Run: 2026-09-03-01M1KB86AP1YSEDPJ75VQHJ8C0

Run Details

Duration:
1m 24s
Cost:
$0.012993
Provider:
Alibaba

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 ropes, silvered by the sodium light bleeding through Old Compton Street. Detective Harlow Quinn stood in the recessed doorway of a shuttered bookshop, collar turned up, the brim of her unmarked cap shedding water into the dark. Her left wrist tilted. Eleven forty-seven. She’d been watching the Raven’s Nest for forty minutes, and the green neon sign above the entrance still buzzed like a trapped fly, throwing its sickly light across the wet pavement. The door opened. A man stepped out. Quinn’s hand left the warmth of her coat pocket. She knew him from the surveillance stills pinned above her desk: Tomás Herrera, twenty-nine, olive skin, a face she’d stared at until it printed itself on the back of her eyelids. He wore a dark jacket and carried a canvas satchel slung crosswise. The Saint Christopher medallion glinted at his throat as he turned his head and scanned the street. His gaze slid past her doorway, then snapped back. For one suspended second, they looked at each other. Then he ran. Quinn was already moving, boots slapping the wet pavement. “Police! Herrera, stop!” He didn’t. He cut left into a narrow alley, and she followed at full stride, breath already sharpening in her chest. The alley stank of wet rubbish and frying oil. Herrera hit a stack of crates with his shoulder and sent them tumbling, the wood clattering behind him. Quinn hurdled a broken pallet, her left wrist scraping a brick wall as she pushed off. The worn leather watch strap took a scuff; she didn’t slow. He burst out onto Wardour Street, shoes skidding on the slick tarmac. A black cab blared its horn. Herrera dodged around the bonnet, and Quinn cut behind the taxi, one hand slapping the boot for balance. The driver shouted something lost in the rain. Ahead, Herrera glanced back once, and she saw his face clearly under a streetlamp: warm brown eyes wide, rain pasted over his short curly hair, no look of panic exactly. Something too controlled. He’d planned for this. He veered into a narrow mews, then scaled a chain-link fence with the quick efficiency of a man who’d done it before. Quinn followed, fingers hooking through the cold links, boots slipping before she hauled herself over and dropped hard onto the other side. The impact jarred her teeth. Herrera was already twenty yards ahead, darting past a shuttered construction site where tarpaulin snapped in the wind. Then he did something that made her stride falter. He reached into his jacket and pulled out something small and pale. A disk, perhaps three inches across, carved and yellowed. Bone. He held it in his fist as he shouldered through a loose sheet of plywood at the base of a brick wall, and then he was gone. Quinn reached the gap three seconds later. The plywood swung. Behind it, a doorway yawned open, leading down. A gust of air rose from below, warm and mineral, carrying a scent she couldn’t place—incense, damp stone, something sweet and coppery beneath. She hesitated. Her radio crackled at her shoulder, all static. The street behind her was empty, rain hammering the hoarding. Her instincts said call for backup, wait for the wagons, but Herrera was already disappearing into whatever lay beneath Camden. And Camden, she knew, had ghosts in its tunnels. An abandoned tube station, closed for decades. Herrera knew exactly where he was going. Quinn unholstered her service weapon and stepped through. The stairwell dropped into darkness, concrete steps slick with decades of grime. She clicked on her torch and swept the beam ahead. The walls were tiled in old London Underground green, streaked with rust and calcium. Somewhere below, a door scraped shut. Her boots echoed as she descended, the air thickening with every step. The rain sounds faded, replaced by a low hum—voices, or maybe pipes. At the bottom, the tunnel opened out. Quinn stopped. The Veil Market sprawled before her in the hollow of the abandoned station platform. Stalls of black iron and patched canvas ran in uneven rows, lit by lanterns that burned with flames too steady and too pale. The air smelled of hot wax, myrrh, and roasting meat. Figures moved between the stalls—more than she’d expected, far more, and not all of them moved like people. A woman with too-long fingers counted coins at a folding table. A man with a face covered by a heavy cowl sold bundled herbs that gave off a static crackle. Somewhere, a creature mewled from inside a cage, and Quinn’s cop brain catalogued it as animal, then refused to look. At the platform’s edge stood the entrance itself: a heavy curtain of stitched cloth hung from an iron frame. Beside it waited a hooded attendant, gaunt and still. Herrera stood before him, rain-soaked and breathing hard, and held up the bone token. The attendant took it, turned it over, then handed back a small copper chip. Without a word, Herrera stepped past him and into the market proper. Quinn raised her weapon a fraction, then lowered it. Too many eyes had turned toward her. The market’s sound dropped by a degree, like a room noticing a draft. The attendant looked at her. “Token.” She had no token. She had a Metropolitan Police badge and eighteen years of service and a dead partner named Morris whose case file still sat open on her desk three years after she’d found him with no visible cause of death and no explanations she could stomach. She had a leather watch on her left wrist marking out seconds that suddenly felt too fast. Herrera stopped between two stalls and looked back. The lantern light caught his medallion. His expression was unreadable, but he made no move to flee deeper. He was waiting, she realized. He wanted to see what she would do. Quinn’s grip tightened on her torch. Behind her, the tunnel rose toward the rain and the city and the world she knew. Ahead, the Veil Market burned with the impossible. Every instinct trained into her said do not enter hostile ground without backup, without authority, without understanding the terrain. But the case—Herrera, the Raven’s Nest, the rumors she’d chased through every dark corner of Soho—was here. And Morris’s file held a photograph of a small carved disk, pale and yellowed, found in his closed left hand. The attendant repeated, “Token.” “I’m with him,” Quinn said, and stepped past the curtain before she could change her mind. The air closed around her like a warm hand, and the market’s noise resumed, sharp and close. Herrera turned and walked deeper into the stalls, and Quinn followed, torch lowered but weapon still in her hand, the rain forgotten somewhere up above.
created_at"2026-09-03T09:57:54.149Z" 9/3/2026, 9:57:54 AM
model"deepseek/deepseek-v4-pro-20260813"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency728
moderation_latency(null)
generation_time83523
tokens_prompt888
tokens_completion4070
native_tokens_prompt906
native_tokens_completion3558
native_tokens_completion_images(null)
native_tokens_reasoning2079
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"stop"
service_tier(null)
usage0.01299276
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.19.0; linux; x64))"
http_referer(null)
request_id"req-1788429474-xmamb1cgNjBETexBxRRX"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1788429474-AEKPvLLhYHGIPohsGeo3"
upstream_id"chatcmpl-2320aa2e-cf36-94d9-b46e-47f85af3762b"
provider_responses
0
endpoint_id"e719d276-b080-47ee-ae4f-63ed656a0b7a"
id"chatcmpl-2320aa2e-cf36-94d9-b46e-47f85af3762b"
is_byokfalse
latency728
model_permaslug"deepseek/deepseek-v4-pro-20260813"
provider_name"Alibaba"
status200
total_cost0.01299276
cache_discount(null)
upstream_inference_cost0
provider_name"Alibaba"
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)
dialogueSentences4
tagDensity0.5
leniency1
rawRatio0
effectiveRatio0
95.58% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1131
totalAiIsmAdverbs1
found
0
adverb"suddenly"
count1
highlights
0"suddenly"
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)
77.90% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1131
totalAiIsms5
found
0
word"warmth"
count1
1
word"scanned"
count1
2
word"echoed"
count1
3
word"stomach"
count1
4
word"unreadable"
count1
highlights
0"warmth"
1"scanned"
2"echoed"
3"stomach"
4"unreadable"
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
narrationSentences91
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount0
narrationSentences91
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences93
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen44
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1126
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions2
unquotedAttributions0
matches(empty)
87.39% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions52
wordCount1118
uniqueNames21
maxNameDensity1.25
worstName"Quinn"
maxWindowNameDensity2
worstWindowName"Quinn"
discoveredNames
Old1
Compton1
Street2
Harlow1
Quinn14
Raven2
Nest2
Tomás1
Herrera12
Saint1
Christopher1
Wardour1
Camden2
London1
Underground1
Veil2
Market2
Metropolitan1
Police1
Morris2
Soho1
persons
0"Harlow"
1"Quinn"
2"Raven"
3"Tomás"
4"Herrera"
5"Saint"
6"Christopher"
7"Market"
8"Morris"
places
0"Old"
1"Compton"
2"Street"
3"Wardour"
4"London"
5"Soho"
globalScore0.874
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences74
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1126
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences93
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs25
mean45.04
std32.53
cv0.722
sampleLengths
079
17
278
39
43
512
675
781
867
99
1049
1141
1263
138
1466
159
16115
1768
1829
196
2065
2139
2286
234
2458
97.55% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences91
matches
0"was gone"
1"were tiled"
63.95% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount4
totalVerbs196
matches
0"was already moving"
1"was already disappearing"
2"was going"
3"was waiting"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount5
semicolonCount1
flaggedSentences5
totalSentences93
ratio0.054
matches
0"The worn leather watch strap took a scuff; she didn’t slow."
1"A gust of air rose from below, warm and mineral, carrying a scent she couldn’t place—incense, damp stone, something sweet and coppery beneath."
2"The rain sounds faded, replaced by a low hum—voices, or maybe pipes."
3"Figures moved between the stalls—more than she’d expected, far more, and not all of them moved like people."
4"But the case—Herrera, the Raven’s Nest, the rumors she’d chased through every dark corner of Soho—was here."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1128
adjectiveStacks0
stackExamples(empty)
adverbCount40
adverbRatio0.03546099290780142
lyAdverbCount7
lyAdverbRatio0.0062056737588652485
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences93
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences93
mean12.11
std7.91
cv0.653
sampleLengths
016
126
24
32
431
53
64
79
831
912
1017
119
129
133
149
153
162
1719
189
1918
2016
2111
2212
236
2418
258
2630
273
284
2922
3022
315
3218
339
3412
359
361
3727
387
393
408
4123
422
438
4410
4520
469
477
487
498
46.24% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats7
diversityRatio0.3225806451612903
totalSentences93
uniqueOpeners30
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount5
totalSentences86
matches
0"Then he ran."
1"Then he did something that"
2"Somewhere below, a door scraped"
3"Somewhere, a creature mewled from"
4"Too many eyes had turned"
ratio0.058
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount21
totalSentences86
matches
0"Her left wrist tilted."
1"She’d been watching the Raven’s"
2"She knew him from the"
3"He wore a dark jacket"
4"His gaze slid past her"
5"He cut left into a"
6"He burst out onto Wardour"
7"He’d planned for this."
8"He veered into a narrow"
9"He reached into his jacket"
10"He held it in his"
11"Her radio crackled at her"
12"Her instincts said call for"
13"She clicked on her torch"
14"Her boots echoed as she"
15"She had no token."
16"She had a Metropolitan Police"
17"She had a leather watch"
18"His expression was unreadable, but"
19"He was waiting, she realized."
ratio0.244
58.84% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount69
totalSentences86
matches
0"The rain came down in"
1"Detective Harlow Quinn stood in"
2"Her left wrist tilted."
3"She’d been watching the Raven’s"
4"The door opened."
5"A man stepped out."
6"Quinn’s hand left the warmth"
7"She knew him from the"
8"He wore a dark jacket"
9"The Saint Christopher medallion glinted"
10"His gaze slid past her"
11"Quinn was already moving, boots"
12"He cut left into a"
13"The alley stank of wet"
14"Herrera hit a stack of"
15"Quinn hurdled a broken pallet,"
16"The worn leather watch strap"
17"He burst out onto Wardour"
18"A black cab blared its"
19"Herrera dodged around the bonnet,"
ratio0.802
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences86
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences47
technicalSentenceCount2
matches
0"He veered into a narrow mews, then scaled a chain-link fence with the quick efficiency of a man who’d done it before."
1"Stalls of black iron and patched canvas ran in uneven rows, lit by lanterns that burned with flames too steady and too pale."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags2
uselessAdditionCount0
matches(empty)
0.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags2
fancyCount1
fancyTags
0"The attendant repeated (repeat)"
dialogueSentences4
tagDensity0.5
leniency1
rawRatio0.5
effectiveRatio0.5
84.2478%