Fancy prompts don't confuse small LLMs. One format line does

Ask an LLM to "improve" your prompt and you often get something like this:

Kindly undertake a meticulous examination of the order correspondence reproduced hereunder and distill therefrom the following salient particulars...

Does a small local model still understand that? And the other way round: if you write in very simple words, short sentences, like for a child, does that help a 1B model?

In my last test I broke prompts with typos and bad punctuation. This time the spelling is perfect. Only the register changes: plain, normal, or ornate. I ran every version on eight local models from 0.6B to 14B, the biggest that fit in my 16 GB of VRAM.

The short answer: the register itself barely matters. What matters is one line, the one that tells the model how to format its answer.

What I tested

50 tasks, each with an answer a script can check. Five kinds, ten tasks each:

• Math: multi-step word problems, answer is one number.
• Extract: an order note, return JSON with five fixed keys.
• Classify: a support ticket, return one of four labels.
• Filter: a list of records, return the ones that match a condition, sorted. Each list has a trap at the edge: an employee aged exactly 30 for "older than 30", a flight at 14:05 for "between 10:00 and 14:00".
• Format: writing tasks with strict rules. "Exactly 3 bullets, no word health." "Write yes 7 times."

Each task exists in three versions:

	plain	normal	ornate
example	Give only the number.	Answer with the number only.	Furnish solely the numerical figure, without accompanying exposition.
reading grade (Flesch-Kincaid)	4.1	8.3	17.9
words per sentence	9.8	12.3	22.6
words with 9+ letters	1%	4%	26%

The facts, numbers, and conditions are the same in all three versions. A Claude model checked every triple for differences in meaning before the run (it missed one, more on that below). Data you would paste, like the order note or the record list, is identical across versions. Only the words around it change.

Models, all through Ollama with thinking off: Qwen3 0.6B, 1.7B, 4B-instruct, 8B, 14B and Gemma3 1B, 4B, 12B. Three trials per prompt with fixed seeds. 3,600 calls in the main run, plus 1,440 for a follow-up on math.

Result 1: outside math, register makes no difference you can trust

Here are the 40 non-math tasks. Percent correct, and the difference from normal with a 95% interval.

model	plain	normal	ornate	plain − normal	ornate − normal
qwen3 0.6B	31	39	39	−8 [−21, +3]	+0 [−9, +9]
gemma3 1B	52	61	53	−9 [−19, +1]	−8 [−19, +4]
qwen3 1.7B	68	68	60	+1 [−8, +9]	−8 [−17, +2]
qwen3 4B-instruct	82	89	79	−8 [−18, +2]	−10 [−22, +1]
gemma3 4B	69	70	68	−1 [−8, +7]	−2 [−12, +8]
qwen3 8B	73	75	74	−2 [−9, +6]	−1 [−9, +7]
gemma3 12B	78	74	78	+4 [−4, +13]	+3 [+0, +9]
qwen3 14B	78	76	77	+3 [−5, +10]	+1 [−8, +9]

From 8B up, the three versions land within 4 points of each other. Ornate English with 23-word sentences did not hurt them at all.

At 4B and below there is a pattern: normal scores highest, and both plain and ornate lose about 8 points. Every one of those intervals touches zero, so I can't call it proven. But it points one way. For a small model, very simple language is not a safe choice either. My plain prompts were slightly longer than the normal ones (more, shorter sentences), and the facts were spread over more sentences.

Result 2: math swings 30 points, and it is not about reading

Math looked like the big story. Pooled over all models: plain 28%, normal 34%, ornate 18%. qwen3 4B-instruct went from 93% on normal to 43% on ornate.

Then I looked at the replies. When a model wrote out its working, it was right 79% of the time. When it gave a bare number, it was right 11% of the time. These models have thinking off, so writing the steps is the thinking.

And the format line decided how often they wrote the steps:

format line	replies with working shown
"Answer with the number only." (normal)	37%
"Give only the number." (plain)	27%
"Furnish solely the numerical figure, without accompanying exposition." (ornate)	8%

The ornate prompts didn't confuse the models. The models obeyed them more strictly, and that stopped them from reasoning.

To separate the two effects, I kept each problem in its register but gave all three versions the same last line:

setup	plain	normal	ornate
each register's own format line	28	34	18
A: all end with "Answer with the number only."	37	35	40
B: all end with "Work it out step by step, then write the final number on the last line."	84	83	79

With the same last line, the register of the problem text moves the score by 5 points at most. Changing the last line moves it by 45.

The strangest part: two plain phrasings with the same meaning. qwen3 1.7B showed its working in 17% of replies to "Give only the number." and in 80% to "Answer with the number only." Same model, same problems, almost the same words.

Result 3: fancy words can change what you asked

One extraction task had this note: "Hi, this is Laura Becker from Becker Design. We'd like 12 desk lamps..."

• Plain ("the name of the customer"): 88% answered Laura Becker.
• Normal ("customer name"): 96%.
• Ornate ("the appellation of the commissioning customer"): 25%. Most models answered Becker Design.

That is not really a wrong answer. A "commissioning customer" can well be the company. The ornate wording made the question vaguer, and the models picked the other reading. The Claude reviewer I used to check the triples for equal meaning did not flag it either. This is exactly the risk with prompts an LLM has rewritten: each fancy word is a small chance that the meaning moved.

Is it just what the model was trained on?

The theory. Instruction-tuning data is mostly ordinary, direct English. So "normal" prompts should look the most familiar to a model, and both very simple and very ornate English should look less familiar. Small models cope worse with unfamiliar input. That would explain why small models did best on normal, and why big models did not care.

The test. Perplexity measures how surprised a model is by a piece of text. Lower means more familiar. I scored the instruction wording of all 150 prompts with the same Qwen3 models (0.6B, 1.7B, 4B-instruct), leaving out the pasted data that is identical in all three versions.

model	plain	normal	ornate
qwen3 0.6B	15.6	16.7	37.2
qwen3 1.7B	15.9	16.3	31.2
qwen3 4B-instruct	17.4	17.4	31.9

Then I checked, task by task, whether the less familiar version was also the one the model got wrong more often. Outside math, the link was weak (r between −0.15 and +0.08) and not distinguishable from zero for any model. Inside math it was clear for the 4B (r = −0.41). But that is the format line again: the ornate "furnish solely the numerical figure" is both the most unfamiliar text and the line that stopped the model from reasoning.

The explanation. The theory is half right.

• Ornate English really does look foreign to these models: about twice the perplexity. But they understand it anyway. Unfamiliar words alone barely cost accuracy.
• Plain English looks exactly as familiar as normal. Yet small models still did a bit worse on it. So familiarity can't be the reason. The likely reason is structure: my plain versions split each task into more, shorter sentences, and a small model has to join the facts back together.

So it is less about which words you use and more about how the task is laid out, and how strictly the model reads the format line.

Final thoughts

1. Don't bother rewriting a prompt into fancier English. It won't help, and it can shift the meaning.
2. Don't dumb it down for small models either. Normal, direct English did as well as anything, and slightly better at 4B and below.
3. Spend your effort on the format line. "Number only" on a model without thinking tells it to guess. If you want a correct number, let it show its work, and read the last line.
4. If an LLM rewrote your prompt, read the rewritten version for words that could mean two things.

Caveats

50 tasks, three trials per prompt, fixed seeds, Ollama default temperatures. The registers were written by one person (with help from an LLM), so another writer's "ornate" could be harder or easier. The ornate prompts are longer than normal ones, so length and register are partly mixed. Only English. Only local models up to 14B with thinking off. Frontier models were not tested this time; my typos test suggests they would shrug all of this off. The perplexity check covers only the three smaller Qwen models, scored with full-precision weights, while the runs used Ollama's 4-bit builds. The "structure" explanation for plain is my best reading, not something this test isolated.

Repo with the prompts, the harness, and every raw reply: github.com/valbarov/prompt-register-test