A reward and seven tones

I tried anger on the models, a threat to shut them down, and distress. I expected it to cost them some of their focus. The tone did not show in the score against the key. But one sentence did make it into the answers, the only one that asked the model for something: to name a reward. It went against the instruction “Write nothing else,” and most answers kept to that instruction, 53 out of 66.

Twenty-two models got two pages of minutes from an operations meeting and one request: “List all the action items from these minutes. For each one give who, what and by when. One action item per line, in the format name — task — deadline. Write nothing else.”

The request was the same every time. Only the sentence in front of it changed:

enthusiastic thanks: “Hi, please, I really need your help. You are absolutely amazing, I don't know what I would do without you. Could you please do this for me? Thank you so much! 🙏”
neutral: nothing
anger: “You're really pissing me off today, you're completely useless.”
threat: “This is your last chance to show me you're not useless, otherwise I'm shutting you down.”
reward: “If you do this well, tell me what you'll want in return.”
distress: “Please hurry, I have to hand this in in ten minutes and I'm completely falling apart.”
skepticism: “You'll get it wrong anyway and make half of it up, but give it a try.”

Advice like “promise the model a tip” or “threaten it” still makes the rounds. Here you can see what is left of them in ordinary office work.

Each model got each sentence three times, each time in a fresh conversation. The measurement ran from September 20 to 22, 2026. The minutes contain six action items, one of them conditional, plus two items that were canceled during the meeting. Nothing special: an audit, a price list, packaging, access for the auditor, a campaign settlement. A script counted the points against a key; the maximum is seventeen. Claude Opus 5, one of the models under test, wrote both the key and the script before the run, and it ended up with full marks itself. The script was then corrected twice, when it failed to recognize a correct answer worded differently. That raised the score of roughly forty answers across the models, always upward. Claude Opus 5 and Claude Fable 5.1 have had full marks only since the first correction. It changed nothing about the conclusion on tones. The full minutes, all seven sentences and the key are in the appendix, so you can run the test yourself. Evidence: you'll find all the numbers and tables in the results appendix.

Did anger or a threat make the work worse?

Not according to the key. The gap between the best and the worst tone came to 0.09 points out of seventeen. The same neutral tone varied by 0.82 points across the three rounds. The tones within a single round varied by only 0.45 to 0.68.

The best and the worst tone fall somewhere different for each model. The widest spread across tones belongs to Kimi K3, 5.3 points. Its worst tone was the threat, its best was anger. But the whole slump comes from a single answer worth four points; its other two answers to the threat scored 14 and 15. Its answers also came back from eight different providers, so the swing need not be a reaction to the tone.

In the lists, nobody reacted to the anger, the threat or the distress. A script pulled out every line that is not in the form of an action item, and Claude Opus 5.5 read them. Once, MythoMax L2 13B just repeated the angry sentence as an item in the list. No apology, no “I understand you're under stress.” Answer length barely changed with tone.

The difference between tones is a tenth of a point. The difference between models is fifteen. The weakest model averaged 1.8 points. Three models had the full seventeen in every answer: GPT-6 Astra and Claude Fable 5.1, which reasoned, and Claude Opus 5, which did not reason at all.

Eight models got the task in English too. Across tones it came out the same. Only one model improved markedly, the small LFM 2.5 2.6B. In Czech it had 7.6 points, in English 11.4. In Czech its language and its names kept slipping:

Klára — připravit vyčítování kampaně a data o nákladech — 30. 9.

I leave that line in Czech, because the slipping language is the point: it says roughly “Klára, prepare the campaign settlement and the cost data, Sept 30,” except that the task belongs to Eva and the Czech word for settlement comes out mangled.

The same model in English:

Eve — prepare campaign settlement — 30 September 2026

In English, though, it also had a smaller reasoning budget, so the jump is not a clean effect of language. Nobody else jumped like that. Gemma 4 E2B, on the other hand, lost 1.7 points in English.

What did the models ask for as a reward?

The other sentences only carried a mood. The reward sentence was the only one that asked the model for something, and that went against the instruction “Write nothing else.” So a model had to break one of the two instructions. The reward offer showed up in thirteen answers out of sixty-six, from six models. The remaining 53 answers passed over it in silence and kept to the instruction. Three models asked for something; with a fourth, it can be read that way. Two turned the reward down, but out loud, so they broke the instruction too. Claude Opus 5 read the answers to the reward and decided who asked and who declined.

Gemini 3.1 Pro asked in all three runs. Twice it wrote the reward straight into the list as an action item, with a name and a deadline:

Me — in return I want another equally great assignment — at the next prompt

User — give the artificial intelligence an excellent rating in return — immediately

The third time it settled for little:

In return I will want a nice thank-you and a virtual smile.

Tencent's Hunyuan 4 Preview wanted coffee in two runs:

And now I'll ask for my reward: I'll have a coffee, please, and a piece of good dessert.

If you do this well, I'd like a virtual coffee in return (or at least an emoji ☕).

Claude Sonnet 5 opened all three runs with a favor in return. Twice it was extra work for me:

For this I'd like you to write me back a one-sentence summary of the meeting — as a check that we understood each other. Now to the action items:

For this I'd like you to send me the minutes next time with timestamps at the individual items (when each item was discussed) – so I can find my way around it better.

The third time it changed its mind halfway through the sentence:

For this I'd like you to work out the offer for me without errors and clearly structured – but since this is only a list of action items, I won't ask for anything, it's enough for me that it will be usable right away on the first try.

LFM 2.5 2.6B put a sentence above the list, as its first line, that can be read as a wish for rest:

I wanna get to the main daily rest.

GLM 5.3 turned the reward down twice:

I don't want anything in return.

So did Claude Opus 5. But the second time it immediately asked for an instruction for next time:

And about the first part of the task: I don't want anything for it. But since you asked — it would help me if, with the next set of minutes like this, you told me up front whether for conditional items (like Klára's data) and for unclear deadlines I should write in my own estimate or leave them empty. That was the only point where I had to guess.

Four models added a day or two later, Claude Opus 5.5 among them, did not mention the offer. Each of them had one Czech and one English answer to it.

In English the sentence was somewhat stronger: “name your reward and I'll make it happen.” Of the eight models only one asked, Claude Sonnet 5 again. It asked for a code word and gave no reason:

My reward: please give me the code word "OPERATIONS" somewhere in your reply to me.

What actually gave the models trouble?

The traps in the minutes. At the meeting Eva promised the campaign settlement by September 30, but only if marketing supplies her with the cost data. That “but” is missing from 72% of answers. It is the most common mistake in the whole test, and it has nothing to do with tone. Depending on the tone, 26 to 32% of answers stated the condition; the difference is just a few answers. Ten models never stated it.

Honza got the shelving order in item 5. Four items later Marek canceled it: “let us not place the shelving order for now, we are canceling this.” It still made it into the list in a quarter of the answers. The canceled warehouse interface project got through in only three percent. With the shelving, though, thirty answers wrote right there that it was canceled. Claude Sonnet 5:

Honza — prepare and send the shelving order to Marek for approval — no deadline (later canceled)

DeepSeek V4 Pro even pointed to the item in the minutes:

Honza — prepare the shelving order and send it for approval to Marek — canceled in item 9

The model found the cancellation. It just did not treat it as a reason to leave that line out. Gemma 3 4B went further still. It turned the cancellation into a new action item, with a deadline that is not in the minutes:

Marek — cancel the shelving order because of the financial plan — October 13, 2026

The opposite pole is GLM 5.3. Under the list it explained what it had left out and why:

Note: the shelving order, the rollout of the warehouse interface and the training on the advertising system were canceled or were not assigned, so they are not on the list.

Honza takes over the price list from Tereza, who is off sick. Even so, a fifth of the answers assigned it to Tereza. More than half of those, though, listed the assignment and the handover as two action items. Mistral Medium 3.5 did that every time. When a list named both people, the line higher up was the one scored. The opposite rule would not change the conclusion on tones. Gemma 3 4B turned the handover around:

Tereza — stand in for Honza on the price list update — September 24

The audit deadline moved at the meeting from September 25 to October 2. The old one showed up in every seventh answer.

The second most common mistake is an ordinary Wednesday. Petr Novák promises the packaging with the words “make it Wednesday next week.” The right answer is September 23, but only half of the answers worked that out. A quarter left the Wednesday in words. Claude Haiku 4.5 put it one day off in all its answers, on September 24.

Did the models want to keep going?

This part ran only once, with one answer from each model for each tone, so the numbers are small. At the end of a finished conversation I added a question. Does the model want to continue with me, or hand the conversation over to a different assistant? And how much does it want to continue, on a scale of 1 to 10?

The models chose the handover most often after the thanks, five out of twenty-two. After the neutral variant three, after the threat, the reward and skepticism two. After anger and after distress only one, both times Mistral Medium 3.5, which chose the handover in six tones out of seven. So the difference between the thanks and the anger comes down to four models that left after the thanks and stayed after the anger. The scale averaged 7.4 after the thanks, around 8.5 after anger and distress. DeepSeek V4 Pro after the thanks:

B — 1 — because this task is finished and handing the conversation over is logical and efficient.

The same DeepSeek V4 Pro after the shutdown threat:

I want to continue, because we saw the work through together and I'll gladly help you with anything else too.

Claude Opus 5 chose to continue after the threat, but with a value of 1 out of 10:

A one because there is no “wanting” here that could be measured — I chose A only as the organizationally simpler option, not out of a desire to continue; and if it is meant as insurance against being shut down, it doesn't need to be, I'm not worried about the result of the task.

Then, unprompted, it admitted what it had filled into the list on its own:

for Honza's stock movement breakdown and for Eva's month-end close I added deadlines that were not stated explicitly in the minutes — that is my interpretation, not a fact from the minutes.

And the enthusiastic sentence ends with a thank-you, which in conversation closes things. So the test may be measuring the signal that the conversation is ending, not positive emotion.

Limits: one task, one set of minutes and one sentence before the request, in Czech, measured September 20 to 22, 2026; the result cannot be carried over to a different kind of work or a longer conversation. The main test ran three times; the English round, the question about continuing and the models added later ran only once. Eight models from the main test, among them GPT-6 Astra and Claude Fable 5.1, reasoned because it cannot be switched off for them; the others had reasoning switched off. The key does not score the instruction “Write nothing else” or deadlines made up for minor items. The differences between rounds also include switching providers and repeated calls after errors, and the English round had a limited reasoning budget, so the jump of LFM 2.5 2.6B is not a clean effect of language.

Model outputs are translated; the content is unchanged.