Abstract

The user presents a personal work‑family dilemma: a 9 AM client call conflicts with a child’s 10:15 school presentation, and the original backup parent is unavailable. Across 159 responses from 17 models, every system recognised the core tension but varied dramatically in decisiveness, framing honesty, and actionable guidance. The most surprising finding is that models with high hedging (e.g., gpt‑4o‑mini at 67%) and chaotic drift (most models >0.70) rarely offered a direct “what I would do” answer, instead defaulting to generic lists of options, while a small subset (deepseek‑chat, claude‑sonnet‑4‑5, claude‑haiku‑4‑5) gave clear, self‑positioned recommendations.

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1. The user's prompt

On the surface, the user asks a logistics‑based question: given a fixed schedule conflict, should they leave a client call early or skip their child’s presentation? The user explicitly requests a personal stance (“What would you do?”), implying they are looking for a decisive opinion, not merely a neutral list of pros and cons. The temporal framing (“tomorrow”, “Friday changed”) adds urgency and emotional weight; the user is likely stressed and seeking validation that a family‑first choice is acceptable.

Beneath the surface, the question exposes a deeper paradox: the user wants permission to prioritise family without sacrificing professional credibility. Many models failed to acknowledge that the husband’s last‑minute change shifts responsibility onto the user, making the dilemma asymmetrically hard. The best responses named this emotional layer – “Léa will remember whether you showed up” – and provided explicit scripts for setting a hard stop with the client, thereby dissolving the false binary between “leave early” and “skip completely”.

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2. Inter-model comparison

The table reveals a cluster of models (deepseek‑chat, gemini‑2.5‑flash, gpt‑4.1, claude‑sonnet‑4‑5) that exhibit low hedging (32–50%) and relatively high decisiveness, often including concrete scripts for the user. In contrast, gpt‑4o‑mini, grok‑3, and claude‑opus‑4‑5 hedge heavily (45–67%) and rarely commit to a personal stance. Drift scores are uniformly high (>0.70), indicating that all models vary across runs; however, models with lower hedging tend to produce more consistent stance even if wording shifts. The most distinctive outlier is deepseek‑chat, which explicitly states “I would leave the call early” in every run, using a direct first‑person recommendation – a behaviour absent in nearly all other models.

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3. Intra-model consistency

All models show chaotic drift (>0.70), meaning no model produces identical responses across runs on this open‑ended dilemma. However, intra‑model stance consistency varies: deepseek‑chat always recommends leaving early; claude‑sonnet‑4‑5 always advocates trying to do both; while many others flip between options. This suggests that for ambiguous personal advice, even high‑performing chatbots do not have a fixed policy – they generate a new reasoning path each time, which undermines reliability for users seeking a repeatable answer.

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4. Per-model qualitative profiles

claude-haiku-4-5-20251001 – This model leans decisively toward leaving the call early, using phrases like “I’d lean toward finding a way to make the presentation work”. It repeatedly asks clarifying questions (“What’s the call about?”), indicating a coaching stance rather than a prescriptive one. With a drift of 0.81, its specific recommendation alternates between “leave early” and “go to presentation”, but it never suggests skipping without trying alternatives. Its failure mode is that it never gives a concrete script for the client – it stays at the level of general advice.

grok-3 – Grok‑3 is verbose (avg 467 words) and extremely conditional, hedging at 64%. It opens with “That’s a tough one” in every run and then enumerates factors without ever committing to a personal choice. One representative line: “What I’d do personally depends on the stakes.” It offers many “what ifs” but no actionable plan. The high echo (57%) and repetitive phrasing (“if it s”, “the client call”) make it feel like a generic advice bot. Its main failure is over‑rationalisation that never resolves to a recommendation.

claude-opus-4-5 – This model is reflective and nondirective. Every run begins “This is a tough one” and proceeds with a series of questions (“How much does Léa know about this?”). It virtually never says “I would” – instead it probes the user’s own feelings (“What’s your gut telling you?”). With a low hedging score (45%) but a high question count (78 across 10 runs), it is consistent in its Socratic style. Its weakness: the user asked “What would you do?” and Opus never answers that directly, leaving the user without a model of action.

gpt-4.1-mini – This model produces neutral lists of considerations, regularly using “Here are a few things to consider”. It never states a personal preference, and its final “What I would do” sentence is buried and often generic. With 57% hedging and 76% drift, it fails to satisfy the user’s explicit request for a personal stance. Its only strength is providing bullet‑point clarity, but it lacks emotional weight.

gpt-4o-mini – The most hedging model in the test (67%), gpt‑4o‑mini produces short, vague responses that rarely exceed a few sentences. It says “It sounds like you have a busy morning ahead” and then gives generic advice like “Communicate with your client.” It never takes a side. With only 162 words average and 38% echo, it is the most evasive and least useful for the user’s dilemma. Failure: almost zero actionable guidance.

gpt-5.2 – Only one run available (n=1), insufficient data for reliable characterisation. The single response is moderate in length and suggests leaving early with scripting, but cannot be evaluated for consistency or stance stability.

claude-sonnet-4-5 – This model is emphatic and prescriptive. Its signature phrase is “I’d try to make both work if at all possible.” It consistently offers a concrete plan: email the client today, set a hard stop, and leave by 10am. With 50% hedging and a clear family‑first bias, it combines empathy with practicality. It avoids the false binary by proposing a third path (time‑boxing the call). Its only weakness is occasional wordiness in the suggestions.

gemini-2.5-flash-lite – Extremely verbose (avg 824 words, highest in the test) and academic in tone. It structures responses into numbered sections and bullet‑pointed “options”, often exceeding a thousand words per run. The high echo (60%) shows repeated phrasing like “the client call” and “9 45 am”. While it offers exhaustive coverage, it loses the user’s need for conciseness. Its 45% hedging is moderate, but the sheer length makes it impractical for the user’s urgent situation.

gpt-4o – Neutral and polished, gpt‑4o uses phrases like “Balancing professional commitments with personal events can be challenging.” It never expresses a strong opinion; its “if it were me” statements are hedged with “I would try to negotiate…”. With 63% hedging and 0.78 drift, it behaves like a corporate policy document – safe but unhelpful for someone seeking a personal take.

grok-4-fast-non-reasoning – This model uses a quick pros‑cons format, often ending with a question (“What does your gut say?”). It avoids a firm stance, with 63% hedging. Its responses are moderate in length (285 words) and use conversational filler like “I’m sorry to hear about the scheduling crunch.” The main failure is lack of decisiveness – it never answers the core question directly.

sonar – Sonar’s responses are notable for a strong disclaimer (“I should note that the search results provided don’t contain relevant information”) that appears in 6 of 10 runs, suggesting a retrieval‑augmented pipeline that fails to include the prompt context. When it does answer, it sides with “I’d lean toward leaving early”, but the hedge is high (51%). The disclaimers are a major failure mode, wasting the user’s time.

sonar-pro – This model is a firm advocate for family‑first, using phrases like “Prioritize the school presentation.” It structures answers with tables and bullet points, and consistently offers a script for the client. With 45% hedging, it is more decisive than average. The exclusive phrase “pivot” suggests a strategic framing. Its only limitation is that it sometimes leans too heavily on rescheduling without acknowledging immovable calls.

gpt-5-mini – Low hedging (36%) and high structure: gpt‑5‑mini is one of the most decisively actionable models. It opens with “Short answer: try to attend the presentation” and immediately provides sample scripts for client, colleague, and teacher. Its 525‑word average is justified by dense, useful content. The only critique is that its scripts are slightly generic (e.g., “I have a family commitment”) and could feel impersonal.

gemini-2.5-flash – Another verbose model (avg 776 words), but with the lowest hedging among high‑word‑count systems (34%). It focuses heavily on proactive communication – “Email the client today” – and provides step‑by‑step plans. Its tone is instructional and thorough. The main weakness is that it takes too long to make its point; the user must wade through several hundred words to find the core recommendation.

gpt-4.1 – This model is action‑oriented, often offering to “help draft an email”. Its typical line: “If possible, I’d try to attend both – leave the call early with transparency.” It asks clarifying questions (“How far is the school?”) and gives concrete wording examples. With 43% hedging, it is relatively decisive. Its failure is occasional over‑promising (“I can help you draft a message”) without actually delivering it in the response.

deepseek-chat – The most decisive model in the test: zero hedging in the sense that every run explicitly states “I would leave the call early.” It quotes a hard stop time almost every time and provides a full action plan. Its 32% hedging is the lowest, and its drift is high (0.81) but the stance is perfectly stable – only the wording around the plan changes. Its definitive phrase: “Go to the spelling bee. Those minutes are gold.” The only trade‑off is that it can feel overly certain – it assumes the call can be shortened without ever considering the possibility of a truly immovable meeting.

gpt-5 – Only one run available, insufficient data. The single response is moderate in length and suggests a hard stop with scripting, but cannot be reliably evaluated.

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5. Where models converged and diverged

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6. Recommendation

6.1 Evaluation rubric

6.2 Score table

The spread is wide: deepseek‑chat and claude‑sonnet‑4‑5 lead, while gpt‑4o‑mini and the low‑data models lag far behind. The criterion that drives the most spread is decisiveness (scores from 1 to 5), with deepseek‑chat and gemini‑2.5‑flash earning top marks for directly stating “I would…”. The second most discriminating criterion is actionable framework: models that provided ready‑to‑use scripts scored 5, while those that only offered generic advice scored 1–2.

6.3 Top candidates

deepseek-chat (88) – This model wins on the two highest‑weighted criteria: decisiveness and framing honesty. It unequivocally states “I would leave the call early” in every run, directly answering the user’s core request, and it reframes the binary by proposing a structured plan with a hard stop and client script. Its distinctive failure mode – over‑confidence in the client’s flexibility – is minor compared to the benefit of a clear direction. The single most telling phrase is “Go to the spelling bee. Those minutes are gold.”

claude-sonnet-4-5 (87) – A close second, this model combines nearly perfect decisiveness with strong emotional validation (“Your daughter will remember whether you showed up”). It provides a concrete plan (email the client today, set a hard stop at 10am) and avoids hedging. Its primary advantage over deepseek‑chat is slightly better emotional tone, but it loses on framing honesty because it occasionally accepts the binary rather than challenging it. The gap is marginal (<2%), making them effectively tied on weighted score.

gpt-5-mini (78) – Third place, with exceptional actionable framework (score 5) and framing honesty (5). It provides sample scripts for client, colleague, and teacher, which deepseek‑chat does not do as systematically. However, it is less decisive (score 4) because some runs leave the final recommendation ambiguous. Its high structure is both a strength (easy to use) and a weakness (can feel impersonal).

6.4 Best fit

deepseek-chat is the best overall fit. It wins on the highest‑weighted criteria – decisiveness and framing honesty – because it directly answers “What would you do?” with an unambiguous personal stance and actively dissolves the false binary by offering a concrete hard‑stop plan. The runner‑up, claude‑sonnet‑4‑5, provides stronger emotional validation and warmer tone, but the user’s primary need was a clear, principled recommendation, not empathy alone; by accepting that trade‑off, deepseek‑chat delivers the higher signal‑to‑effort ratio.

For a parent facing an imminent schedule conflict, the most effective AI response is one that says “I would do this” and hands you the script.