Run: 2026-09-23-01M36NVF8XSFHK537EVC2RX1K8

Run Details

Duration:
47.8s
Cost:
$0.001553
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 green neon raven above the bar flickered once, then steadied in the rain. Quinn watched the man come out beneath it. He had a narrow face and a dark coat buttoned to the throat. She had seen him twice at the Raven’s Nest in the last week, always at the edge of the room, never drinking, always leaving before the men she was watching. Tonight he’d carried a flat leather case under one arm. When he caught sight of her across the street, his eyes went to her hands, then to the unmarked car at the kerb. Then he ran. Quinn was over the road before the door had swung shut behind him. “Police!” she shouted, though the rain and traffic swallowed the word. He cut between a parked cab and a delivery van, shouldered through a knot of people huddled beneath an awning. Quinn followed, boots striking the slick pavement hard enough to jar her knees. Her coat caught on a sign bracket. She tore free without slowing. The man took the corner at speed and vanished down an alley between a shuttered tailor’s and a restaurant with its kitchen lights still burning. Quinn heard the case bump against his side. She reached the mouth of the alley in time to see his heel skid on a scatter of wet leaves, then recover. “Stop!” He glanced back. For a heartbeat, she saw fear. Not the familiar panic of a man caught with a blade or a pocketful of stolen phones. This was something rawer. He looked past her, too, as though he expected another pursuer to be close behind. Quinn lengthened her stride. The alley spat them out onto a broader street. A bus hissed at the kerb. The man darted around its nose and sprinted north, ignoring the blare of a horn. Quinn followed as the bus pulled away, throwing a sheet of dirty water across the road. It slapped her trousers and soaked through one knee. She gave herself three breaths to settle into the chase. In through the nose, out through the mouth. Arms close. Don’t look at the distance. Look at the man. Eighteen years in the Met had taught her how to run without letting anger choose her pace. Anger burned fast and badly. She kept hers banked, a hard coal beneath her ribs. The suspect cut through a narrow passage, crossed a square, and plunged into a second street where late-night drinkers crowded under pub lights. He shoved through them. Quinn heard a curse, a bottle clatter, the wet slap of her own boots. Someone yelled after him. Nobody moved to help. Her left wrist struck brick as she turned. The worn leather watch twisted against her skin. She tightened her fist around it, straightened the strap with her thumb, and kept going. At the next junction, the man vanished beneath the red glare of traffic lights. Quinn slowed just long enough to read the lanes. A black cab braked hard. A cyclist swore and swerved. She crossed behind them, jaw clenched against the cold, and spotted the man’s coat beyond a row of shuttered shops. He was heading toward Camden. That made no sense. The Raven’s Nest sat in Soho. The men he met there lived, worked, and disappeared within a small, expensive circle of the city. Camden was a long way to run with a case under one arm. Unless he had somewhere to be. Quinn thumbed the radio at her shoulder. “Control, this is Detective Quinn. I’m pursuing a male on foot, northbound from Soho. Dark coat, carrying a leather case. Send a unit to Camden High Street. I’ll update.” Static answered, then a dispatcher’s voice, thin in her earpiece. “Copy. Do you have a location?” “Not yet.” She rounded a corner and saw the man slip down a side road. The rain had thickened, blurring the streetlamps into pale smears. Above the rooftops, clouds tore open. The moon showed through—almost full, bright as a coin dropped into black water. The man looked up at it. He stumbled. Quinn closed the gap by five yards. He ducked through a gap in a metal fence and dropped into a service lane. She followed, ducking under the wet chain-link. The lane smelled of rust and old grease. At the far end, he yanked at a door set into the brickwork. It opened onto darkness. Quinn drew her weapon. “Police! Show me your hands!” The man disappeared inside. She reached the door a second later. It shuddered on its hinges in the wind, a black rectangle in the wall. Beyond it, steps descended steeply. The smell rising from them was stone, standing water, and something sharp and mineral, like blood on a cold knife. Quinn aimed into the stairwell. “Come back out. Now.” No answer. She listened. Rain hissed in the lane behind her. Farther below came a thud, then the faint scrape of metal. Her radio crackled. “Quinn, units are several minutes out. Stand by.” “Negative. He’s gone underground.” “Repeat?” She didn’t answer. The suspect had been gone for too long. The case might contain drugs, documents, a weapon. She had seen enough odd deliveries around the Raven’s Nest to know that “wait for backup” could mean “watch the evidence vanish.” Quinn holstered her weapon and started down. The stairs ended at a platform tiled in cracked white ceramic. A station name had been painted over so many times she couldn’t read it. Old Tube, she guessed, though the air felt too cold and too still for a place beneath Camden. A string of bare bulbs ran along the ceiling. Half were out. The man stood by a service gate at the far end. He had taken a small pale object from his pocket. He pressed it into a round recess in the gate. Even at a distance, Quinn could see that it was bone: curved, polished smooth, no bigger than the top joint of her thumb. The lock clicked. The gate opened on its own. Beyond it, light spilled into the station—warm and gold, crowded with voices. The man slipped through. As the gate started closing, the bone token dropped from his hand and bounced once on the platform. Quinn ran. She caught the token before it rolled under the gate. It felt dry and warm in her palm, though the station was cold. A hole had been drilled through its centre, threaded with black cord. She had no idea what animal it had come from, or why a gate should care. The gate was closing. Quinn could hear the dispatcher in her earpiece, calling her name. She could hear the market beyond the opening, too: the restless hum of a crowd and the clink of glass. Somewhere deeper in that noise, the suspect’s shoes struck stone. She could stop. Wait for backup. Mark the entrance. Do things in the proper order. Three years ago, DS Morris had told her to hold position while he checked the basement. She had followed the call, followed the procedure. By the time she reached him, there had been no basement on the plans, no door where he’d said it was, and no Morris to bring back out. The inquiry had ended with a sealed report and a room full of people explaining what couldn’t have happened. Quinn looked at the bone in her hand. The gate was nearly shut. She slid the token into the recess. The mechanism clicked. She stepped through. The space beyond the old platform opened wider than the station should have allowed. Stalls crowded the tunnel in haphazard rows, their canvas roofs tied to pipes and old signal brackets. Lamps burned in bottles, in cages, in the bellies of glass globes. Their light glanced off jars, polished metal, and hanging charms. A woman in a red scarf held up a bundle of silver keys while a customer inspected them through a lens. Near the tunnel wall, a man in a butcher’s apron weighed blue crystals on a brass scale. The air tasted of hot oil and wet stone. No one stared at Quinn for long. A few people noticed her coat, the set of her shoulders, the badge clipped inside its lapel. They looked away with practised speed. The suspect was already pushing through the crowd, case clamped to his side. “Police!” Quinn shouted. The word cut across the market. Heads turned. A stallholder snatched a tray of stoppered bottles off the counter. Somewhere behind Quinn, the gate clanged shut. The suspect glanced back. The fear was gone now. He had the look of a man who knew the ground beneath his feet. He shoved a customer aside and disappeared between two stalls. Quinn put a hand to her radio. Only static answered. She had no map, no uniformed backup, and no idea what counted as contraband here. All around her, people sold things she could not name to people who had no intention of speaking to police. The market pressed close on every side, bright and crowded and wrong. She looked once toward the gate. Then she went after him.
created_at"2026-09-23T08:24:27.686Z" 9/23/2026, 8:24:27 AM
model"openai/gpt-6-luna-20260922"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency5134
moderation_latency(null)
generation_time47776
tokens_prompt888
tokens_completion2617
native_tokens_prompt810
native_tokens_completion2943
native_tokens_completion_images0
native_tokens_reasoning1043
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"
usage0.0015525
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.20.0; linux; x64))"
http_referer(null)
request_id"req-1790151867-k41EyT02fyeMzowxQG3R"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1790151867-ZEGZUfSzkFF8cVjt48Fw"
upstream_id"resp_0213cb91930e2376016ab38cbbc87887d18409ecab67a70791"
provider_responses
0
endpoint_id"05e94e02-b9c2-4bbb-ba55-4082ee9ad687"
id"resp_0213cb91930e2376016ab38cbbc87887d18409ecab67a70791"
is_byokfalse
latency1032
model_permaslug"openai/gpt-6-luna-20260922"
provider_name"OpenAI"
status200
total_cost0.0015525
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
totalTags3
adverbTagCount0
adverbTags(empty)
dialogueSentences12
tagDensity0.25
leniency0.5
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1524
totalAiIsmAdverbs0
found(empty)
highlights(empty)
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)
90.16% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1524
totalAiIsms3
found
0
word"flickered"
count1
1
word"familiar"
count1
2
word"jaw clenched"
count1
highlights
0"flickered"
1"familiar"
2"jaw clenched"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches1
maxInWindow1
found
0
label"jaw/fists clenched"
count1
highlights
0"jaw clenched"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences148
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences148
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences157
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen30
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1522
ratio0
matches(empty)
89.29% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions7
unquotedAttributions1
matches
0"Old Tube, she guessed, though the air felt too cold and too still for a place beneath Camden."
77.74% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions35
wordCount1453
uniqueNames8
maxNameDensity1.45
worstName"Quinn"
maxWindowNameDensity2
worstWindowName"Quinn"
discoveredNames
Raven3
Nest3
Met1
Camden3
Soho1
Quinn21
Tube1
Morris2
persons
0"Raven"
1"Nest"
2"Quinn"
3"Morris"
places
0"Camden"
1"Soho"
globalScore0.777
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences105
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1522
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences157
matches
0"see that it"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs69
mean22.06
std20.8
cv0.943
sampleLengths
014
18
276
33
413
511
645
755
81
945
104
1155
1229
1332
1449
1531
1653
175
1840
196
2036
2116
222
2342
246
252
267
2747
284
295
304
3146
329
332
3420
3511
364
371
3841
397
4055
4111
4243
433
446
4534
462
4751
484
4941
95.78% Passive voice overuse
Target: ≤2% passive sentences
passiveCount4
totalSentences148
matches
0"been gone"
1"been painted"
2"been drilled"
3"was gone"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount3
totalVerbs250
matches
0"was watching"
1"was heading"
2"was already pushing"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount2
semicolonCount0
flaggedSentences2
totalSentences157
ratio0.013
matches
0"The moon showed through—almost full, bright as a coin dropped into black water."
1"Beyond it, light spilled into the station—warm and gold, crowded with voices."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1457
adjectiveStacks0
stackExamples(empty)
adverbCount41
adverbRatio0.028140013726835965
lyAdverbCount4
lyAdverbRatio0.002745367192862045
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences157
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences157
mean9.69
std6.23
cv0.642
sampleLengths
014
18
213
330
410
523
63
713
811
920
1013
117
125
1325
148
1522
161
173
186
1917
204
2115
224
239
246
2515
2616
279
2810
298
302
315
324
3317
345
3510
3623
374
3814
394
404
418
428
4315
4414
459
465
475
4820
495
54.35% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats8
diversityRatio0.35668789808917195
totalSentences157
uniqueOpeners56
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount5
totalSentences141
matches
0"Then he ran."
1"Somewhere deeper in that noise,"
2"Somewhere behind Quinn, the gate"
3"Only static answered."
4"Then she went after him."
ratio0.035
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount42
totalSentences141
matches
0"He had a narrow face"
1"She had seen him twice"
2"she shouted, though the rain"
3"He cut between a parked"
4"Her coat caught on a"
5"She tore free without slowing."
6"She reached the mouth of"
7"He glanced back."
8"He looked past her, too,"
9"It slapped her trousers and"
10"She gave herself three breaths"
11"She kept hers banked, a"
12"He shoved through them."
13"Her left wrist struck brick"
14"She tightened her fist around"
15"She crossed behind them, jaw"
16"He was heading toward Camden."
17"She rounded a corner and"
18"He ducked through a gap"
19"She followed, ducking under the"
ratio0.298
73.48% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount109
totalSentences141
matches
0"The green neon raven above"
1"Quinn watched the man come"
2"He had a narrow face"
3"She had seen him twice"
4"Tonight he’d carried a flat"
5"Quinn was over the road"
6"she shouted, though the rain"
7"He cut between a parked"
8"Quinn followed, boots striking the"
9"Her coat caught on a"
10"She tore free without slowing."
11"The man took the corner"
12"Quinn heard the case bump"
13"She reached the mouth of"
14"He glanced back."
15"This was something rawer."
16"He looked past her, too,"
17"Quinn lengthened her stride."
18"The alley spat them out"
19"A bus hissed at the"
ratio0.773
70.92% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount2
totalSentences141
matches
0"Even at a distance, Quinn"
1"By the time she reached"
ratio0.014
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences66
technicalSentenceCount1
matches
0"He had the look of a man who knew the ground beneath his feet."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags3
uselessAdditionCount0
matches(empty)
0.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags2
fancyCount2
fancyTags
0"she shouted (shout)"
1"Quinn shouted (shout)"
dialogueSentences12
tagDensity0.167
leniency0.333
rawRatio1
effectiveRatio0.333
91.7236%