Run: 2026-09-10-01M25DPAASGC8657HS3N6E3S0T

Run Details

Duration:
17.0s
Cost:
$0.001529
Provider:
DeepSeek

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 had been falling for six hours and showed no interest in stopping. It came down in sheets off the Camden rooftops, turned the gutters into rivers, and plastered Harlow Quinn's salt-and-pepper crop flat against her skull. She'd lost her umbrella somewhere around Mornington Crescent. She hadn't lost him. Tomás Herrera. Twenty-nine. Former paramedic, license revoked. Five-ten, olive skin, curly dark hair, a scar on his left forearm he'd never given a straight answer about. She'd been on him since the pub on Delancey Street, where he'd met a woman in a gray coat and passed her something small enough to vanish into a palm. Quinn had watched from across the bar with a glass of tonic she never drank. Eighteen years on the job taught you patience before it taught you anything else. He was fast. Faster than a man who'd spent his life in ambulances had any right to be. He cut left down a service alley between two shuttered restaurants, vaulted a stack of pallets, and didn't look back. Quinn went over the pallets after him, landed badly on her bad knee, and kept going. The rain filled the alley with noise. It hid the sound of her breathing. Her watch — worn leather, water-darkened now — said 11:47. She'd noted the time when she'd left the car. Notes mattered. Morris had taught her that. Morris had taught her a great many things before the night he stopped teaching anything at all. The alley spilled onto a dead-end street behind the canal, a place of graffiti and dumpsters and one flickering sodium lamp fighting a losing war with the dark. Herrera was already at the far end, slipping between two buildings. Quinn closed the distance, coat heavy with water, shoes silent on the wet asphalt. She had the advantage he didn't know about: he thought he'd lost her two streets back, when she'd ducked behind the bus shelter and let him run. He'd straightened up after that. Slowed. A man who believed he was alone walked differently. He walked like a man with somewhere to be. The buildings gave way to a construction hoarding, plywood and scaffolding, and beyond it the black mouth of what had once been a Tube station. Quinn knew the place. Camden Town's forgotten sibling — closed in the seventies, sealed, bricked, forgotten by everyone except the taggers who'd found it and the city workers who hadn't bothered to chase them off. The entrance was a Victorian arch, soot-stained and blind, ringed with iron fencing that looked rusted through. Herrera didn't slow. He went through a gap in the fence like water through a grate, and then he was gone, swallowed by the dark beneath the arch. Quinn stopped at the fence. Rain dripped off her jaw. She put one hand on the cold iron and stood there, breathing, and made herself think. Everything about this was wrong. Wrong enough that a smarter detective would have called it in, backed off, written the report, and let the machine grind. That was the procedure. That was what eighteen years had taught her. You didn't follow a suspect into an unknown structure at midnight, alone, without backup, without a warrant, without so much as a working radio — hers had drowned an hour ago in the same gutter that had taken her umbrella. But procedure was for cases that made sense. This one didn't. Three months of surveillance, and she still couldn't say what Herrera was guilty of. The people he met, the packages he moved, the patients he treated in flats with the curtains drawn — none of it fit a pattern she could name. It fit a shape. She could feel the shape. It was there in the way witnesses went quiet when she asked the wrong question, in the way the file on her own desk kept coming back thinner than she'd left it. And in the way Morris had died. She thought about him then, the way she always did at thresholds. Three years ago, a warehouse in Bermondsey, a call she hadn't been on because she'd swapped shifts to take her mother to an appointment. They'd told her the scene made no sense. No forced entry. No weapon. No explanation for the wounds. The coroner had used the word *inconclusive* four times in a two-page report, and Quinn had read those two pages so many times the paper had gone soft as cloth. She'd never believed the word. There was a conclusion. It was just somewhere she hadn't been permitted to look. This was where you looked. She drew her baton from her coat and went through the gap in the fence. The arch swallowed the rain noise first, then the light. Inside, the old station went down farther than a Tube station should — steps that spiraled rather than descended straight, tiled walls green with mold, a smell that was wet stone and something else, something faintly sweet and turned. She flicked her torch on, then off again, afraid of the beam. She went by touch and memory of the floor. Two turns down, she found the rail. A handrail, bolted to the wall, worn smooth where thousands of hands had gripped it. The public didn't come down here. The public hadn't come down here in fifty years. So someone did. Three more turns and she heard it. Not footsteps. Voices. A low murmur of them, many, the sound of a crowd in a place a crowd had no business being — and beneath the voices, a kind of music, thin and reedy, like a flute played badly and proudly. And light. A faint glow, orange and shifting, coming up from below, painting the moldy tiles in warm colors. Quinn stopped on the stairs and listened hard. The murmur was not alarmed. Whatever was going on down there, it had not noticed her yet. She descended the last flight and came out into the old platform. It was a market. There was no other word for it, though the word felt too small. The platform stretched away into the dark on both sides, longer than any platform had a right to be, and along its whole length people had strung up stalls under canopies of patched canvas and hanging lamps. There were tables. There were crates and cages and bottles. There were things on those tables she could not identify and things she did not want to, jars of fluid with shapes suspended in them, bundles of dried herbs bound with black thread, coins and cards and teeth — teeth, small pale teeth, laid out in rows like a currency. The crowd moved between the stalls, a hundred people at least, hooded and quiet, and not one of them startled to find a police detective standing on the platform in a soaked wool coat. Because they hadn't seen her. Because none of them had looked. At the near end of the market, where the platform met the dark, stood a curtained stall with a single lamp above it, and there was Herrera, shrugging off his wet jacket, and the woman in the gray coat from the pub was handing him something, and he laughed. He looked different here. He looked at home. A woman at the nearest stall had finished her business and was turning away, and her hand brushed Quinn's arm, and she froze, and turned, and looked at Quinn's face. The woman did not scream. She did the worse thing. She tilted her head, and looked at Quinn the way you look at a bill you hadn't expected, and said, very quietly, "You don't have a token." The medallion around Herrera's neck caught the lamplight across the market. Saint Christopher. Patron of travelers. Quinn reached into her pocket. Her fingers closed on the thing she'd picked up off the alley floor twenty minutes ago, the small pale thing Herrera had dropped and not noticed, which she had stepped on and stopped and bent down and taken without knowing why — a knucklebone, drilled through, strung on a cord. She held it up. The woman considered it for a long moment. Then she moved aside. Quinn walked forward into the market, toward the lamp, toward the man, and did not let herself look at the exit behind her again.
created_at"2026-09-10T10:26:53.92Z" 9/10/2026, 10:26:53 AM
model"deepseek/deepseek-v4.1-flash-20260910"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency467
moderation_latency(null)
generation_time16996
tokens_prompt888
tokens_completion2696
native_tokens_prompt853
native_tokens_completion2366
native_tokens_completion_images(null)
native_tokens_reasoning648
native_tokens_cached128
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)
usage0.001528734
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.20.0; linux; x64))"
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request_id"req-1789036013-lI0GL7QsbGh92SJSasY4"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1789036013-BM35owuIF3rDQpxDzZu1"
upstream_id"ae74d00d-c433-449a-8223-6025509161ee"
provider_responses
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endpoint_id"fd104bc8-535a-4d31-a847-a8e402e86a02"
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is_byokfalse
latency362
model_permaslug"deepseek/deepseek-v4.1-flash-20260910"
provider_name"DeepSeek"
status200
total_cost0.001528734
cache_discount0.000018816
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provider_name"DeepSeek"
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
totalTags1
adverbTagCount0
adverbTags(empty)
dialogueSentences1
tagDensity1
leniency1
rawRatio0
effectiveRatio0
96.38% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1383
totalAiIsmAdverbs1
found
0
adverb"very"
count1
highlights
0"very"
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)
85.54% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1383
totalAiIsms4
found
0
word"structure"
count1
1
word"could feel"
count1
2
word"flicked"
count1
3
word"footsteps"
count1
highlights
0"structure"
1"could feel"
2"flicked"
3"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
narrationSentences108
matches
0"was guilty"
89.95% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount4
hedgeCount0
narrationSentences108
filterMatches
0"watch "
1"think"
2"look"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences108
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen49
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans1
markdownWords1
totalWords1392
ratio0.001
matches
0"inconclusive"
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions5
unquotedAttributions0
matches(empty)
83.33% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions39
wordCount1387
uniqueNames15
maxNameDensity0.94
worstName"Quinn"
maxWindowNameDensity2.5
worstWindowName"Quinn"
discoveredNames
Camden2
Harlow1
Quinn13
Mornington1
Crescent1
Herrera7
Delancey1
Street1
Tube2
Town1
Victorian1
Morris3
Bermondsey1
Christopher1
Three3
persons
0"Harlow"
1"Quinn"
2"Herrera"
3"Morris"
4"Christopher"
places
0"Camden"
1"Mornington"
2"Crescent"
3"Delancey"
4"Street"
5"Town"
6"Victorian"
7"Bermondsey"
globalScore1
windowScore0.833
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences67
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1392
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences108
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs29
mean48
std35.9
cv0.748
sampleLengths
050
185
268
343
495
59
677
728
826
978
1094
117
12103
135
1415
1570
1640
1768
1825
1912
20148
2111
2257
2367
2416
2555
264
2712
2824
98.77% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences108
matches
0"was gone"
1"been permitted"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount3
totalVerbs237
matches
0"was going"
1"was handing"
2"was turning"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount9
semicolonCount0
flaggedSentences8
totalSentences108
ratio0.074
matches
0"Her watch — worn leather, water-darkened now — said 11:47."
1"Camden Town's forgotten sibling — closed in the seventies, sealed, bricked, forgotten by everyone except the taggers who'd found it and the city workers who hadn't bothered to chase them off."
2"You didn't follow a suspect into an unknown structure at midnight, alone, without backup, without a warrant, without so much as a working radio — hers had drowned an hour ago in the same gutter that had taken her umbrella."
3"The people he met, the packages he moved, the patients he treated in flats with the curtains drawn — none of it fit a pattern she could name."
4"Inside, the old station went down farther than a Tube station should — steps that spiraled rather than descended straight, tiled walls green with mold, a smell that was wet stone and something else, something faintly sweet and turned."
5"A low murmur of them, many, the sound of a crowd in a place a crowd had no business being — and beneath the voices, a kind of music, thin and reedy, like a flute played badly and proudly."
6"There were things on those tables she could not identify and things she did not want to, jars of fluid with shapes suspended in them, bundles of dried herbs bound with black thread, coins and cards and teeth — teeth, small pale teeth, laid out in rows like a currency."
7"Her fingers closed on the thing she'd picked up off the alley floor twenty minutes ago, the small pale thing Herrera had dropped and not noticed, which she had stepped on and stopped and bent down and taken without knowing why — a knucklebone, drilled through, strung on a cord."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1386
adjectiveStacks0
stackExamples(empty)
adverbCount40
adverbRatio0.02886002886002886
lyAdverbCount7
lyAdverbRatio0.005050505050505051
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences108
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences108
mean12.89
std11.64
cv0.903
sampleLengths
014
124
28
34
42
51
64
719
830
915
1014
113
1215
1320
1416
157
167
1710
189
192
205
2117
2228
2311
2414
2527
265
271
289
299
3025
314
3231
3317
343
3525
365
375
3816
395
4021
414
428
4340
448
453
4614
4728
484
495
52.16% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats12
diversityRatio0.37962962962962965
totalSentences108
uniqueOpeners41
33.67% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences99
matches
0"Then she moved aside."
ratio0.01
86.67% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount33
totalSentences99
matches
0"It came down in sheets"
1"She'd lost her umbrella somewhere"
2"She hadn't lost him."
3"She'd been on him since"
4"He was fast."
5"He cut left down a"
6"It hid the sound of"
7"Her watch — worn leather,"
8"She'd noted the time when"
9"She had the advantage he"
10"He'd straightened up after that."
11"He walked like a man"
12"He went through a gap"
13"She put one hand on"
14"You didn't follow a suspect"
15"It fit a shape."
16"She could feel the shape."
17"It was there in the"
18"She thought about him then,"
19"They'd told her the scene"
ratio0.333
81.21% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount75
totalSentences99
matches
0"The rain had been falling"
1"It came down in sheets"
2"She'd lost her umbrella somewhere"
3"She hadn't lost him."
4"She'd been on him since"
5"Quinn had watched from across"
6"He was fast."
7"He cut left down a"
8"Quinn went over the pallets"
9"The rain filled the alley"
10"It hid the sound of"
11"Her watch — worn leather,"
12"She'd noted the time when"
13"Morris had taught her that."
14"Morris had taught her a"
15"The alley spilled onto a"
16"Herrera was already at the"
17"Quinn closed the distance, coat"
18"She had the advantage he"
19"He'd straightened up after that."
ratio0.758
100.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount2
totalSentences99
matches
0"Because they hadn't seen her."
1"Because none of them had"
ratio0.02
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences46
technicalSentenceCount2
matches
0"You didn't follow a suspect into an unknown structure at midnight, alone, without backup, without a warrant, without so much as a working radio — hers had drown…"
1"Inside, the old station went down farther than a Tube station should — steps that spiraled rather than descended straight, tiled walls green with mold, a smell …"
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
90.2560%