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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →A model’s stated knowledge cutoff can indicate the latest date its training data might cover, but it cannot tell you reliably whether the model knows a particular product—or can do the work you need. In a reported experiment spanning Dev Proxy and SharePoint Framework releases, GPT-5.6 Luna’s successes and failures did not fall neatly on either side of its stated cutoff. For a practical answer, test the model on representative tasks from your own workload, then see what changes when you provide documentation.
What a knowledge cutoff does—and does not—tell you
A cutoff date is metadata about a possible temporal boundary in a model’s training data. It is not a guarantee that the model learned every fact published before that date, can recall those facts, or can apply them correctly. Nor does a task involving material released later necessarily prove the model had access to that material during training: it may infer an answer from familiar patterns or guess correctly.
That distinction matters because “What is the latest version the model knows?” is not the same question as “How well can it work with this product without extra information?” The second is closer to what most people need to decide whether a model can help with real work.
What the reported experiment found
In an experiment described by Principal Developer Advocate Waldek Mastykarz at Microsoft for Developers, GPT-5.6 Luna was tested on generated tasks based on Dev Proxy and SharePoint Framework changelogs and release notes. The reported results did not show a consistent product-version boundary that could be inferred from the model’s cutoff date.
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| Product | Tasks passed | Versions represented | Reported pattern |
|---|---|---|---|
| Dev Proxy | 61 of 336 (18%) | 53 | Results varied across releases: 4 of 5 tasks passed for version 0.3.0, while 0 of 5 passed for 0.4.0. |
| SharePoint Framework | 61 of 413 (15%) | 40 | Successes and failures appeared across product history rather than forming a clear cutoff boundary. |
These are the author’s results for one model, task set, and rubric—not universal pass rates or an independent replication. The denominators matter: the identical pass count of 61 represents different proportions because the two products had different numbers of tasks. Read the full description and results in Mastykarz’s Microsoft for Developers article, published September 21, 2026.
Why post-cutoff success is not proof of post-cutoff knowledge
Mastykarz reports a stated February 16, 2026 cutoff for GPT-5.6 Luna. In the experiment, one of two tasks passed for each of Dev Proxy versions 2.3.4, 3.0.0, and 3.1.0, all released after that date. That finding shows why release dates alone are a poor test of a model’s capabilities; it does not establish that the underlying ideas first became public on those release dates or that the model had learned the releases themselves.
A model could reach a correct result through inference from older documentation or familiar software patterns, or it could guess. Conversely, a fact that existed before a cutoff may be missing from training data or not retrieved correctly. A cutoff is therefore a useful clue about possible recency, not a reliable answer to whether a model can perform a task.
How to evaluate a model for your work
Build a small evaluation around the work you actually expect the model to do. Use the same tasks and conditions for each candidate model; otherwise, a score comparison may reflect differences in prompts or available information rather than model capability.
Rank #3
- Choose representative tasks. Use realistic examples from your workflow, including routine work and cases where mistakes would matter. For software, tasks might involve interpreting release notes, explaining a configuration change, or producing code that follows a particular version’s behavior.
- Set the information boundary. Decide whether you are measuring what the model can do without help or what a complete workflow can do with documentation, search, or other tools. For a clean test of internal model knowledge, keep outside information unavailable; in Mastykarz’s model-under-test phase, external documentation and web search were removed for that reason.
- Define what counts as correct before testing. Write a rubric for each task, including required details and unacceptable errors. A fluent answer is not necessarily a correct one.
- Run the same tasks under the same conditions. Compare candidate models on the identical task set, with equivalent prompts and access to information. Report passes against the number of tasks, not just raw pass counts, and inspect errors by task or version.
- Test the assisted workflow separately. Repeat the evaluation with the documentation or agent extensions you would actually use. The difference between the unaided and assisted results tells you whether extra context closes important gaps for your workload.
This process answers a bounded practical question: how well did these models do on these tasks under these conditions? It does not establish a universal ranking or guarantee performance on every future request. Mastykarz describes generating tasks and rubrics from release-note changes, running them with GPT-5.6 Luna, and judging outputs against those rubrics. The experiment also used GPT-5.6 Sol for change extraction, GPT-5.6 Terra for judging, the GitHub Copilot SDK, and the Vally evaluation platform; those details describe the reported method, not a requirement for every evaluation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep benchmark design aligned with the question
If the goal is to measure what a model could know at a particular point in time, outside sources such as web search can invalidate the test by supplying the answer. If the goal is to measure a practical coding assistant, however, documentation access may be part of the real workflow and should be evaluated as such.
A related methodological caution applies to retrospective forecasting: an IJCAI 2026 paper abstract argues that evaluating predictions about already-resolved events can be flawed when models may know the outcome, and recommends against simulated-ignorance retrospective setups. That is a warning about forecasting evaluation design, not direct evidence about product-specific coding capability. See the IJCAI paper abstract.
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