Run: 2026-09-23-01M37HPY2DR2GQ2GGDB4KNK5VJ

Run Details

Duration:
1m
Cost:
$0.031870
Provider:
OpenAI

Model input

System
You are an expert novelist with a gift for vivid, immersive prose. You write in tight third-person limited point of view with a strong narrative voice. Your prose is clean, precise, and engaging — you favor strong verbs, concrete details, and natural-sounding dialogue. You will be given a story bible (codex) containing canonical details about characters, locations, items, and lore. Use these details to ground your writing in the established world. Stay consistent with the codex. Write only the prose of the scene. Do not include titles, chapter headings, markdown formatting, or any commentary before or after the scene.
User/Human
<codex> <character id="4416" name="Detective Harlow Quinn" aliases="Quinn, Detective Quinn, Harlow" pronouns="she/her" species="Human" storyRole="Antagonist"> <eyeColor>brown</eyeColor> <hair>closely cropped salt-and-pepper</hair> <height>5'9"</height> <age>41</age> <jawline>sharp jaw</jawline> <watch>worn leather watch on her left wrist</watch> <bearing>military precision</bearing> <description>A Metropolitan Police detective with 18 years of decorated service who suspects the clique is involved in criminal activity.</description> <backstory>She lost her partner, DS Morris, during a case three years ago under unexplained circumstances that had supernatural origins she doesn't yet understand.</backstory> </character> <character id="4417" name="Tomás Herrera" aliases="Tommy, Tomás, Herrera" pronouns="he/him" species="Human" storyRole="Support"> <eyeColor>warm brown</eyeColor> <hair>short curly dark brown</hair> <height>5'10"</height> <age>29</age> <skinTone>olive skin</skinTone> <scar>scar running along his left forearm from a knife attack</scar> <medallion>Saint Christopher medallion around his neck</medallion> <birthplace>Seville, Spain</birthplace> <description>A former paramedic who provides off-the-books medical care for the clique.</description> <backstory>Born in Seville, Spain. He moved to London to work for the NHS but lost his license after administering unauthorized treatments to supernatural patients.</backstory> </character> <location id="854" name="The Raven's Nest" aliases="the Nest, Raven, Silas' bar" realm="Earth"> <signColor>distinctive green neon sign above the entrance</signColor> <secretRoom>hidden back room accessible through a bookshelf, used for clandestine meetings</secretRoom> <description>A dimly lit bar in Soho, London. The walls are covered with old maps and black-and-white photographs.</description> </location> <location id="856" name="The Veil Market" aliases="Veil Market, the Market, underground market" realm="Earth"> <currentLocation>abandoned Tube station beneath Camden</currentLocation> <entryRequirement>bone token</entryRequirement> <schedule>moves locations every full moon</schedule> <description>A hidden supernatural black market that sells enchanted goods, banned alchemical substances, and information.</description> </location> </codex> Write a fully fleshed out scene of at least 1000 words in the thriller genre. A detective is pursuing a suspect through rain-soaked city streets at night. The chase takes them from the street level down into an underground market. The detective must decide whether to follow the suspect into unfamiliar and potentially dangerous territory. 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.

Model response

AI
The man fell outside a shuttered chemist on Camden High Street, and Tomás Herrera caught him before his head struck the pavement. For half a second, Detective Harlow Quinn thought she’d misread Herrera. He knelt in the rain, fingers at the man’s throat, his other hand steadying the man’s head with the easy care of someone who’d done it a thousand times. Then he slipped a small metal case from inside the man’s coat. Quinn stepped out of the doorway across the street. “Herrera.” His head came up. Rain flattened his dark curls. He looked at the warrant card in her hand, then past her, measuring the street. The man on the pavement convulsed. Quinn raised her radio. “Control, officer requesting urgent ambulance. Camden High Street, outside the closed chemist near the junction with Greenland Road. Male, unconscious, convulsing.” Herrera pulled the man onto his side. His left sleeve rode up, exposing a pale scar along his forearm. “Keep him like that,” he said. “Put the case down.” “He needs a hospital.” “I’ve called one.” Herrera looked at the case in his hand. It was hardly bigger than a cigarette packet, dull steel with a dent along one edge. He stood. “Don’t,” Quinn said. He ran. Quinn swore once and went after him. She had followed him from the Raven’s Nest in Soho, through a Tube carriage packed with late drinkers, and into the rain at Camden Town. Twenty minutes of careful distance, wasted in one sprint. Behind her, the man on the pavement made a wet, choking sound. She keyed her radio as she ran and told Control about him again, the words clipped by her breathing. An ambulance was on its way. There was no one else on the street to send after Herrera. He cut across the road in front of a taxi. Its brakes shrieked. Quinn cleared the bonnet close enough to feel heat from the engine and hit the far pavement hard, her shoes skidding on wet stone. “Herrera! Police!” He glanced back. Not at her face. At the distance between them. He had a lead of thirty feet and knew the back streets better. He turned into a narrow lane lined with overflowing bins. Quinn followed, shouldering past a stack of beer crates. Water poured from a broken gutter and struck her neck like a cold hand. Ahead, Herrera slipped through a gap in a chain-link fence. Quinn hit it a step behind him. The wire snagged her coat. She drove her shoulder through, felt fabric tear, and caught the back of his jacket. For a moment she had him. Her fingers closed around a cord sewn inside his collar. Herrera twisted, the cord snapped, and he lurched away. Something small and hard stayed in her palm. A white disc, no wider than a pound coin. Bone, by the look of it. A hole had been drilled through the top, and a narrow doorway carved into one face. She shoved it into her pocket and kept running. The lane opened onto a service road behind a row of shops. Herrera vaulted a low barrier and disappeared between two buildings. By the time Quinn reached it, he was pulling open a metal door set into a wall covered with old gig posters. An Underground sign hung above it, half hidden under grime, though Camden Town station was two streets away. Herrera looked back again. His Saint Christopher medallion flashed against his throat. “Leave it,” he called. “Drop the case.” “I can’t.” He went through the door. It banged against the wall and began to swing shut. Quinn caught it with her foot. Beyond lay a stairwell lit by a single orange bulb. The stairs descended farther than she could see. She stopped. Rain tapped the back of her coat. Her radio hissed at her shoulder. Control was asking for an update, and somewhere behind her a siren rose over the traffic. Herrera’s file sat sharp in her mind: born in Seville, trained as a paramedic, dismissed from the NHS after treatments no one had properly explained in the hearing. She’d found payments to him from people who gave false names at the Raven’s Nest. She had spent six weeks trying to establish what service they bought. Now a man lay convulsing on a pavement, and Herrera had taken something from him and fled. That much Quinn understood. The stairwell she didn’t. No station staff, no public entrance, no light at the bottom. Herrera could be waiting round the first bend with a knife. She could hold this door and wait for officers who might find another way down. She touched the torn edge of her coat. The bone disc pressed against her thigh through the pocket. Three years ago, DS Morris had followed a lead through a locked door and vanished. Quinn had been twelve minutes behind him. Every account of what happened after contradicted the last, and she had learned to distrust the tidy parts most of all. She lifted the radio. “Control, suspect entered disused Underground access off the service road behind Camden High Street. I’m going in. Send units to the entrance.” “Detective Quinn, wait for—” She stepped inside. The door closed behind her with a heavy click. Her radio crackled into silence. Quinn drew her torch and went down. At the first landing she found a fresh smear of blood on the handrail. At the second, the air changed. The damp concrete smell gave way to hot oil, incense and something medicinal underneath. Voices carried up from below, too many for a closed station. The stairs ended at an old ticket hall. Its tiles were cracked and veined with black mould. A man sat behind a wooden table where the barriers should have been. He wore a dark suit and held out his hand without looking up. “Token.” Quinn kept her torch pointed low. Behind him, canvas screens concealed most of the hall. Lamplight shone through them. People moved beyond, their shadows long and crooked. She could hear someone arguing over a price and, farther off, a train passing on a live line. Herrera was nowhere in sight. “I’m police,” she said. The man looked up. His eyes went to her empty hand, then to the stairwell behind her. “Token.” Quinn could turn back. The siren was probably close now. She reached into her pocket and set the bone disc on his table. He slid it beneath a narrow brass stamp. The metal came down with a crack that made her flinch. He returned the disc, now marked with a red crescent. “Keep it where they can see it,” he said. “Who?” But he had already looked past her, toward the stairs. Quinn moved through the screens. An abandoned platform stretched beneath vaulted brick, its tracks hidden under wooden boards. Stalls crowded both sides. Glass jars glowed on shelves. Bundles of dried flowers hung beside sealed steel canisters and rows of small, labelled bottles. A woman in a fur coat counted cash into the hand of a boy whose face was covered by a motorcycle helmet. No one called out to Quinn. Several people noticed the warrant card in her hand and looked away. At the far end of the platform, Herrera pushed through a blue curtain. Quinn started after him. A tray of silver rings blocked the narrow path, and the woman carrying it stopped dead in front of her. “Your token,” the woman said. Quinn showed her the stamped disc. The woman’s gaze dropped to Quinn’s coat, then to her shoes, wet and gritty from the street. “You should have waited upstairs.” Quinn stepped around her. “Probably.” She reached the curtain and heard Herrera speaking on the other side, his voice low and urgent. “Her breathing’s worse. Open it.” Quinn gripped the curtain’s edge. Through a gap, she saw the steel case open on a trestle table. Inside were three glass ampoules packed in grey foam. Beside it lay a girl of perhaps sixteen, wrapped in blankets, her lips blue. Herrera pressed two fingers to her neck. A second man stood over him with a syringe. He wore a butcher’s apron over an expensive suit. He saw Quinn first. Herrera followed his gaze. Relief crossed his face so quickly she nearly missed it. “Quinn,” he said. “Help me hold her still.” She stayed where she was. The girl’s chest lifted once, shallow and slow. On the table beside her sat a small brass bell, its blue enamel chipped along the handle. Quinn knew that bell. She had bagged it herself at the last address Morris had visited. It had disappeared from the evidence store before the inquiry, and no one had been able to tell her how. The man with the syringe saw where she was looking. He picked up the bell. “Detective,” he said, “either come in or go upstairs.” The girl’s chest did not rise again. Quinn pushed through the curtain.
created_at"2026-09-23T16:31:19.126Z" 9/23/2026, 4:31:19 PM
model"openai/gpt-6-sol-20260922"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency17389
moderation_latency(null)
generation_time60334
tokens_prompt888
tokens_completion2653
native_tokens_prompt810
native_tokens_completion3025
native_tokens_completion_images0
native_tokens_reasoning1156
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"completed"
service_tier"default"
usage0.03187
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.20.0; linux; x64))"
http_referer(null)
request_id"req-1790181079-xsliY0lPxzLnMkcbiYkl"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1790181079-UQIRMOJV5rQMKN56Pf8j"
upstream_id"resp_0d41ed5807f2aa95016ab3fed7374c87d19ecc5f62d96b9753"
provider_responses
0
endpoint_id"1cfc7d9d-4404-4b8e-9ee4-58ae45c9dcd4"
id"resp_0d41ed5807f2aa95016ab3fed7374c87d19ecc5f62d96b9753"
is_byokfalse
latency1121
model_permaslug"openai/gpt-6-sol-20260922"
provider_name"OpenAI"
status200
total_cost0.03187
cache_discount(null)
upstream_inference_cost0
provider_name"OpenAI"
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
totalTags8
adverbTagCount0
adverbTags(empty)
dialogueSentences25
tagDensity0.32
leniency0.64
rawRatio0
effectiveRatio0
96.65% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1494
totalAiIsmAdverbs1
found
0
adverb"quickly"
count1
highlights
0"quickly"
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)
93.31% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1494
totalAiIsms2
found
0
word"lurched"
count1
1
word"silence"
count1
highlights
0"lurched"
1"silence"
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
narrationSentences145
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount1
narrationSentences145
filterMatches(empty)
hedgeMatches
0"began to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences162
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen29
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1494
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions11
unquotedAttributions0
matches(empty)
50.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions66
wordCount1383
uniqueNames19
maxNameDensity1.81
worstName"Quinn"
maxWindowNameDensity3.5
worstWindowName"Quinn"
discoveredNames
Camden3
High1
Street1
Tomás1
Herrera17
Detective1
Harlow1
Quinn25
Raven2
Nest2
Soho1
Tube1
Town2
Control2
Underground1
Saint1
Christopher1
Seville1
Morris2
persons
0"Tomás"
1"Herrera"
2"Harlow"
3"Quinn"
4"Saint"
5"Christopher"
6"Morris"
places
0"Camden"
1"High"
2"Street"
3"Raven"
4"Soho"
5"Tube"
6"Town"
7"Seville"
globalScore0.596
windowScore0.5
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences103
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1494
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences162
matches
0"knew that bell"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs71
mean21.04
std19.7
cv0.936
sampleLengths
022
152
29
31
424
56
625
725
84
94
103
1126
123
132
1490
1537
162
1712
1856
1927
2033
2131
229
2362
2412
254
263
272
2815
2924
302
3129
3255
3362
3418
3543
3626
374
3817
397
4045
4143
421
4345
445
454
4618
4723
4829
499
98.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount3
totalSentences145
matches
0"been drilled"
1"were cracked"
2"was covered"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount3
totalVerbs234
matches
0"was pulling"
1"was asking"
2"was looking"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences162
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1384
adjectiveStacks0
stackExamples(empty)
adverbCount29
adverbRatio0.020953757225433526
lyAdverbCount5
lyAdverbRatio0.0036127167630057803
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences162
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences162
mean9.22
std5.77
cv0.626
sampleLengths
022
111
229
312
49
51
64
75
815
96
104
1121
127
1312
146
154
164
173
188
1916
202
213
222
237
2425
259
2612
2719
286
2912
3010
313
3224
332
343
354
365
3713
3810
399
4014
4110
427
435
4415
456
4610
479
488
499
55.56% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats5
diversityRatio0.35185185185185186
totalSentences162
uniqueOpeners57
24.69% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences135
matches
0"Then he slipped a small"
ratio0.007
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount40
totalSentences135
matches
0"He knelt in the rain,"
1"His head came up."
2"He looked at the warrant"
3"His left sleeve rode up,"
4"It was hardly bigger than"
5"She had followed him from"
6"She keyed her radio as"
7"He cut across the road"
8"Its brakes shrieked."
9"He glanced back."
10"He had a lead of"
11"He turned into a narrow"
12"She drove her shoulder through,"
13"Her fingers closed around a"
14"She shoved it into her"
15"His Saint Christopher medallion flashed"
16"He went through the door."
17"It banged against the wall"
18"Her radio hissed at her"
19"She’d found payments to him"
ratio0.296
56.30% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount109
totalSentences135
matches
0"The man fell outside a"
1"He knelt in the rain,"
2"Quinn stepped out of the"
3"His head came up."
4"Rain flattened his dark curls."
5"He looked at the warrant"
6"The man on the pavement"
7"Quinn raised her radio."
8"Herrera pulled the man onto"
9"His left sleeve rode up,"
10"Herrera looked at the case"
11"It was hardly bigger than"
12"Quinn swore once and went"
13"She had followed him from"
14"She keyed her radio as"
15"An ambulance was on its"
16"He cut across the road"
17"Its brakes shrieked."
18"Quinn cleared the bonnet close"
19"He glanced back."
ratio0.807
74.07% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount2
totalSentences135
matches
0"By the time Quinn reached"
1"Now a man lay convulsing"
ratio0.015
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences63
technicalSentenceCount2
matches
0"He knelt in the rain, fingers at the man’s throat, his other hand steadying the man’s head with the easy care of someone who’d done it a thousand times."
1"She’d found payments to him from people who gave false names at the Raven’s Nest."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags8
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags8
fancyCount0
fancyTags(empty)
dialogueSentences25
tagDensity0.32
leniency0.64
rawRatio0
effectiveRatio0
91.6194%