningful financial, security, or reputational damage
• You need multi-agent coordination through ultra
• You are solving complex coding, science, research, or computer-use tasks

Choose GPT-5.6 Terra if:

• You need one dependable default for daily work
• The task requires judgment, analysis, or tool use
• You want strong capability without paying Sol prices
• You are building an AI product and need predictable unit economics

Choose GPT-5.6 Luna if:

• The task is repetitive and high volume
• Outputs can be checked automatically
• Speed and cost matter more than maximum reasoning
• You have an escalation path for uncertain cases

For most people, the answer is Terra. Sol is the specialist you bring in for the hard cases. Luna is the efficient worker you give clear, testable jobs.