What the primary source establishes
OpenAI’s GPT-5.5 announcement includes GPT-5.5 Pro and an API availability update dated 24 April 2026.
The model matters, and so does the product it’s accessed through. Establish whether you’re comparing an API configuration or a consumer or business application, and list any tools that application adds. A model with browsing enabled and one restricted to supplied documents are answering different tests.
Choose the use case before choosing the model
Use a side-by-side scorecard with separate rows for source fidelity, completeness, instruction following, uncertainty and reviewer effort. Avoid a single winner until the weighting reflects the work you actually need. A drafting task may tolerate stylistic revision; an incorrect credential or salary statement needs a different failure threshold.
A hypothetical procurement team can run an advertisement review task and a source-grounded research task separately. If one option needs more corrections in the research task, don’t bury that result in an average across easier drafting tasks. Keep the examples and the human reviewer’s reasons for the final choice.
Compare like-for-like access routes
OpenAI’s release page distinguishes GPT-5.5 from GPT-5.5 Pro and includes an API availability update. Anthropic’s Opus 4.7 page describes a model available through its product and platform offerings. These product routes need their own configuration and contract checks; a subscription demonstration and an API benchmark aren’t equivalent purchasing evidence.
Request a row for each proposed configuration: exact identifier, application or API, enabled tools, input format, account plan and date tested. If you’re evaluating AI recruiting agents, add the human review step to that row as well. Mark unknowns explicitly. Confirm current availability in the intended account before building a migration timetable around either option.
Use a decision matrix with task-specific weights
For source-grounded research, weight correct citations and handling of unavailable evidence. For document drafting, weight factual fidelity and editing effort. For a coding task, use executable tests and code review. Explain the weights before the trial so the result cannot be changed to favour a preferred brand after the outputs arrive.
An example matrix can use rows for source accuracy, instruction following, missing-information handling, output usability, review time and total cost. Record both the score and the example behind it. If a critical fact is invented, flag that failure directly rather than allowing polished style to cancel it out.
Evaluate the application around the model
Ask where approved data travels, which logs retain it and who can access the outputs. Check the relevant commercial terms for each service rather than assuming that all products sold by a provider use identical settings. Include deletion, export and account-revocation questions in the review.
For migration, retain a comparison set and run both configurations on it before changing the production route. Keep a named human responsible for accepting the result. The final decision may approve different models for different tasks; this article doesn’t declare a universal benchmark winner or imply that either has been validated for hiring decisions.
Design a comparison that answers your question
Choose a task you can score without relying on a fluent answer: extracting agreed fields, finding evidence in a supplied document, or producing a draft that an editor checks. Use the same input, instructions, tool access and output requirements for each model. Record the exact model identifier and test date rather than treating a product family name as a reproducible configuration.
Prepare expected results before reading the generated answers. Mark unsupported assertions, missing evidence, wrong source references and failures to follow instructions separately. Include ambiguous and incomplete inputs. Where practical, ask a reviewer to assess the outputs without knowing which model produced them. A confident answer shouldn’t receive credit for filling a gap that the source never resolves.
Account for the whole workflow
Count review time, corrections, failed attempts and external tool calls alongside inference cost and response time. Check the terms for the service and plan you intend to use, including retention, access and permitted data. Test only with information your organisation is authorised to supply. Keep an accountable person responsible for decisions about candidates and for approving external actions.
Write a narrow adoption decision: which task is approved, which inputs are allowed, what requires escalation and when the evaluation will be repeated. A software benchmark can justify investigating a model, but it can’t replace this decision. Save the unsuccessful examples as regression cases so a later model or prompt change must pass the same checks.
Sources and reading
Primary sources checked 9 October 2026. The practical evaluation steps in this article are editorial recommendations, not measured customer outcomes.
