Run: 2026-07-25-01KYCSDV0HD9TQ6KZCPRK7JPDJ

Run Details

Duration:
6.1s
Cost:
$0.003033
Provider:
Google AI Studio

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 over Soho did not fall so much as it drove sideways, a relentless London sleet that soaked through heavy wool and slicked the tarmac to a mirror sheen. Detective Harlow Quinn did not pull up her collar. She kept her stride measured, her posture upright with the rigid military precision of eighteen years on the force, her boots slapping hard against the pavement. Up ahead, cutting through the glow of the green neon sign above The Raven's Nest, a familiar flash of dark, curly hair darted down a narrow maintenance alley. Tomás Herrera. Quinn’s breath plumed in the freezing air, sharp and silver. She had tracked the former paramedic for three blocks, watching him slip through the damp shadows like he knew every rat-run in the district. She knew what Herrera was—or at least, she knew what he did. A disgraced NHS medic patching up the city's untouchables, laundering underworld violence through back-alley stitches and unrecorded prescriptions. But tonight wasn't about medical fraud. Tonight, Quinn was chasing a ghost. Or rather, she was chasing the men who had made ghosts out of better cops than her. She rounded the corner into the alley, her hand instinctively dropping to hover above her holster. The brick walls were slick with moss and soot, trapping the scent of stale ale and diesel. A rusted dumpster rattled in the backdraft of the wind. Herrera was gone. Quinn cursed under her breath, stepping carefully over a heap of sodden cardboard. She checked the worn leather watch on her left wrist—02:14. Her sharp jaw tightened as her brown eyes swept the dead end. No doors. No fire escapes low enough to reach. Just a dead wall of grimy brick and— A heavy iron grate, set flush into the concrete floor, was canted open at an odd, jarring angle. Rising from the dark gap beneath was a thin plume of warm, greasy air that smelled faintly of ozone, old copper, and damp earth. She crouched, her knees popping faintly in the chill. Reaching out, she snagged the edge of the iron grate with a gloved hand and pulled it back. Below, a rusted metal ladder plunged straight down into absolute blackness. There was no sound save for the distant, rhythmic thrum of the underground—a low-frequency vibration that rattled her teeth and made the hairs on the back of her neck stand on end. She pulled a heavy-duty tactical torch from her coat pocket, thumbed the switch, and swept the blinding beam down the shaft. The light caught the slick rungs of the ladder, then vanished into a cavernous expanse below. This wasn't a standard storm drain or a maintenance tunnel. The brickwork down there was Victorian, massive and arched, but it had been modified. Sawn-off iron pipes had been welded into strange, geometric arrays. Faint flickers of amber and violet light pulsed against the curved walls far below, painting the shadows in sick, alien hues. Quinn knew the geography of London's subterranean network by heart. She knew the sewer mains, the deep-level postal railways, the abandoned Tube spurs choked with decades of soot. None of them looked like this. None of them hummed with that unnatural, thrumming resonance that seemed to bypass her ears and vibrate directly inside her skull. She hesitated, her boot poised over the first rung of the ladder. Three years ago, she had stood in a basement just like this—dark, quiet, smelling of ozone and iron—and watched her partner, DS Morris, step into a shadow that had no business existing. Morris hadn't come back. The official report called it an accident, a structural collapse, a tragedy of gas and falling concrete. But Quinn had found the burn marks on the stone. She had found the coldness that lingered for weeks, defying the laws of thermodynamics. She was a cop. She believed in evidence, ballistics, forensics, and the unyielding coldness of the municipal code. But the city had a hidden pulse, a dark underbelly that bled through the cracks of the asphalt, and Herrera was running straight toward it. If she followed him down into whatever subterranean market or den he was crawling toward, she would be stepping outside her jurisdiction, beyond backup, and deep into terrain she couldn't police with a badge and a firearm. There were no radios down there. No dispatch. If things went sideways, the Metropolitan Police wouldn't even know where to dig for her bones. A sharp, metallic clink echoed from the depths below—the distinct sound of a boot striking loose gravel. Then came a muffled voice, followed by the low murmur of unseen crowds murmuring in a dialect Quinn's ear refused to quite translate. She gripped the metal rail of the ladder. Her knuckles turned white beneath her black leather gloves. Letting Herrera go meant losing her only thread to the syndicate, the dark alchemy, and the lingering, unanswered questions of Morris's death. Following him meant walking blindly into a trap designed by things she couldn't arrest. Quinn didn't blink. With the practiced, rigid motion of a soldier dropping into a trench, she swung her legs over the rim, found the first rung, and descended into the dark.
created_at"2026-07-25T14:03:52.732Z" 7/25/2026, 2:03:52 PM
model"google/gemini-3.5-flash-lite-20260721"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency296
moderation_latency(null)
generation_time6051
tokens_prompt888
tokens_completion1297
native_tokens_prompt852
native_tokens_completion1111
native_tokens_completion_images0
native_tokens_reasoning0
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"default"
usage0.0030331
router(null)
provider_responses
0
endpoint_id"6bd8f433-79e1-416c-b407-1772eb796c9a"
id"SMJkaojXMILbjMcP8NCe-As"
is_byokfalse
latency296
model_permaslug"google/gemini-3.5-flash-lite-20260721"
provider_name"Google AI Studio"
status200
user_agent"langchainjs-openai/1.0.0 ((node/v24.18.0; linux; x64))"
http_referer(null)
request_id"req-1784988232-MwPXXqUiDU1zqUKUNjr2"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1784988232-MyS9QcMtgwzUhS6bj9iC"
upstream_id"SMJkaojXMILbjMcP8NCe-As"
total_cost0.0030331
cache_discount(null)
upstream_inference_cost0
provider_name"Google AI Studio"
response_cache_source_id(null)
data_region"global"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags0
94.24% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount868
totalAiIsmAdverbs1
found
0
adverb"carefully"
count1
highlights
0"carefully"
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)
42.40% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount868
totalAiIsms10
found
0
word"measured"
count1
1
word"familiar"
count1
2
word"chill"
count1
3
word"rhythmic"
count1
4
word"standard"
count1
5
word"pulsed"
count1
6
word"resonance"
count1
7
word"pulse"
count1
8
word"echoed"
count1
9
word"depths"
count1
highlights
0"measured"
1"familiar"
2"chill"
3"rhythmic"
4"standard"
5"pulsed"
6"resonance"
7"pulse"
8"echoed"
9"depths"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches1
maxInWindow1
found
0
label"knuckles turned white"
count1
highlights
0"knuckles turned white"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences59
matches(empty)
94.43% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount1
narrationSentences59
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)
analyzedSentences59
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen37
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords862
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions1
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions26
wordCount862
uniqueNames12
maxNameDensity0.93
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Quinn"
discoveredNames
Soho1
London2
Harlow1
Quinn8
Raven1
Nest1
Herrera5
Victorian1
Tube1
Morris3
Metropolitan1
Police1
persons
0"Harlow"
1"Quinn"
2"Raven"
3"Herrera"
4"Tube"
5"Morris"
6"Police"
places
0"Soho"
1"London"
globalScore1
windowScore1
53.85% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences52
glossingSentenceCount2
matches
0"looked like this"
1"resonance that seemed to bypass her ears and vibrate directly inside her skull"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount862
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences59
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs20
mean43.1
std23.39
cv0.543
sampleLengths
065
128
22
393
443
53
652
742
870
937
1055
1155
1212
1377
1443
1561
1640
1717
1836
1931
81.47% Passive voice overuse
Target: ≤2% passive sentences
passiveCount4
totalSentences59
matches
0"was gone"
1"was canted"
2"been modified"
3"been welded"
0.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount4
totalVerbs129
matches
0"was chasing"
1"was chasing"
2"was running"
3"was crawling"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount7
semicolonCount0
flaggedSentences6
totalSentences59
ratio0.102
matches
0"She knew what Herrera was—or at least, she knew what he did."
1"She checked the worn leather watch on her left wrist—02:14."
2"Just a dead wall of grimy brick and—"
3"There was no sound save for the distant, rhythmic thrum of the underground—a low-frequency vibration that rattled her teeth and made the hairs on the back of her neck stand on end."
4"Three years ago, she had stood in a basement just like this—dark, quiet, smelling of ozone and iron—and watched her partner, DS Morris, step into a shadow that had no business existing."
5"A sharp, metallic clink echoed from the depths below—the distinct sound of a boot striking loose gravel."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount874
adjectiveStacks0
stackExamples(empty)
adverbCount24
adverbRatio0.02745995423340961
lyAdverbCount9
lyAdverbRatio0.010297482837528604
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences59
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences59
mean14.61
std8.37
cv0.573
sampleLengths
030
19
226
328
42
510
624
712
818
96
106
1117
1216
1317
1410
153
1613
1710
1812
192
207
218
2218
2324
249
2518
2611
2732
2821
2916
3010
3114
3210
3321
3410
3518
366
3721
3812
3932
404
4117
4210
4314
444
4514
4625
4737
486
492
73.45% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats5
diversityRatio0.4915254237288136
totalSentences59
uniqueOpeners29
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences56
matches
0"Just a dead wall of"
1"Then came a muffled voice,"
ratio0.036
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount15
totalSentences56
matches
0"She kept her stride measured,"
1"She had tracked the former"
2"She knew what Herrera was—or"
3"She rounded the corner into"
4"She checked the worn leather"
5"Her sharp jaw tightened as"
6"She crouched, her knees popping"
7"She pulled a heavy-duty tactical"
8"She knew the sewer mains,"
9"She hesitated, her boot poised"
10"She had found the coldness"
11"She was a cop."
12"She believed in evidence, ballistics,"
13"She gripped the metal rail"
14"Her knuckles turned white beneath"
ratio0.268
100.00% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount36
totalSentences56
matches
0"The rain over Soho did"
1"Detective Harlow Quinn did not"
2"She kept her stride measured,"
3"Quinn’s breath plumed in the"
4"She had tracked the former"
5"She knew what Herrera was—or"
6"A disgraced NHS medic patching"
7"Tonight, Quinn was chasing a"
8"She rounded the corner into"
9"The brick walls were slick"
10"A rusted dumpster rattled in"
11"Herrera was gone."
12"Quinn cursed under her breath,"
13"She checked the worn leather"
14"Her sharp jaw tightened as"
15"A heavy iron grate, set"
16"She crouched, her knees popping"
17"She pulled a heavy-duty tactical"
18"The light caught the slick"
19"This wasn't a standard storm"
ratio0.643
100.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount2
totalSentences56
matches
0"If she followed him down"
1"If things went sideways, the"
ratio0.036
74.83% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences42
technicalSentenceCount4
matches
0"A disgraced NHS medic patching up the city's untouchables, laundering underworld violence through back-alley stitches and unrecorded prescriptions."
1"Rising from the dark gap beneath was a thin plume of warm, greasy air that smelled faintly of ozone, old copper, and damp earth."
2"There was no sound save for the distant, rhythmic thrum of the underground—a low-frequency vibration that rattled her teeth and made the hairs on the back of he…"
3"None of them hummed with that unnatural, thrumming resonance that seemed to bypass her ears and vibrate directly inside her skull."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags0
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags0
87.1555%