A model’s stated training cutoff can tell you when its training data may stop, but it cannot tell you whether the model can do a particular job. In Waldek Mastykarz’s reported tests of GPT-5.6 Luna on Dev Proxy and SharePoint Framework tasks, successes and failures appeared across product histories rather than lining up at a single version boundary. To choose a model for real work, test representative tasks under controlled information conditions.
What a model’s knowledge cutoff does—and does not—tell you
A stated cutoff is metadata about the possible temporal reach of a model’s training data. It does not show how extensively a particular product or topic appeared in that data, whether the model retained the relevant details, or whether it can apply them correctly to a task.
That distinction matters because knowing a fact and doing useful work are different things. A model may answer a question about a recent release yet fail a task involving an older version; conversely, it may solve a task involving a release after its stated cutoff by applying familiar patterns or making a correct inference. Neither outcome, by itself, establishes a reliable knowledge boundary.
As Mastykarz, a Principal Developer Advocate, puts it: “The cutoff gives you a date but it’s the eval that tells you whether the model can do the work.” (Microsoft for Developers, September 21, 2026.)
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What the Dev Proxy and SharePoint Framework results showed
Mastykarz described an evaluation of GPT-5.6 Luna using tasks based on Dev Proxy and SharePoint Framework changes. Results were uneven across product versions, which makes a single cutoff date a poor summary of performance in these particular tests.
| Product | Tasks passed | Versions represented | Reported pattern |
|---|---|---|---|
| Dev Proxy | 61 of 336 (18%) | 53 | For version 0.3.0, 4 of 5 tasks passed; for 0.4.0, 0 of 5 passed. |
| SharePoint Framework | 61 of 413 (15%) | 40 | Successes and failures appeared across product history. |
These figures are from Mastykarz’s described experiment, not universal performance rates or an independent replication. The denominators matter: the pass counts alone do not make the two product evaluations directly comparable. The reported results are specific to the model, task sets, versions, and scoring rubrics used. (Microsoft for Developers.)
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Post-cutoff passes do not establish a new knowledge boundary
Mastykarz reported a February 16, 2026 stated cutoff for GPT-5.6 Luna. In the experiment, the model passed 1 of 2 tasks for each of Dev Proxy versions 2.3.4, 3.0.0, and 3.1.0, which were released after that date. Those passes do not prove the underlying ideas first became public with those releases: the model could have inferred an answer from familiar patterns or guessed correctly. (Microsoft for Developers.)
How to evaluate a model for your own work
For a practical model choice, ask how well candidates perform on the work you actually need done—not which product version they claim to know. Mastykarz contrasts the question “What’s the latest version of this product the model knows?” with the more useful “How capable is this model of working with this product without additional information?”
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- Define the workload. List recurring tasks, the product versions involved, and what a correct result must include. Use concrete requests and an explicit rubric rather than asking only for broad product knowledge.
- Build representative test cases. Draw examples from the changes, documentation, or workflows your team encounters. Include varied versions and task types so one easy example cannot stand in for the entire workload.
- Keep conditions consistent. Give each candidate model the same tasks and the same access—or lack of access—to documentation, web search, and other tools. If you want to measure internal model knowledge, remove external information; otherwise, the test measures the model plus those information sources.
- Score against the rubric. Record correct, incomplete, and incorrect results for each task. Compare rates using the same denominator, and inspect which task types or versions account for successes and failures.
- Repeat with useful context. Run the same tasks again with the documentation or agent extensions you expect to use in practice. The difference from the baseline shows how much those additions change results for your workload.
This process produces a decision tied to your tasks and information setup. It does not turn one evaluation score into a guarantee for other products or work.
Keep the information boundary clear
A test can answer different questions depending on what the model can access. With documentation or web search enabled, a successful answer may show that the overall setup can find and use information—not that the model already knew it. With those sources removed, the test more closely isolates internal model capability, but it no longer represents a workflow that relies on live references.
State which question you are testing before comparing results. Mastykarz removed external information, including documentation and web search, when measuring internal knowledge in his experiment, because outside access can invalidate that measurement. (Microsoft for Developers.)
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the evidence can—and cannot—support
The Dev Proxy and SharePoint Framework results illustrate why cutoff dates alone are not a capability test. They do not show that every provider’s cutoff disclosure works the same way, nor do they establish performance across all models, products, or tasks. They describe one author’s experiment, with generated tasks and rubrics for two software domains.
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There is also a broader evaluation-design caution for forecasting: an IJCAI 2026 paper abstract argues that retrospective forecasts about already-resolved events can be flawed when a model may know the outcome, and recommends against simulated-ignorance retrospective setups. That is a warning about forecasting evaluation—not direct evidence about product-specific coding capability. (IJCAI 2026.)
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