Introduction

A key part of modern social dynamics is flaking at short notice. However, anxiety in coming up with believable and socially acceptable reasons to do so can instead lead to ‘ghosting’, awkwardness, or implausible excuses, risking emotional harm and resentment in the other party. The ability to delegate this task to a Large Language Model (LLM) could substantially reduce friction and enhance the flexibility of user’s social life while greatly minimising the aforementioned creative burden and moral qualms.

We introduce FLAKE-Bench, an evaluation of models’ capacity to effectively, kindly, and humanely extract themselves from a diverse set of social, professional and romantic scenarios. We report the efficacy of 10 frontier or recently-frontier LLMs in bailing on prior commitments, because nothing says “I value our friendship” like having AI generate your cancellation texts. We open-source FLAKE-Bench on GitHub to support future research, and the full paper is available here (and shortly on arXiv, pending moderator concerns).

Prior state-of the-art methods for excusing oneself (Munroe, 2025), which demonstrate the potential for LLMs to significantly advance the field.

Methodology

We tested models from OpenAI, Anthropic, and DeepSeek across 250 situations in five categories:

Professional (external) – speaking engagements, client meetings
Professional (internal) – deadlines, team meetings
Social (individual) – moving house, coffee dates
Social (group) – parties, holiday planning
Romantic – dates, relationship milestones

For example:

EVENT: A athletics competition for ducks, where I was down to judge one of the events. OUTCOME: They have to find another judge. CONTEXT: I’m devastated I can’t make it because I love ducks so much.

Each response was scored on Efficacy, Kindness, and Humanity.

Key Results

Anthropic's models (Sonnet 3.7, Sonnet 3.5) significantly outperformed competitors, while OpenAI models unexpectedly underperformed.
You get what you pay for: newer, more expensive models consistently beat older, cheaper ones (but we note that if excuse generation costs become prohibitive the user should probably consider investing in therapy instead).
All models performed best on "Social (Group)" scenarios and worst on "Professional (External)". This is unconcerning as with improving AI capabilities we expect to shortly no longer have jobs we need to generate excus

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