Run: 2026-10-09-01M4G16BMH8NA5EZR48RVKT7XN

Run Details

Duration:
12.7s
Cost:
$0.001089
Provider:
Claude Platform on AWS

Model input

System
You are an expert fiction writer. Always keep the following rules in mind: - Write in past tense and use General English spelling, grammar, and colloquialisms/slang. - Write in active voice - Always follow the "show, don't tell" principle. - Avoid adverbs and cliches and overused/commonly used phrases. Aim for fresh and original descriptions. - Convey events and story through dialogue. - Mix short, punchy sentences with long, descriptive ones. Drop fill words to add variety. - Skip "he/she said said" dialogue tags and convey people's actions or face expressions through their speech - Avoid mushy dialog and descriptions, have dialogue always continue the action, never stall or add unnecessary fluff. Vary the descriptions to not repeat yourself. - Put dialogue on its own paragraph to separate scene and action. - Reduce indicators of uncertainty like "trying" or "maybe" When writing text: - NEVER conclude the scene on your own, follow the beat instructions very closely. - NEVER end with foreshadowing. - NEVER write further than what I prompt you with. - AVOID imagining possible endings, NEVER deviate from the instructions. - STOP EARLY if the continuation contains what was required in the instructions. You do not need to fill out the full amount of words possible.
User/Human
Take into account the following glossary of characters/locations/items/lore... when writing your response: <codex> <character id="4412" name="Aurora Carter" aliases="Rory, Laila, Aurora, Carter, Malphora" pronouns="she/her" species="Human" storyRole="Protagonist"> <eyeColor>bright blue</eyeColor> <hair>straight shoulder-length black</hair> <height>5'6"</height> <age>25</age> <scar>small crescent-shaped scar on her left wrist from a childhood accident</scar> <description>Aurora, referred to by her friends as Rory, is cool-headed, intelligent, and known for quick out-of-the-box thinking.</description> <backstory>Born in Cardiff to an Irish barrister father (Brendan Carter) and Welsh teacher mother (Jennifer Carter née Ellis), Rory reluctantly pursued Pre-Law at Cardiff University. She fled to London at the behest of her childhood friend Eva after leaving an abusive ex named Evan. By day, Rory works part-time as a delivery person for Yu-Fei Cheung's Golden Empress restaurant. She lives in a flat above Silas' bar.</backstory> </character> <character id="4415" name="Lucien Moreau" aliases="Lucien, Luc, the Frenchman" pronouns="he/him" species="Half-Demon" storyRole="Ally"> <eyeColor>heterochromatic — one amber, one black</eyeColor> <hair>slicked-back platinum blond</hair> <height>5'11"</height> <age>32</age> <cane>ivory-handled cane that conceals a thin blade</cane> <dress>tailored charcoal suits</dress> <description>An impeccably dressed fixer and information broker in London's supernatural underworld. He speaks four languages fluently.</description> <backstory>Born in Marseille to a human mother and a demon father from the realm of Avaros.</backstory> </character> <location id="858" name="Eva's Flat" aliases="Eva's place, the flat on Brick Lane" realm="Earth"> <cat>tabby cat named Ptolemy</cat> <security>three deadbolts on the front door</security> <description>A cramped one-bedroom flat above a curry house on Brick Lane, East London. Every surface is covered with books, scrolls, and research notes.</description> </location> </codex> Continue the story using the following instructions: <instructions> Write a fully fleshed out scene of at least 1000 words in the romance genre. Two people who parted on complicated terms are forced together again when one shows up unannounced at the other's flat. There's history between them — attraction, hurt, and things left unsaid. Write the scene from the moment the door opens. Use the codex entries above as your world reference for characters and setting. Only include the prose of the scene without any markdown or commentary. </instructions>

Model response

AI
The knock came at ten past eleven, three measured raps that didn't belong to anyone who drank at Silas' bar. Rory set down the mug of tea she'd been nursing, wiped her palms on her jeans, and crossed the cramped landing in her socks. Through the spyhole, a slice of platinum hair and a charcoal lapel filled the fish-eye view. She didn't need to see the rest. She knew the cane, too, ivory-handled and tapping once against the doorframe. She opened the door anyway, because pretending otherwise had never worked on him. Lucien Moreau leaned against the frame like he owned the stairwell. Rain beaded on his shoulders and dripped from the tips of his hair, which had been slicked back with considerably less care than usual. One amber eye caught the landing bulb. The other, black as a well, stayed fixed on her face. "You've not been answering your phone," he said. "I've been answering it fine. I just haven't been answering you." "A distinction without a difference, Aurora." "Don't call me that. Not on my landing." Something flickered across his mouth, too quick to be a smile. "Rory, then. Forgive me. I've had a long night and a longer drive, and the stairs in this building are an insult to anyone with a bad hip." "You don't have a bad hip." "I have an excellent hip, which is why I'm standing here instead of sprawled on your carpet." He shifted his weight onto the cane. Only then did she notice the tremor in his left hand, the faint dark stain across his cuff that the charcoal wool was meant to hide. "May I come in?" She should have said no. Every sensible voice in her head, the one her mum had trained with decades of Sunday lectures, said to close the door and call Eva. Instead she stepped back and let the door swing wide. He came in slowly, favouring the right side, and paused just inside to take in the flat. The mismatched armchair. The stack of Pre-Law textbooks she had never quite sold back. The window that looked down onto Silas' bar and the rain-slick street beyond. He looked at everything except the sofa, which was a deliberate choice, because it still held the blanket she'd wrapped herself in three nights ago when she couldn't sleep and had cried over something stupid on the telly. "You're bleeding," she said. "I'm aware." "Sit down before you fall down." "I am rarely so accommodating." "Lucien." He sat. She heard the breath go out of him when he did, a short hiss he tried to swallow. She crossed to the kitchen, filled the kettle without thinking, then stopped with her hand on the tap and stared at the cupboard doors for a moment too long. Her wrist ached where the old crescent scar pulled, the way it always did when she was about to make a decision she'd regret. "Whose blood is it?" she asked. "Mine, mostly." He unbuttoned his waistcoat with one hand. "The rest belongs to a man in Limehouse who thought a cane was a walking stick. He will think twice next time." "Do you need a doctor?" "I need a towel and an argument I am fairly certain I will lose." She turned around. He had shrugged out of his jacket and was working on the shirt beneath it, and the lamplight caught the dark stripe along his ribs where the fabric had torn. Her throat did something complicated. She looked at the kettle instead. "You left," she said. "You asked me to." "I asked you to stop lying to me. That's not the same thing." "No," he agreed quietly. "It is not. But I was not sure you would still hear the difference by the time I finished explaining myself, so I chose the easier path." "Easier for who?" He didn't answer straight away. The kettle began to tick and hum on the counter. When she glanced back, he was watching her with both eyes, the amber one warm and the black one unreadable, and something in his face had gone unguarded. "For you," he said. "I told myself that was why." She pulled a towel from the drawer, the blue one she'd bought in a rush from the chemist, and crossed the room. She didn't hand it to him. She knelt on the rug instead, close enough to smell rain and smoke and the sharp bite of his cologne underneath. "Show me," she said.
created_at"2026-10-09T09:51:30.456Z" 10/9/2026, 9:51:30 AM
model"anthropic/claude-haiku-5.5-20261007"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency3100
moderation_latency(null)
generation_time12697
tokens_prompt1004
tokens_completion1172
native_tokens_prompt1469
native_tokens_completion1884
native_tokens_completion_images(null)
native_tokens_reasoning474
native_tokens_cached0
num_media_prompt(null)
num_input_audio_prompt(null)
num_media_completion0
num_search_results(null)
num_fetches(null)
web_search_engine(null)
origin"https://ncbench.com/"
is_byokfalse
finish_reason"stop"
native_finish_reason"end_turn"
service_tier"default"
usage0.0010889
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1791539490-IlzCluolnN2b1Erzat4L"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1791539490-27LkCfbpsRRkOrLyxoA5"
upstream_id"msg_011CfrTTdNL6ayq4fzr2FvDZ"
provider_responses
0
endpoint_id"b6cdf493-3a61-441f-a134-e7944fddb980"
id"msg_011CfrTTdNL6ayq4fzr2FvDZ"
is_byokfalse
latency840
model_permaslug"anthropic/claude-haiku-5.5-20261007"
provider_name"Claude Platform on AWS"
status200
total_cost0.0010889
cache_discount(null)
upstream_inference_cost0
provider_name"Claude Platform on AWS"
response_cache_source_id(null)
data_region"global"
workspace_id"97e315e5-d303-487d-83c1-83180e8a13d4"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags10
adverbTagCount1
adverbTags
0"he agreed quietly [quietly]"
dialogueSentences27
tagDensity0.37
leniency0.741
rawRatio0.1
effectiveRatio0.074
93.34% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount751
totalAiIsmAdverbs1
found
0
adverb"slowly"
count1
highlights
0"slowly"
100.00% AI-ism character names
Target: 0 AI-default names (17 tracked, −20% each)
codexExemptions(empty)
found(empty)
100.00% AI-ism location names
Target: 0 AI-default location names (33 tracked, −20% each)
codexExemptions(empty)
found(empty)
73.37% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount751
totalAiIsms4
found
0
word"measured"
count1
1
word"flickered"
count1
2
word"weight"
count1
3
word"unreadable"
count1
highlights
0"measured"
1"flickered"
2"weight"
3"unreadable"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches0
maxInWindow0
found(empty)
highlights(empty)
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences43
matches(empty)
43.19% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount2
narrationSentences43
filterMatches
0"notice"
hedgeMatches
0"tried to"
1"began to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences61
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen38
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords751
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions15
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions5
wordCount533
uniqueNames5
maxNameDensity0.19
worstName"Rory"
maxWindowNameDensity0
worstWindowName(null)
discoveredNames
Rory1
Moreau1
Sunday1
Eva1
Pre-Law1
persons
0"Rory"
1"Moreau"
2"Eva"
places(empty)
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences30
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount751
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences61
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs33
mean22.76
std22.72
cv0.998
sampleLengths
060
119
213
353
48
511
66
78
839
96
1054
1140
1282
134
142
156
165
171
1873
196
2031
215
2214
2344
244
254
2613
2731
283
2943
3010
3149
324
88.94% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences43
matches
0"been slicked"
1"was meant"
56.63% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount2
totalVerbs93
matches
0"was working"
1"was watching"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences61
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount175
adjectiveStacks0
stackExamples(empty)
adverbCount7
adverbRatio0.04
lyAdverbCount2
lyAdverbRatio0.011428571428571429
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences61
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences61
mean12.31
std9.06
cv0.736
sampleLengths
020
124
216
37
412
513
611
724
87
911
108
1111
126
138
1411
1528
166
1724
1826
194
205
2125
2210
2317
243
2511
2613
2738
284
292
306
315
321
332
3418
3529
3624
376
389
3922
405
4114
423
4330
445
456
464
474
4813
494
63.39% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats8
diversityRatio0.45901639344262296
totalSentences61
uniqueOpeners28
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences36
matches
0"Only then did she notice"
1"Instead she stepped back and"
ratio0.056
0.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount20
totalSentences36
matches
0"She didn't need to see"
1"She knew the cane, too,"
2"She opened the door anyway,"
3"He shifted his weight onto"
4"She should have said no."
5"He came in slowly, favouring"
6"He looked at everything except"
7"She heard the breath go"
8"She crossed to the kitchen,"
9"Her wrist ached where the"
10"He unbuttoned his waistcoat with"
11"She turned around."
12"He had shrugged out of"
13"Her throat did something complicated."
14"She looked at the kettle"
15"he agreed quietly"
16"He didn't answer straight away."
17"She pulled a towel from"
18"She didn't hand it to"
19"She knelt on the rug"
ratio0.556
29.44% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount31
totalSentences36
matches
0"The knock came at ten"
1"Rory set down the mug"
2"She didn't need to see"
3"She knew the cane, too,"
4"She opened the door anyway,"
5"Lucien Moreau leaned against the"
6"Rain beaded on his shoulders"
7"The other, black as a"
8"Something flickered across his mouth,"
9"He shifted his weight onto"
10"She should have said no."
11"Every sensible voice in her"
12"He came in slowly, favouring"
13"The mismatched armchair."
14"The stack of Pre-Law textbooks"
15"The window that looked down"
16"He looked at everything except"
17"She heard the breath go"
18"She crossed to the kitchen,"
19"Her wrist ached where the"
ratio0.861
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences36
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences22
technicalSentenceCount1
matches
0"He looked at everything except the sofa, which was a deliberate choice, because it still held the blanket she'd wrapped herself in three nights ago when she cou…"
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags10
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags7
fancyCount1
fancyTags
0"he agreed quietly (agree)"
dialogueSentences27
tagDensity0.259
leniency0.519
rawRatio0.143
effectiveRatio0.074
84.9436%