Run: 2026-09-23-01M36RADCS08T3ZKWVT712M2QY

Run Details

Duration:
56.5s
Cost:
$0.002262
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
Rain blurred the green neon over the Raven’s Nest until the sign looked like a wound in the dark. Quinn watched from beneath the narrow awning opposite, collar turned up, water ticking from its edge onto her shoulder. The bar’s front windows showed only a smear of amber light and the shadows of people who knew better than to sit with their backs to the door. Old maps and black-and-white photographs covered the walls. She had seen them through the glass often enough to know that the maps were not there for decoration. The man came out at 11:17. He was lean, dark-haired, and young enough to think a raised collar made him invisible. One hand stayed inside his coat. The other carried nothing. He paused under the neon, glanced down the street, then turned north. Quinn pushed away from the wall. Her worn leather watch pressed against her wrist as she checked the time. She’d learned to trust a watch more than a radio: the watch did not hiss, crackle, or insist that the impossible was a bad connection. The man walked quickly but not carelessly. He took the quieter side streets, skirted a knot of drunks outside a late-night restaurant, and crossed against the light when traffic slowed. Quinn kept half a block behind. Rain shone on the pavement, running in thin black streams toward the drains. Taxis sprayed dirty water over the kerb. At Tottenham Court Road, the man looked back. Quinn turned toward a shopfront, studying the dark display. In its glass she caught him watching her reflection. His gaze moved over her cropped salt-and-pepper hair, her coat, the set of her shoulders. Military precision, one inspector had once called it, as if she’d assembled herself by regulation. He moved again. Faster. Quinn came off the kerb after him. He broke into a run. She followed, heels striking slick pavement, one hand braced against the radio clipped under her coat. “Control, Detective Quinn. Foot pursuit, male suspect northbound from Soho, heading toward Euston Road. Request a unit to my location.” A burst of static answered. Then dispatch: “Quinn, say your location again.” She gave it. The man vaulted a low barrier at the mouth of an alley. Quinn cleared it a beat later and nearly lost her footing on the wet landing. He had a good turn of speed. He knew the alleys, too, cutting through the narrow backs of buildings as if the city had laid itself out for him alone. “Unit is being dispatched,” Control said. “Can you maintain visual?” “Affirmative.” The man glanced over his shoulder. His face had gone pale with effort. He stumbled at the end of the alley, slapped a hand against the brick, and pushed on. Quinn lengthened her stride. “Police. Stop.” He answered by running harder. They burst onto Euston Road with a bus bearing down on them. The man darted through traffic; Quinn waited for a gap, then crossed between two cabs, their horns blaring in her ears. Rain stung her eyes. Her lungs burned, but her pace held. For one strange second, the man’s outline seemed to double in the glass of a passing bus—one shadow running with him, another running just behind. Quinn blinked. The bus was gone. The street was only a street, slick and bright and full of noise. She had seen that sort of thing once before. Three years ago, Morris had been at her shoulder. There had been a stairwell, a broken light, and a sound Quinn still could not put into any report. Afterward, they found blood on the steps and no sign of her partner. The case file said unresolved. Her memory said she should have kept hold of his sleeve. The runner cut north into Camden. Quinn forced the thought away and kept after him. He dragged a wheeled bin across a narrow lane. She hit it hard with her thigh, shoved it aside, and heard a jar of glass break behind her. Camden High Street opened ahead, wet shop signs and late-night crowds blurring beneath the rain. The suspect shouldered through people without slowing. Quinn called after him, warning the crowd to move. Someone cursed her. A man in a beanie went down on one knee and came up laughing. Then the suspect veered into a gap between two boarded-up shops. Quinn followed him into darkness. The passage ended at a metal service door. He struck it with his palm. Nothing. He dug inside his coat and brought out a small pale disc, pinched between thumb and forefinger. It looked like a button from where she stood. He pressed it to the door. A click sounded from inside. The door swung inward. Quinn reached the threshold just as he disappeared through it. The door began to swing shut. She caught it with her shoulder and forced it back, pain flaring down her arm. Beyond lay a narrow flight of stairs descending into darkness, the walls striped with old white paint and damp. No station sign. No work notices. No camera dome. Just a black stairwell breathing cold air up at her. Her radio hissed. “Detective Quinn? Confirm your status.” She turned her head toward the rain-silvered alley behind her. A siren wailed somewhere south, a long way off. “Suspect entered a service access,” she said. “I’m following.” “Do not enter an unsecured structure alone. Wait for backup.” The order was clear. Reasonable. The sort of instruction she would have given someone else. Quinn looked down the stairs. The man’s footsteps had stopped. She took one step. The old building answered with a faint metallic groan. A smell came up from below: wet stone, hot dust, and something sharp enough to sting the back of her throat. The door began to close against her shoulder. She eased it open again and descended. At the bottom, a second gate blocked the passage. It was old iron, set into brickwork. Beyond it lay what should have been a disused Tube platform, if the maps in the Met’s archive could be trusted. The gate stood open by a hand’s width. Through the gap Quinn saw the suspect pass into a pool of strange yellow light. Then the gate swung shut. She reached it a few seconds later. A small brass plate sat at eye level, dulled by age. Someone had carved a shallow circle into its centre. The shape matched the pale token the man had pressed to the service door. Quinn checked the stairs behind her. The alley waited at the top, soaked and empty. Her radio whispered in her ear. “Quinn. You need to answer.” She could go back. She could wait for a unit, secure the entrance, and try the door with people at her back. If the suspect had slipped away inside, he could be anywhere by then. If he had not, he would have time to destroy whatever evidence he carried. The memory of Morris surfaced again, unwelcome and precise: his gloved hand closing around her sleeve; her own hand slipping free. Quinn rubbed her thumb across the worn leather of her watch. “Control, I’m at the gate. Hold the unit outside. Do not enter without me.” “Quinn—” She cut the transmission before dispatch could finish. A small bone disc lay by the gate’s hinge. Rainwater dripped down the stairwell and ran over it, shining in its shallow grooves. The token was warm when she picked it up. From somewhere beyond the iron bars came a murmur of voices, layered and close. A market crowd, perhaps. Or the sound of a crowd played through hidden speakers. Quinn could make out no words. She fitted the token into the circle. The gate released with a dry, delicate click. Quinn pulled it open and stepped through. The abandoned platform stretched ahead under a ceiling of cracked white tiles. It should have been empty. Instead, stalls crowded the old platform edge, packed shoulder to shoulder beneath canvas awnings. Lamps burned in glass jars. A woman in a red veil weighed silver powder on a jeweller’s scale. A man with yellow teeth displayed stoppered bottles whose contents stirred without being touched. At the far end, an old departure board clacked through destinations that had been closed for decades. No one looked surprised to see her. The suspect was already moving through the crowd, his dark coat slipping between swaths of red and blue fabric. He looked back once and met her eyes. Then he smiled. Quinn’s hand went to her belt, though the crowd and the narrow aisles made her holster feel useless. She had no backup, no clear exit, and no idea what half the people around her were selling. The air tasted of iron and smoke. Somewhere below the platform, a train screamed along a tunnel that did not appear on any map she had seen. She followed him into the market.
created_at"2026-09-23T09:07:34.438Z" 9/23/2026, 9:07:34 AM
model"openai/gpt-6-luna-20260922"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency32908
moderation_latency(null)
generation_time56393
tokens_prompt888
tokens_completion2455
native_tokens_prompt810
native_tokens_completion4361
native_tokens_completion_images0
native_tokens_reasoning2514
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"
usage0.0022615
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1790154454-qATi6oUbdGZGhAL9ewsD"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1790154454-Y5pmVxK9hZYxlG7ZGePd"
upstream_id"resp_0148e5e0089b7e3a016ab396d698b887d19986b8dc444cea74"
provider_responses
0
endpoint_id"05e94e02-b9c2-4bbb-ba55-4082ee9ad687"
id"resp_0148e5e0089b7e3a016ab396d698b887d19986b8dc444cea74"
is_byokfalse
latency813
model_permaslug"openai/gpt-6-luna-20260922"
provider_name"OpenAI"
status200
total_cost0.0022615
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
totalTags2
adverbTagCount0
adverbTags(empty)
dialogueSentences13
tagDensity0.154
leniency0.308
rawRatio0
effectiveRatio0
96.63% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1485
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)
93.27% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1485
totalAiIsms2
found
0
word"structure"
count1
1
word"footsteps"
count1
highlights
0"structure"
1"footsteps"
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
emotionTells1
narrationSentences142
matches
0"looked surprised"
82.49% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount3
hedgeCount3
narrationSentences142
filterMatches
0"watch"
1"feel"
hedgeMatches
0"seemed to"
1"began to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences152
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen28
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1484
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions7
unquotedAttributions0
matches(empty)
75.32% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions39
wordCount1406
uniqueNames14
maxNameDensity1.49
worstName"Quinn"
maxWindowNameDensity2.5
worstWindowName"Quinn"
discoveredNames
Raven1
Nest1
Tottenham1
Court1
Road2
Euston1
Quinn21
Morris2
Camden2
High1
Street1
Tube1
Met1
Rain3
persons
0"Raven"
1"Quinn"
2"Morris"
3"Rain"
places
0"Tottenham"
1"Court"
2"Road"
3"Euston"
4"Camden"
5"High"
6"Street"
7"Met"
globalScore0.753
windowScore0.833
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences107
glossingSentenceCount2
matches
0"looked like a wound in the dark"
1"looked like a button from where she stood"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1484
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount2
totalSentences152
matches
0"know that the"
1"insist that the"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs64
mean23.19
std21.43
cv0.924
sampleLengths
019
174
26
337
46
538
656
78
848
94
107
115
1236
1312
1460
1510
161
1730
186
195
2044
2144
229
2357
246
259
2676
2711
285
2947
305
314
3250
3319
348
3519
369
3710
3815
3910
4034
418
427
4360
445
4541
4621
475
4849
4921
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences142
matches
0"was gone"
1"been closed"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount2
totalVerbs230
matches
0"was already moving"
1"were selling"
86.47% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount1
semicolonCount2
flaggedSentences3
totalSentences152
ratio0.02
matches
0"The man darted through traffic; Quinn waited for a gap, then crossed between two cabs, their horns blaring in her ears."
1"For one strange second, the man’s outline seemed to double in the glass of a passing bus—one shadow running with him, another running just behind."
2"The memory of Morris surfaced again, unwelcome and precise: his gloved hand closing around her sleeve; her own hand slipping free."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1416
adjectiveStacks0
stackExamples(empty)
adverbCount45
adverbRatio0.03177966101694915
lyAdverbCount5
lyAdverbRatio0.0035310734463276836
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences152
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences152
mean9.76
std5.85
cv0.599
sampleLengths
019
119
228
38
419
56
615
76
84
912
106
1113
1225
137
1423
156
1613
177
188
199
209
2115
2215
233
241
257
265
2716
2820
295
307
313
3212
3315
347
3523
366
374
381
396
407
4117
424
432
445
4512
4621
474
487
4925
45.83% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats7
diversityRatio0.3026315789473684
totalSentences152
uniqueOpeners46
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount6
totalSentences135
matches
0"Then the suspect veered into"
1"Just a black stairwell breathing"
2"Then the gate swung shut."
3"Instead, stalls crowded the old"
4"Then he smiled."
5"Somewhere below the platform, a"
ratio0.044
95.56% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount42
totalSentences135
matches
0"She had seen them through"
1"He was lean, dark-haired, and"
2"He paused under the neon,"
3"Her worn leather watch pressed"
4"She’d learned to trust a"
5"He took the quieter side"
6"His gaze moved over her"
7"He moved again."
8"He broke into a run."
9"She followed, heels striking slick"
10"She gave it."
11"He had a good turn"
12"He knew the alleys, too,"
13"His face had gone pale"
14"He stumbled at the end"
15"He answered by running harder."
16"They burst onto Euston Road"
17"Her lungs burned, but her"
18"She had seen that sort"
19"Her memory said she should"
ratio0.311
52.59% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount110
totalSentences135
matches
0"Rain blurred the green neon"
1"Quinn watched from beneath the"
2"The bar’s front windows showed"
3"She had seen them through"
4"The man came out at"
5"He was lean, dark-haired, and"
6"The other carried nothing."
7"He paused under the neon,"
8"Quinn pushed away from the"
9"Her worn leather watch pressed"
10"She’d learned to trust a"
11"The man walked quickly but"
12"He took the quieter side"
13"Quinn kept half a block"
14"Rain shone on the pavement,"
15"Taxis sprayed dirty water over"
16"Quinn turned toward a shopfront,"
17"His gaze moved over her"
18"Military precision, one inspector had"
19"He moved again."
ratio0.815
74.07% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount2
totalSentences135
matches
0"If the suspect had slipped"
1"If he had not, he"
ratio0.015
93.60% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences58
technicalSentenceCount4
matches
0"The bar’s front windows showed only a smear of amber light and the shadows of people who knew better than to sit with their backs to the door."
1"Military precision, one inspector had once called it, as if she’d assembled herself by regulation."
2"He knew the alleys, too, cutting through the narrow backs of buildings as if the city had laid itself out for him alone."
3"At the far end, an old departure board clacked through destinations that had been closed for decades."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags2
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags2
fancyCount0
fancyTags(empty)
dialogueSentences13
tagDensity0.154
leniency0.308
rawRatio0
effectiveRatio0
93.1944%