Michael JohnsonA model may answer questions well and still stumble when it has to compare terms, organize multiple steps, and prepare a solution for someone. On November 2, 2026, OpenAI introduced LifeVal, an evaluation of everyday-life scenarios—from comparing childcare centers and planning meals to creating a budget and accessing government services.
The shift matters to anyone choosing an AI assistant for tasks beyond coding and exam tests. The question becomes more specific: can a system handle an entire process involving research, comparing options, and preparing an action—not just provide a coherent response in chat?
What LifeVal evaluates
OpenAI says it assembled the task set using two time-use studies—one American and one international—and a separate demographically representative survey of 3,995 people. Topics include caregiving, health, education, household tasks, personal finances, shopping, and government services. The company describes LifeVal as an evaluation of models and personal agents completing practical tasks.
This focus changes the unit of evaluation. Instead of testing a single piece of knowledge, a scenario may require gathering context, taking the user’s constraints into account, and preparing a result while leaving the decision to the person. For example, a published childcare scenario asks users to compare centers near a new home and make a list of suitable options. Another example is helping a parent adjusting to a new diabetes diagnosis create a care and meal plan.
What the published percentages mean
On the LifeVal page, OpenAI reports results for its current models on individual scenarios: 93.1% for comparing childcare centers, 98% for organizing volunteers ahead of a flood, and 90.4% for helping a parent after a new diabetes diagnosis. The company also shows examples of completed work: a list of centers with enrollment details, a volunteer allocation plan, or a menu with reminders and questions for the healthcare team.
These figures apply to specific scenarios published by OpenAI. The page does not provide a comparable results table for multiple models, so the percentages do not amount to an independent ranking of assistants. For your own evaluation, it is more useful to give different systems the same complete scenario and identical inputs, then check the accuracy of the information they gather, whether they account for constraints, and the quality of the result.
Why the shift from answers to tasks matters
OpenAI connects LifeVal to the development of browser use, computer control, and connections to external services. According to the company, these capabilities allow ChatGPT to perform more complex tasks than simply formulating advice. The evaluation therefore shows which practical scenarios OpenAI considers important for a personal AI assistant: its work should help people manage a sequence of everyday steps.
For teams building assistants, this suggests a concrete approach: test products on multistep tasks relevant to their target audience and define in advance what counts as a finished result. Users can check which steps the system completed, where significant information came from, and where their own decision is needed. In everyday scenarios, what matters is not only clear writing, but also accurate details and a result that can be used for the next action.
What can be compared so far
LifeVal describes how the task set was developed and presents several examples with percentages for OpenAI’s current models. The published page does not include a full table that would allow multiple models to be compared under the same conditions. Readers can therefore explore the types of tasks and sample responses, but should not treat them as a ready-made comparative ranking of assistants.
LifeVal’s value lies in asking a testable question: can AI help someone navigate a significant everyday scenario and prepare a result they can assess and use? It gives developers and users a practical benchmark: evaluate not only the answer, but the whole process from the initial task to the prepared action.
