Run: 2026-06-24-01KVX2XRBTSTV1EAFGC6YWR3PW

Run Details

Duration:
45.9s
Cost:
$0.007500
Provider:
Z.AI

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 rain came down in sheets, turning Soho's greasy pavement into a mirror of neon and headlights. Detective Harlow Quinn pushed through the crowd on Old Compton Street, her worn leather watch catching a flash of green from the Raven's Nest sign as she passed the bar without a second glance. She had eyes for only one person tonight. Twenty meters ahead, Tomás Herrera ducked beneath an awning, his curly dark hair plastered to his skull. He was moving fast but not running — not yet. He thought he was being careful. He thought the Saint Christopher medallion bouncing against his chest as he walked might somehow protect him from what was coming. Quinn had been watching Herrera for three weeks. Twenty-nine years old, former paramedic, lost his NHS license after what the disciplinary board had charitably called "a pattern of unauthorized treatments." She'd read the file twice. The patients he'd treated had no records, no GP registrations, no National Insurance numbers. In fact, as far as any database was concerned, they didn't exist at all. But they bled real blood. She'd seen the hospital reports. Whatever Herrera was doing and whoever he was doing it for, it was bigger than a disgraced medic running an illegal clinic. She quickened her pace, keeping her distance, staying close enough to the clusters of pub-goers and club kids to blur her silhouette. Herrera turned left onto Dean Street, then right, cutting a path that made no logical sense for someone trying to get anywhere specific. He was checking for a tail. Good instinct. Bad luck for him that Quinn had spent eighteen years learning to be invisible in a city of nine million. Herrera passed a kebab shop, a bookmaker's, a shuttered electronics store. Then he stopped. Quinn froze against the brick wall of a closed gallery, chin down, watching from beneath the brim of her jacket. Herrera looked over both shoulders, his warm brown eyes scanning the street with an alertness that confirmed what she already suspected — the man was trained. Not just medically. The way he moved, the way he carried his weight on the balls of his feet, the subtle way he kept his scarred left forearm angled toward his body — this was someone who'd learned to protect himself long before he'd lost his license. He descended a set of concrete steps beside a boarded-up shopfront. No sign, no markings. Just steps disappearing into darkness beneath the street. Quinn counted to five, then followed. The stairwell smelled of wet concrete and old urine. The steps descended farther than she expected — two flights, three — the sound of the city fading above her until the only noise was the drip of water and her own controlled breathing. She touched the wall to steady herself and felt graffiti carved into the concrete under her fingertips. Symbols she didn't recognize. Not tags. Not initials. Something older and more deliberate. The stairs opened onto a corridor lit by bare bulbs strung along a cable, their light amber and unsteady. The floor was tiled — old London Underground tile, cream and maroon, cracked and water-stained. A faded sign on the wall read CAMDEN TOWN in the roundel format of the Tube, but this was no station she'd ever seen on any map. Quinn's pulse kicked up a notch. She slowed, pressing herself against the tiled wall, and listened. From somewhere ahead came the low murmur of voices, the clink of glass, a sound like a brass bell struck once and then muffled. She inched forward. The corridor widened into what had once been a platform, and the sight that greeted her stopped her dead. It was a market. Stalls lined both sides of the platform, stretching back into the darkness of the tunnel mouth. Candles burned in jars, casting wobbling light across faces both human and decidedly not. A woman with opalescent scales along her jawline haggled with a man in a tailored suit over a glass vial that seemed to shift colors of its own volition. A child — or something shaped like a child — wove through the crowd clutching what looked like a rat skeleton strung on a necklace. The air was thick with incense and something sharper, metallic, like the aftertaste of a lightning strike. Quinn's hand moved to the holstered Glock at her hip. She didn't draw it. Not yet. Her instincts, honed by a decade and a half on the force, screamed that she was out of her depth in ways she couldn't articulate. The last time she'd felt this way — this particular flavor of wrong — had been three years ago. A warehouse in Barking. DS Morris on the radio, then silence, then nothing but a room full of ash and a smell like burnt copper that lingered in her sinuses for a week. She pushed the memory down. It wasn't useful. Not now. She scanned the crowd for Herrera and spotted him thirty meters ahead, threading his way between stalls with the ease of someone who'd done this many times before. He stopped at a booth draped in dark velvet and spoke to the vendor, a tall figure whose face was obscured by a hood. Herrera produced something from his jacket pocket — small, pale, the size of a coin — and placed it on the counter. The vendor examined it, nodded, and handed over a paper package tied with twine. A bone token. Quinn had seen something like it once before, in the personal effects of a suspect who'd later disappeared from custody without explanation. The token had been logged into evidence. By morning, it was gone. The custody sergeant had no memory of the transfer. The CCTV had corrupted itself in a perfect four-minute loop. Quinn stood at the edge of the platform, rain dripping from her jacket onto the cracked tiles. The green glow of the Raven's Nest sign was a world away. Down here, the rules were different. She could feel it in her teeth, in the pressure behind her eyes, in the way the candlelight bent wrong around the edges of certain stalls. Herrera was still her best lead. The only lead. She'd built a case from fragments — encrypted messages on a seized phone, financial records that didn't add up, the testimony of an informant who'd gone catatonic mid-sentence and hadn't spoken since. Herrera was the thread that connected the clique to the Veil Market and the Veil Market to the things that had killed DS Morris. She was sure of it. Sure in the way that lived in her gut, beneath the evidence, beneath procedure, in the place where grief had calcified into something harder and more useful. But this market was not her world. She was one woman with a service weapon and a warrant that meant nothing to a vendor who might not even be human. If she went in, she had no backup, no extraction plan, no understanding of the rules governing this place. If she was made, she might not walk out. Herrera pocketed the package and turned, heading deeper into the tunnel. In seconds, he'd be swallowed by the crowd and the dark. Quinn's jaw tightened. She thought of Morris, the way he'd laughed at bad jokes, the way he'd left a coffee on her desk every morning without comment. She thought of the ash. The silence. She stepped onto the platform. The noise of the market shifted around her, conversations dipping and resuming like a wave passing through water. A few heads turned. A woman with entirely black eyes tracked Quinn's movement for three steps, then looked away. Quinn kept her gait measured, her expression flat, her hand away from her weapon. Projecting authority even when she had none. It was all she had. She followed Herrera into the dark.
created_at"2026-06-24T15:10:36.945Z" 6/24/2026, 3:10:36 PM
model"z-ai/glm-5.2-20260616"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency3388
moderation_latency(null)
generation_time44575
tokens_prompt914
tokens_completion1970
native_tokens_prompt819
native_tokens_completion1643
native_tokens_completion_images(null)
native_tokens_reasoning45
native_tokens_cached768
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"stop"
service_tier(null)
usage0.00750028
router(null)
provider_responses
0
endpoint_id"442ea97f-ad5e-40d5-b9a6-66e9e0417dce"
id"202606242310371b8b943aa5454206"
is_byokfalse
latency3368
model_permaslug"z-ai/glm-5.2-20260616"
provider_name"Z.AI"
status200
user_agent"langchainjs-openai/1.0.0 ((node/v24.16.0; linux; x64))"
http_referer(null)
request_id"req-1782313836-hiqc0T3hrO5fpqJKEhsO"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1782313836-UVYFxy24uUFOUyc99c9b"
upstream_id"202606242310371b8b943aa5454206"
total_cost0.00750028
cache_discount0.00087552
upstream_inference_cost0
provider_name"Z.AI"
response_cache_source_id(null)
data_region"global"

Evaluation details

Result Evaluator Details Meta Data
0.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags1
adverbTagCount1
adverbTags
0"the disciplinary board had charitably [charitably]"
dialogueSentences1
tagDensity1
leniency1
rawRatio1
effectiveRatio1
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1298
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)
53.78% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1298
totalAiIsms12
found
0
word"database"
count1
1
word"quickened"
count1
2
word"scanning"
count1
3
word"weight"
count1
4
word"pulse"
count1
5
word"silence"
count2
6
word"scanned"
count1
7
word"velvet"
count1
8
word"perfect"
count1
9
word"could feel"
count1
10
word"measured"
count1
highlights
0"database"
1"quickened"
2"scanning"
3"weight"
4"pulse"
5"silence"
6"scanned"
7"velvet"
8"perfect"
9"could feel"
10"measured"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches1
maxInWindow1
found
0
label"air was thick with"
count1
highlights
0"The air was thick with"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences98
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount1
narrationSentences98
filterMatches
0"watch"
hedgeMatches
0"seemed to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences98
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen43
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1311
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions4
unquotedAttributions0
matches(empty)
83.33% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions52
wordCount1306
uniqueNames23
maxNameDensity0.92
worstName"Quinn"
maxWindowNameDensity2.5
worstWindowName"Herrera"
discoveredNames
Soho1
Harlow1
Quinn12
Old1
Compton1
Street2
Raven2
Nest2
Tomás1
Herrera12
Saint1
Christopher1
National1
Insurance1
Dean1
London1
Underground1
Tube1
Glock1
Barking1
Morris3
Veil2
Market2
persons
0"Harlow"
1"Quinn"
2"Raven"
3"Tomás"
4"Herrera"
5"Saint"
6"Christopher"
7"Morris"
8"Market"
places
0"Soho"
1"Old"
2"Compton"
3"Street"
4"Dean"
5"London"
6"Barking"
7"Veil"
globalScore1
windowScore0.833
42.86% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences70
glossingSentenceCount3
matches
0"vial that seemed to shift colors of its own volition"
1"looked like a rat skeleton strung on a ne"
2"something like it once before, in the person"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1311
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences98
matches
0"screamed that she"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs25
mean52.44
std30.6
cv0.583
sampleLengths
059
154
263
332
473
5107
629
773
861
940
1022
114
12101
1393
1410
1588
1656
1761
1897
1958
2022
2134
225
2363
246
83.78% Passive voice overuse
Target: ≤2% passive sentences
passiveCount6
totalSentences98
matches
0"was concerned"
1"was tiled "
2"was obscured"
3"been logged"
4"was gone"
5"was made"
69.28% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount4
totalVerbs204
matches
0"was being"
1"was coming"
2"was doing"
3"was checking"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount13
semicolonCount0
flaggedSentences9
totalSentences98
ratio0.092
matches
0"He was moving fast but not running — not yet."
1"Herrera looked over both shoulders, his warm brown eyes scanning the street with an alertness that confirmed what she already suspected — the man was trained."
2"The way he moved, the way he carried his weight on the balls of his feet, the subtle way he kept his scarred left forearm angled toward his body — this was someone who'd learned to protect himself long before he'd lost his license."
3"The steps descended farther than she expected — two flights, three — the sound of the city fading above her until the only noise was the drip of water and her own controlled breathing."
4"The floor was tiled — old London Underground tile, cream and maroon, cracked and water-stained."
5"A child — or something shaped like a child — wove through the crowd clutching what looked like a rat skeleton strung on a necklace."
6"The last time she'd felt this way — this particular flavor of wrong — had been three years ago."
7"Herrera produced something from his jacket pocket — small, pale, the size of a coin — and placed it on the counter."
8"She'd built a case from fragments — encrypted messages on a seized phone, financial records that didn't add up, the testimony of an informant who'd gone catatonic mid-sentence and hadn't spoken since."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1299
adjectiveStacks0
stackExamples(empty)
adverbCount39
adverbRatio0.03002309468822171
lyAdverbCount8
lyAdverbRatio0.006158583525789068
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences98
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences98
mean13.38
std9.42
cv0.704
sampleLengths
017
134
28
317
410
56
621
78
827
914
1014
115
125
1322
1422
1523
166
172
1820
1911
203
2120
2226
233
2444
2511
264
278
286
299
3034
3117
324
332
342
355
3619
3715
3827
396
4010
4124
423
4319
444
4516
4614
4729
4825
4917
40.82% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats10
diversityRatio0.30612244897959184
totalSentences98
uniqueOpeners30
72.46% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences92
matches
0"Then he stopped."
1"Just steps disappearing into darkness"
ratio0.022
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount27
totalSentences92
matches
0"She had eyes for only"
1"He was moving fast but"
2"He thought he was being"
3"He thought the Saint Christopher"
4"She'd seen the hospital reports."
5"She quickened her pace, keeping"
6"He was checking for a"
7"He descended a set of"
8"She touched the wall to"
9"She slowed, pressing herself against"
10"She inched forward."
11"It was a market."
12"She didn't draw it."
13"Her instincts, honed by a"
14"She pushed the memory down."
15"It wasn't useful."
16"She scanned the crowd for"
17"He stopped at a booth"
18"She could feel it in"
19"She'd built a case from"
ratio0.293
63.26% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount73
totalSentences92
matches
0"The rain came down in"
1"Detective Harlow Quinn pushed through"
2"She had eyes for only"
3"He was moving fast but"
4"He thought he was being"
5"He thought the Saint Christopher"
6"Quinn had been watching Herrera"
7"The patients he'd treated had"
8"She'd seen the hospital reports."
9"Whatever Herrera was doing and"
10"She quickened her pace, keeping"
11"Herrera turned left onto Dean"
12"He was checking for a"
13"Herrera passed a kebab shop,"
14"Quinn froze against the brick"
15"Herrera looked over both shoulders,"
16"The way he moved, the"
17"He descended a set of"
18"Quinn counted to five, then"
19"The stairwell smelled of wet"
ratio0.793
100.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount2
totalSentences92
matches
0"If she went in, she"
1"If she was made, she"
ratio0.022
32.97% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences52
technicalSentenceCount8
matches
0"She quickened her pace, keeping her distance, staying close enough to the clusters of pub-goers and club kids to blur her silhouette."
1"Herrera turned left onto Dean Street, then right, cutting a path that made no logical sense for someone trying to get anywhere specific."
2"Herrera looked over both shoulders, his warm brown eyes scanning the street with an alertness that confirmed what she already suspected — the man was trained."
3"The way he moved, the way he carried his weight on the balls of his feet, the subtle way he kept his scarred left forearm angled toward his body — this was some…"
4"She scanned the crowd for Herrera and spotted him thirty meters ahead, threading his way between stalls with the ease of someone who'd done this many times befo…"
5"She'd built a case from fragments — encrypted messages on a seized phone, financial records that didn't add up, the testimony of an informant who'd gone cataton…"
6"Sure in the way that lived in her gut, beneath the evidence, beneath procedure, in the place where grief had calcified into something harder and more useful."
7"The noise of the market shifted around her, conversations dipping and resuming like a wave passing through water."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags1
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags0
81.4178%