Run: 2026-09-23-01M37HZBH9DFJQ5N3NKCTFZBQA

Run Details

Duration:
43.7s
Cost:
$0.023730
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 from the Raven’s Nest came out under the green neon sign with one hand pressed to his ribs and no coat. Rain had darkened his shirt nearly black. He looked left, then right, with the quick, economical scan of someone checking for a tail. Quinn stayed beneath the awning of a closed shop across the street. The bar’s light caught the side of his face: olive skin, short dark curls plastered to his forehead, a silver medallion at his throat. Tomás Herrera. Former paramedic. Formerly licensed. A man who kept turning up near people who vanished before anyone could ask them questions. He stepped off the kerb. Quinn pushed away from the wall. Herrera saw her in the reflection of a taxi window. For half a second they held each other’s gaze across the traffic. Then he ran. “Tomás!” He cut between two cars. A horn blared as Quinn followed, her shoes sliding on the painted centre line. Herrera had a head start, but he was favouring his right side. She closed the distance past a row of shuttered restaurants, breathing diesel and rain, until he veered into an alley. Quinn reached the mouth of it in time to see him vault a low gate. She took it less neatly, caught her coat, tore free. On the other side he had stopped beside a motorbike. He looked back at her. “Don’t follow me.” “Then stop.” He swung a leg over the bike and kicked it alive. Quinn caught his sleeve before he could pull away. The fabric ripped. Something small and pale spilled from his fist and struck the pavement between them. Herrera looked down at it, then at Quinn, with a flash of naked alarm. The bike surged forward. Its rear tyre sprayed grit across her shins. Quinn picked up the object. A disk of bone, no wider than a ten-pence piece. A hole had been drilled through one edge, and a crooked line scored its face. She had seen him carrying a similar thing in a photograph taken outside the Nest three nights ago. Until now she’d taken it for jewellery. At the end of the alley the bike’s tail-light turned north. She ran for her car. Forty minutes later, she found the bike abandoned beside a boarded-up station entrance beneath Camden. She had lost Herrera twice in traffic and caught sight of him again only because he’d run a red light in front of a bus. Her last sighting was from half a street away: he’d left the bike, crossed the pavement, and disappeared behind a construction hoarding. Now rain rattled on the roof of her unmarked car. Quinn checked her worn leather watch. Nearly midnight. She radioed her location and asked for another unit. The dispatcher told her the nearest was tied up at a collision and would get to her when it could. Quinn looked at the bike. A medical bag was strapped behind the seat. If Herrera needed it, he had gone in a hurry. She got out, locked the car, and crossed the road. The hoarding concealed a narrow gap between a locksmith’s shop and a brick wall. Beyond it, a flight of wet concrete steps descended to a metal door. No sign. No light except what leaked up through the frame. Quinn stopped at the top. Three years ago, Morris had followed a witness into a disused service tunnel. He’d called to say he had eyes on her. Then his radio had gone dead. By the time Quinn found the tunnel, Morris was gone and the witness was sitting alone on the tracks, unable—or unwilling—to say what had happened to him. Quinn had searched every inch of that tunnel. She still knew where the water had dripped from the ceiling onto the back of her neck. Below her, the metal door opened. A woman in a long red coat emerged, holding a newspaper over her head. She brushed past Quinn without a glance. The door swung shut behind her. Quinn took out her phone and photographed the entrance, then sent the image and her location to dispatch. She pocketed the bone disk. The sensible move was to wait. Herrera’s bike was here. He would have to come out, and backup could cover the other exits if they could find them. A cry rose from below, sharp and brief. Quinn went down the steps. The door had no handle, only a slot at eye level. As she reached it, the slot scraped open. “Token,” said a voice from the other side. Quinn held the bone disk up between finger and thumb. The slot shut. A bolt drew back. Inside stood a broad man wearing a rain poncho over a dinner jacket. His gaze went to the disk, then to her face. “First time?” he asked. Quinn slipped the token into her coat pocket. “Which way did the man in the blue shirt go?” The broad man’s expression closed. Behind him, stairs led down toward noise and warm, unsteady light. “Don’t know,” he said. Quinn showed him her warrant card. “Try.” He looked at it for so long she thought he might take it from her. Then someone below shouted his name. He stepped aside without answering. She could still turn back. The open door was at her shoulder, with rain and an empty street beyond it. She thought of Herrera’s face when the token fell: not fear of arrest. Fear that she would pick it up. Quinn went past the man and started down. The stairwell smelled of damp plaster, hot oil, and something medicinal. A faded Underground poster curled from the wall. At the bottom, a second door stood open onto a platform crowded with stalls. She stopped just inside. The old tracks had been planked over. Lamps burned above tables laden with stoppered bottles, watches laid out on velvet, jars of grey powder. People moved shoulder to shoulder beneath the tiled station name, which had been painted over in strips but still showed CAMDEN at one end. A vendor pressed a folded map into a customer’s hand; as the paper changed hands, its printed lines shifted under Quinn’s eyes. She blinked. The lines were still. Herrera could be anywhere in the crowd. Quinn searched for his blue shirt, his dark curls, the hand against his ribs. She kept close to the wall and tried her radio. Static hissed through the earpiece. A boy carrying a tray of tea glasses dodged around her. One of the glasses held a dark liquid that moved against the tilt of the tray. Quinn stepped aside to let him pass. At the far end of the platform she spotted Herrera’s medallion glinting as he turned. He was talking to a woman behind a stall hung with surgical instruments. The woman handed him a packet wrapped in brown paper. He tucked it beneath his arm and moved toward an arched passage. Quinn followed. “Tomás.” He stopped. The crowd flowed around him. For a moment he looked only tired. “Go back upstairs,” he said. “I’ve heard that one.” “You don’t understand where you are.” “Then explain it to me.” She kept her voice low. “Start with the people from St. Jude’s. Two patients discharged into your care. Neither one seen since.” His eyes flicked toward the arch. “They’re alive.” “Where?” The woman at the instrument stall had stopped sorting her wares. So had two men beside a table of old coins. Quinn felt their attention settle on her warrant card, still in her hand. Herrera saw it too. “Put that away.” “Tell me where they are.” He took a step closer. His left sleeve had ridden up, exposing the long scar along his forearm. Fresh blood showed through the torn fabric at his ribs. “Someone took them from me,” he said. “I came here to find out who.” A bell rang once, somewhere beyond the arch. Heads turned toward the sound. Herrera seized Quinn’s wrist and pulled her behind a tiled pillar as a procession entered the platform: four people carrying a canvas stretcher between them. The figure on it was covered to the throat, one bare hand hanging over the side. Quinn pulled free. “Who’s that?” “I don’t know.” The stretcher passed. The hand swung once with the bearers’ steps. Around its wrist was a strip of white hospital tape. Quinn pushed away from the pillar. Herrera caught her coat. “If you call out down here,” he said, “no one upstairs hears it.” She looked at his hand on her sleeve. He let go. The bearers were moving toward the passage. Quinn thought of the two missing patients. She thought, too, of the empty tunnel where she had found Morris’s radio and nothing else. She had stood there waiting for someone to tell her what to do next. She put her warrant card away and went after the stretcher. Behind her, Herrera swore softly, then followed.
created_at"2026-09-23T16:35:55.057Z" 9/23/2026, 4:35:55 PM
model"openai/gpt-6-sol-20260922"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency9486
moderation_latency(null)
generation_time43649
tokens_prompt888
tokens_completion2165
native_tokens_prompt810
native_tokens_completion2211
native_tokens_completion_images0
native_tokens_reasoning360
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"
usage0.02373
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.20.0; linux; x64))"
http_referer(null)
request_id"req-1790181355-lgdlON7JqUnpcknaghPH"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1790181355-XT0mh3ENP4jDy0KhEFNW"
upstream_id"resp_02d15d19fdc57648016ab3ffeb2b5887d18cdbfdbc0404dd58"
provider_responses
0
endpoint_id"1cfc7d9d-4404-4b8e-9ee4-58ae45c9dcd4"
id"resp_02d15d19fdc57648016ab3ffeb2b5887d18cdbfdbc0404dd58"
is_byokfalse
latency792
model_permaslug"openai/gpt-6-sol-20260922"
provider_name"OpenAI"
status200
total_cost0.02373
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
totalTags7
adverbTagCount1
adverbTags
0"The hand swung once [once]"
dialogueSentences23
tagDensity0.304
leniency0.609
rawRatio0.143
effectiveRatio0.087
96.65% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1493
totalAiIsmAdverbs1
found
0
adverb"softly"
count1
highlights
0"softly"
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)
89.95% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1493
totalAiIsms3
found
0
word"velvet"
count1
1
word"glinting"
count1
2
word"flicked"
count1
highlights
0"velvet"
1"glinting"
2"flicked"
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
narrationSentences147
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount1
narrationSentences147
filterMatches
0"watch"
hedgeMatches
0"happened to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences163
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
totalWords1491
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions12
unquotedAttributions1
matches
0"Behind her, Herrera swore softly, then followed."
49.43% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions50
wordCount1392
uniqueNames7
maxNameDensity2.01
worstName"Quinn"
maxWindowNameDensity3
worstWindowName"Quinn"
discoveredNames
Raven1
Nest2
Herrera14
Quinn28
Camden1
Morris3
Underground1
persons
0"Herrera"
1"Quinn"
2"Morris"
places
0"Raven"
globalScore0.494
windowScore0.667
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences104
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1491
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences163
matches
0"Fear that she"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs69
mean21.61
std18.58
cv0.86
sampleLengths
046
158
211
325
41
551
635
78
82
951
1012
1155
1211
135
1462
1547
1623
1710
1838
195
2055
2125
2233
2351
248
255
2619
278
2810
297
3023
314
3218
3316
344
357
3626
3740
388
3933
404
4170
426
4336
4434
4550
462
471
4814
495
88.55% Passive voice overuse
Target: ≤2% passive sentences
passiveCount7
totalSentences147
matches
0"been drilled"
1"was tied"
2"was strapped"
3"was gone"
4"been planked"
5"been painted"
6"was covered"
87.96% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount4
totalVerbs238
matches
0"was favouring"
1"was sitting"
2"was talking"
3"were moving"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount2
semicolonCount1
flaggedSentences2
totalSentences163
ratio0.012
matches
0"By the time Quinn found the tunnel, Morris was gone and the witness was sitting alone on the tracks, unable—or unwilling—to say what had happened to him."
1"A vendor pressed a folded map into a customer’s hand; as the paper changed hands, its printed lines shifted under Quinn’s eyes."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1309
adjectiveStacks0
stackExamples(empty)
adverbCount35
adverbRatio0.026737967914438502
lyAdverbCount7
lyAdverbRatio0.0053475935828877
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences163
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences163
mean9.15
std5.64
cv0.617
sampleLengths
023
17
216
312
424
52
62
72
816
95
106
1110
1212
133
141
155
1614
1712
1820
1915
2010
2110
225
233
242
2511
269
273
2814
2914
304
318
325
3310
3415
3518
367
3711
385
3915
4025
4122
4210
436
442
459
4620
475
488
4910
53.17% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats5
diversityRatio0.3374233128834356
totalSentences163
uniqueOpeners55
74.63% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount3
totalSentences134
matches
0"Then he ran."
1"Then his radio had gone"
2"Then someone below shouted his"
ratio0.022
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount37
totalSentences134
matches
0"He looked left, then right,"
1"He stepped off the kerb."
2"He cut between two cars."
3"She closed the distance past"
4"She took it less neatly,"
5"He looked back at her."
6"He swung a leg over"
7"Its rear tyre sprayed grit"
8"She had seen him carrying"
9"She ran for her car."
10"She had lost Herrera twice"
11"Her last sighting was from"
12"She radioed her location and"
13"She got out, locked the"
14"He’d called to say he"
15"She still knew where the"
16"She brushed past Quinn without"
17"She pocketed the bone disk."
18"He would have to come"
19"His gaze went to the"
ratio0.276
64.48% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount106
totalSentences134
matches
0"The man from the Raven’s"
1"Rain had darkened his shirt"
2"He looked left, then right,"
3"Quinn stayed beneath the awning"
4"The bar’s light caught the"
5"A man who kept turning"
6"He stepped off the kerb."
7"Quinn pushed away from the"
8"Herrera saw her in the"
9"He cut between two cars."
10"A horn blared as Quinn"
11"Herrera had a head start,"
12"She closed the distance past"
13"Quinn reached the mouth of"
14"She took it less neatly,"
15"He looked back at her."
16"He swung a leg over"
17"Quinn caught his sleeve before"
18"The fabric ripped."
19"Something small and pale spilled"
ratio0.791
100.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount3
totalSentences134
matches
0"Now rain rattled on the"
1"If Herrera needed it, he"
2"By the time Quinn found"
ratio0.022
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences67
technicalSentenceCount0
matches(empty)
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags7
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags5
fancyCount0
fancyTags(empty)
dialogueSentences23
tagDensity0.217
leniency0.435
rawRatio0
effectiveRatio0
93.4938%