GPT Sol: professional work across generations.

From GPT-5.6 Sol to GPT-6.1 Sol

The GPT-5.6 family including Sol was announced for general availability on 9 July 2026. The API changelog records GPT-6 Sol on 22 September and GPT-6.1 Sol on 29 September 2026. This lasting page follows the family, preserving useful references without creating a dictionary article for every minor version.

Progression involves more than a higher number. Settings, processing, rates and task performance can change. Record the complete version for a production workflow. A note saying only that Sol was used may omit information needed to explain a later difference in behaviour or output.

GPT-6.1 Sol appears in the current recommended catalogue at the review date. Earlier Sol variants have not necessarily disappeared everywhere; user documentation describes models retained during rollout. Distinguish the newest recommended version, a still-available version and one that has been officially retired.

Sol’s place in the current offering

OpenAI describes GPT-6.1 Sol as an option for complex coding and professional work at lower cost than Astra. Its language about near-Astra performance is vendor positioning, not universal proof of equivalence on every research task, document or program. Different workloads can reveal different trade-offs.

A marketing team can run a more relevant comparison using its own dossier. Give suitable models the same task and assess source use, completeness and correction work. Measure the whole assignment too. A faster first response is not necessarily a faster finished file if extensive revisions follow.

Luna remains worth comparing for bounded tasks. Terra provides context for older workflows. These family entries are not a ranking that prescribes one choice for everyone. They explain the offering so that a practical trial can start from a reasonable candidate.

Analysis, documents and research

Suppose you need to compare three proposals for a website. Supply the documents, define criteria and ask for the relevant passage under each criterion. Separate assumptions and missing information. This requests a comparison on a common basis, rather than three unrelated summaries that leave the actual comparison to the reader.

For document work, supply an approved example. Ask for the same structure using information from a new dossier, identifying what must remain fixed and what may change. A business letter, product page and research note have different requirements. The word professional alone does not communicate those requirements sufficiently.

Current research requires sources and search tools. A recent model release does not replace reading recent publications. Ask for dates, references and a distinction between facts and interpretation. An inaccessible source should be reported rather than treated as if the research were complete.

End with open decisions: what can the reader decide now, and which missing information could change the conclusion? This keeps a long investigation connected to its original purpose. Report length matters less than whether the material helps the user make the intended decision.

Sol inside a programming environment

Within Codex, Sol can participate in a workflow with repository access, a terminal and checks. The model helps understand code and plan changes; the environment makes execution and review possible. A standalone model call does not automatically have the tools of a fully configured Codex task.

Consider a faulty product-catalogue filter. Describe the observed behaviour, intended behaviour and a reproducible example. Have the relevant code inspected, a focused change prepared and the affected behaviour checked. This produces a traceable process instead of an unexplained block of replacement code.

Ask for a short account of the change and verification. A passing test establishes only the behaviour it covers; checks not run should be identified. Larger changes benefit from human code review. Selecting a capable model does not automatically assign it the role of project owner or final approver.

Separate model, reasoning effort and speed

GPT-6.1 Sol's API documentation supports low, medium, high, xhigh and max reasoning effort, with medium as its API default. None and minimal are not supported for this model. Product interfaces may show other labels or defaults depending on the client and account.

Reasoning effort affects processing allowance. A speed mode concerns how the service delivers that processing and may carry separate usage conditions. Model, reasoning effort and speed mode are therefore distinct choices. Record them separately when comparing outcomes or estimating consumption.

Increase reasoning when the task genuinely needs deeper planning or analysis. A short, clearly defined conversion may not need it. If a result fails because source information is missing, address the source first. Giving an incomplete task more time can produce a longer answer without supplying the absent evidence.

Availability and technical limits

The current rollout documentation lists Plus, Pro, Business, Enterprise and Edu for GPT-6.1 Sol in the specified Work and Codex environments. Free and Go are not included in that launch. Enterprise and Edu require administrator enablement according to that documentation. Work or Codex availability does not imply availability in ordinary Chat.

The API page lists text and image input, text output and a context window of 1,050,000 tokens. This is a model-level API specification, not a promise that every ChatGPT plan accepts that amount of material. A larger window still benefits from deliberate document selection and source checking.

For a new integration, consult the current endpoint and model documentation. For an established workflow, compare before switching. Sol is particularly relevant when complex work recurs and both quality and consumption matter. The most persuasive evidence is a series of useful results on the tasks your organisation actually needs.