GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 series, focused on complex tasks requiring sustained planning, iterative verification, and tool collaboration. It has clear strengths in programming, professional knowledge work, scientific analysis, and defensive security, and can also combine image understanding with interfaces and materials, making it suitable for advancing complex requirements into reviewable code, analysis results, and deliverables.
Clarify capacity, input/output, and invocation methods before selecting a model.
Native reasoning and agent capabilities describe how the model works; use this platform's input organization, output format, and tool configuration according to the selected invocation endpoint.
Learn what gpt-5.6-sol can bring to your work.
Sol's programming strengths go beyond code completion to handling engineering tasks that require planning, iteration, and tool coordination. It is suited to analyzing cross-file dependencies, identifying causes of failures, and proposing modification plans. When used with testing tools, it can repeatedly check against acceptance criteria, producing code changes, issue explanations, and validation steps rather than merely isolated snippets.
When faced with disorganized business materials, Sol can organize arguments, clarify constraints, and help create documents, presentation content, and spreadsheet analyses. Its design judgment and ability to follow reference formats are well suited to template-based deliverables: it considers not only whether content is complete, but also layout and hierarchy. Creating, rendering, and saving final files requires the appropriate tools.
Sol can analyze text and images together, making it useful for understanding interface screenshots, charts, or reference layouts. When submitting images, also describing the areas to inspect and the evaluation criteria helps produce more focused explanations. Visual results can be further turned into modification suggestions or code plans, but understanding images does not mean directly generating or editing them.
Start with specific tasks to find where the model can make an impact.
Provide the relevant code, change diffs, error logs, and testing requirements, and have Sol first identify the scope of impact before proposing an order of changes. Deliverables can include a list of blocking issues, a patch draft, and regression testing recommendations. When actual execution is needed, the application runs the tests and returns the results, giving subsequent revisions real feedback rather than relying solely on static judgment.
Provide organized source text, key data, and images of reference pages, and have Sol produce a report structure, supporting arguments for conclusions, presentation-slide content, or spreadsheet calculation plans. It is suitable for research, financial, and business analysis tasks that require consistent narratives and formatting; prompts should clearly specify which content must be retained and which inferences need their basis listed separately.
Within authorized scope, provide code, system constraints, and known issues, and have Sol assist with security reviews, threat modeling, and patch checks; it can also design analysis steps based on research questions and organized data. Deliverables should include assumptions, validation methods, and items requiring confirmation, so engineers or researchers can review them without treating model judgments as verification results.
Choose based on task complexity, input materials, and expected results.
Sol, Terra, and Luna are different capability tiers in the same generation, not different spellings of one model. Multi-step engineering, specialized analysis, or tasks that are difficult to complete in a single pass are better suited to Sol; for everyday work, consider the balanced Terra, while a large volume of lightweight tasks may benefit from Luna's emphasis on speed and cost efficiency. When choosing, prioritize comparing the completion quality of actual tasks rather than looking only at response length.
Compared with GPT-5.5, the public improvements in GPT-5.6 Sol focus on complex programming, scientific work, visual design judgment, and adherence to reference formats. Existing GPT-5.5 applications can compare modification accuracy, template consistency, and review quality using representative tasks before deciding whether to migrate. max is suitable for questions that merit deeper reasoning; Ultra is a separately configured multi-agent way of working.
From a small-scale task to full integration.
Define the goal, required inputs, and output requirements, using real business examples as a starting point.
Open the trial page, confirm the parameters supported by this entry point, then submit a small-scale task to review the results.
Keep the complete model ID, use the request format specified in the documentation, and confirm billing rules on the Pricing page.
Before formal use, understand output quality and capability scope.
Answers to common questions about using gpt-5.6-sol.
Sol is the flagship tier, designed primarily for complex reasoning and demanding work; Terra emphasizes balance for everyday work, while Luna emphasizes speed and cost efficiency. For engineering or professional analysis that requires ongoing review, Sol may be the preferred choice; for tasks such as simple classification and summarization, consider evaluating other tiers first.
It can interpret images together with text, for example by reviewing UI screenshots or explaining charts. Chat Completions can structure image messages through image_url and include specific questions. This is a visual analysis workflow and should not be treated as native image generation or image editing capability.
No. max allows the model to spend more time reasoning deeply, making it suitable for complex analysis and repeated review; Ultra advances tasks through parallel collaboration among multiple agents. Basic gpt-5.6-sol requests do not automatically enable Ultra, and setting a higher reasoning level does not mean creating a multi-agent workflow.
Existing message-based applications can use Chat Completions and submit content with messages; responsive workflows can use Responses and submit content with input. For simplified Q&A and continuous conversations, you can choose conversations, ask with question, and include stateful and id.
It can generate code, plan review steps, and organize document or presentation content, but running code, rendering pages, and saving office files require the appropriate tool environment. When using it, return tool results to the model and set testing or delivery acceptance criteria to distinguish content generation from actual execution completion.
Model information · Updated: 2026-10-01. For calling parameters and billing rules, see the API and pricing sections.
Start with a clear goal and judge from real results whether it is suitable for your work.
Input
$0.80per 1M tokens
Official $4.00
Output
$4.00per 1M tokens
Official $20.00
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This is only a basic call example. See the full docs for more parameters and advanced usage.
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