GPT-5.6 Luna vs Terra vs Sol: Complete Comparison
The GPT-5.6 Luna vs Terra vs Sol comparison is not simply a choice between small, medium, and large context windows. OpenAI gives all three API models the same published context length, maximum output, vision capability, tool support, and range of reasoning settings.
Their primary differences are intelligence, speed, intended workload, and price.
Quick Answer: GPT-5.6 Sol is OpenAI’s flagship for complex professional reasoning and coding. Terra balances intelligence and cost for everyday production workloads. Luna is the fastest and lowest-cost option for high-volume, cost-sensitive tasks. All three API models support 1.05 million tokens of context, 128,000-token outputs, vision, tools, and adjustable reasoning.
What is GPT-5.6 Sol?
GPT-5.6 Sol is OpenAI’s flagship GPT-5.6 model.
OpenAI recommends Sol as the starting point when users are unsure which member of the family to choose. It is designed for:
- Complex reasoning
- Software engineering
- Research
- Scientific work
- Cybersecurity
- Computer use
- Design
- Knowledge work
- Long multi-step agent tasks
In standard ChatGPT, GPT-5.6 Sol powers the Medium, High, and Extra High reasoning options on eligible plans.
GPT-5.6 Sol Pro is a separate, higher-capability model available through the Pro reasoning option. It should not be confused with ordinary Sol.
What is GPT-5.6 Terra?
GPT-5.6 Terra is positioned as the balanced model.
It combines stronger capability than a lightweight high-volume model with lower cost than Sol.
Terra is suited to workloads such as:
- Business-document extraction
- Routine code generation and review
- Support-ticket analysis
- Structured research preparation
- Data transformation
- Content-production workflows
- Internal business agents
- Repeated professional automation requiring moderate judgment
Terra is also the GPT-5.6 model available to Free and Go users inside Codex. It is not currently selectable in ordinary ChatGPT conversations.
What is GPT-5.6 Luna?
GPT-5.6 Luna is the fastest and least expensive model in the family.
It is intended for cost-sensitive, high-volume work with clear instructions and easy validation.
Suitable tasks include:
- Classification
- Tagging
- Routing
- Simple extraction
- Format conversion
- Short summaries
- Basic rewriting
- Search-query processing
- Repetitive support operations
- First-pass agent steps
Luna is not automatically the cheapest model for every workflow. If it produces more retries, errors, or human review, Terra or Sol may deliver a lower total cost per approved result.
GPT-5.6 Sol vs Terra vs Luna specifications
OpenAI’s current API catalog lists the following specifications:
| Feature | GPT-5.6 Sol | GPT-5.6 Terra | GPT-5.6 Luna |
|---|---|---|---|
| Positioning | Flagship complex work | Balanced intelligence and cost | Fast, cost-sensitive volume |
| Input price | $5 per million tokens | $2.50 per million tokens | $1 per million tokens |
| Output price | $30 per million tokens | $15 per million tokens | $6 per million tokens |
| Context window | 1.05 million tokens | 1.05 million tokens | 1.05 million tokens |
| Maximum output | 128,000 tokens | 128,000 tokens | 128,000 tokens |
| Reliable knowledge cutoff | February 16, 2026 | February 16, 2026 | February 16, 2026 |
| Reasoning levels | None through max | None through max | None through max |
| Text and image input | Yes | Yes | Yes |
| Functions and tools | Yes | Yes | Yes |
Terra currently costs half as much as Sol for both input and output. Luna costs one-fifth as much as Sol.
The shared context window means model choice should be driven by the required intelligence and reliability, not only by document length.
Which GPT-5.6 model is best?
There is no universal winner.
Choose Sol when:
- The task is difficult or unfamiliar.
- Errors are expensive.
- Several constraints interact.
- Strong coding or reasoning is required.
- The agent must plan over many steps.
- The output is difficult to verify.
- The task involves professional or scientific judgment.
Choose Terra when:
- The task has moderate complexity.
- Both quality and cost matter.
- Luna is not reliable enough.
- Outputs can be tested or sampled.
- You need a practical production default.
Choose Luna when:
- The task is repeated at high volume.
- Instructions are stable and explicit.
- Results are easy to validate.
- Low latency is important.
- Failures can safely escalate.
- Cost per request is a major constraint.
A routing system can begin with Luna or Terra and send uncertain cases to Sol.
Does higher reasoning make Luna equal to Sol?
No.
All three models support reasoning settings from none through max, but reasoning effort does not remove the underlying differences between models.
A high-reasoning Luna request may outperform a low-reasoning Luna request. It does not guarantee Sol-level reliability on complex tasks.
Higher reasoning can also increase latency and token use.
Teams should evaluate combinations such as:
- Luna high vs Terra low
- Terra high vs Sol low
- Sol medium vs Sol high
The most efficient configuration is the lowest-cost model and reasoning setting that passes the required tests.
Can Luna, Terra, and Sol be selected in ChatGPT?
Not in normal ChatGPT conversations.
OpenAI currently documents the following availability:
| Product | Model availability |
|---|---|
| Standard ChatGPT | Sol and Sol Pro where the plan permits |
| ChatGPT Work | Sol, Terra, and Luna for eligible paid plans |
| Codex Free and Go | Terra |
| Codex paid plans | Sol, Terra, and Luna |
| OpenAI API | Sol, Terra, and Luna |
Standard ChatGPT uses GPT-5.5 Instant for everyday fast responses and GPT-5.6 Sol for advanced reasoning.
Users searching for GPT-5.6 Luna in ChatGPT may not find it because Luna is not currently a normal model-picker option.
Do all three have the same context window?
Yes.
The API catalog currently lists:
- 1.05 million tokens of context
- 128,000 maximum output tokens
- February 16, 2026 reliable knowledge cutoff
- Text and image input
- Function calling
- Web search
- File search
- Computer use
Technical input capacity does not guarantee equal quality on very long tasks. Models can differ in retrieval, attention, planning, and reasoning even when they accept the same maximum context.
Which GPT-5.6 model is best for coding?
Sol is OpenAI’s flagship for complex reasoning and coding.
Terra may be more economical for:
- Routine implementation
- Unit tests
- Documentation
- Small bug fixes
- Repeated repository maintenance
- Code review queues
- Migration support
Luna can handle tightly scoped transformations, formatting, classification, and simple explanations.
For production changes, model choice should still be combined with automated tests, static analysis, security checks, and accountable human review.
Which model is best for AI agents?
Different agent steps can use different models.
A cost-aware workflow might use:
- Luna for intent detection and routing
- Terra for extraction and normal operational steps
- Sol for planning, exceptions, and difficult decisions
This approach reduces cost while preserving strong capability for the steps that require it.
Agent designers should use confidence thresholds, tool validation, and explicit escalation rules rather than relying on one model for everything.
How should developers compare the three models?
Use representative tasks from the real workload.
Measure:
- Task success rate
- Factual and logical errors
- Tool-use reliability
- Required human correction
- Latency
- Input and output tokens
- Retry frequency
- Cost per approved result
- Failure severity
- Escalation rate
A model with the lowest token price can still be more expensive if its errors create repeated work.
What is the practical conclusion?
Choose Sol for the hardest professional work, Terra for a balanced production default, and Luna for high-volume tasks with strong validation.
Do not choose only by model name or token price. Run the same evaluation set across all three and compare total cost per verified outcome.
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