Run: 2026-07-25-01KYCSD4SN6HMP54BDYGEA0HNX

Run Details

Duration:
6.3s
Cost:
$0.003006
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 in Soho was the kind that had no intention of stopping, a needle-sharp drizzle that soaked through wool coats and made the asphalt gleam like black glass under the neon. Detective Harlow Quinn adjusted the collar of her trench coat, her sharp jaw set, her eyes fixed on the flash of a dark jacket darting around the corner of Greek Street. Eighteen years on the force left little room for patience, and none for suspects who ran. Quinn lengthened her stride, her military precision cutting through the Friday night crowd of tourists and theatergoers. On her left wrist, the worn leather watch ticked steadily through the downpour, a constant, grounding rhythm. She had been trailing the runner for four blocks, ever since he slipped out the back exit of The Raven's Nest. She knew that dimly lit Soho bar well enough—the walls plastered with old maps and black-and-white photographs, the green neon sign buzzing above the entrance like a dying insect. She had questioned the owner twice, gotten nowhere, and now she was chasing a ghost down into the bowels of the city. The runner didn't look back. He was agile, weaving through a knot of pedestrians outside a curry house with the desperate speed of a man who knew what waited for him if he was caught. Quinn didn't shout. Shouting wasted breath, and breath was currency when the lungs started burning. She tracked him past the shuttered storefronts, her boots slapping rhythmically against the wet pavement. Her thoughts flickered back, uninvited, to DS Morris. Three years gone, swallowed up by a case that the paperwork said was a tragic accident, but which Quinn’s gut—and the strange, cold weight she still felt in her chest—insisted was something else entirely. Something the coroner's report couldn't explain. She wasn't about to lose another lead to the shadows. The runner cut hard to the left, diving down an iron-railed stairwell that plunged into the earth. Quinn didn't hesitate. She hit the top step running, her hand instinctively dropping to rest against her hip, fingers brushing the cold steel of her service weapon. The metallic clang of her boots on the grating echoed sharply in the confined space. The air changed instantly as she descended. The clean, wet bite of the London rain vanished, replaced by a thick, subterranean dampness thick with the smell of ozone, old iron, and something faintly metallic, like pennies left in a wet palm. She was below the surface now, deep beneath Camden, navigating the labyrinth of disused tunnels that the city maps pretended didn't exist. This was the Veil Market. Quinn had heard the rumors in the precinct whispers, the wild tales told by weary cops working the midnight shift about an underground bazaar that shifted its location every full moon. Most wrote it off as urban myth. Quinn believed in evidence, and the evidence was currently twenty yards ahead of her, rounding a concrete pillar painted with peeling white safety lines. She slowed her descent, easing her weight onto the balls of her feet as she reached the bottom of the stairs. The tunnel opened up into a cavernous, subterranean platform where the ghost trains of a forgotten Tube station used to idle. Now, it was alive. Torches flared in iron brackets along the curved brick walls, casting long, writhing shadows that stretched across hundreds of figures moving through the gloom. It was a market, but not of fruit and cloth. Stalls made of salvaged crates and weathered timber lined the tracks. Quinn caught brief, jarring glimpses of wares that defied the logic of the city above: glass jars swirling with luminescent purple smoke, racks of wicked-looking bone daggers, pelts of animals that had no place in the British Isles, and books bound in dark, unfamiliar leather. The crowd was a dense, shifting tide of humanity and things that only looked human if you didn't look too closely at the way their joints bent or the way the torchlight caught their eyes. Her runner was already dissolving into the throng. Quinn stepped out from the shadows of the stairwell, her hand leaving her holster but remaining close, hovering near her waist. Every instinct honed over nearly two decades of police work screamed at her to turn back. This was outside her jurisdiction, outside her understanding, and dangerously exposed. If she went deeper into the market, she would be completely cut off from backup, radios, and sirens. Yet, as she scanned the crowd, she spotted a flash of the dark jacket near a rusted turnstile that marked the perimeter of the platform. He was heading for a heavily guarded archway cut into the back retaining wall—a narrow bottleneck flanked by two imposing figures whose bulky coats couldn't entirely hide the unnatural breadth of their shoulders. To follow him meant stepping off the safe, predictable grid of London and plunging headfirst into a subterranean syndicate whose rules she didn't know. To turn back meant admitting defeat, walking away from the truth of what happened to Morris, and letting another shadow slip through her fingers. Quinn exhaled slowly, watching her breath mist in the chilly damp air of the station. The worn leather of her watch strap tightened against her wrist as she clenched her fist. She stepped off the platform and into the market.
created_at"2026-07-25T14:03:30.199Z" 7/25/2026, 2:03:30 PM
model"google/gemini-3.5-flash-lite-20260721"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency458
moderation_latency(null)
generation_time5948
tokens_prompt888
tokens_completion1341
native_tokens_prompt852
native_tokens_completion1100
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"
usage0.0030056
router(null)
provider_responses
0
endpoint_id"6bd8f433-79e1-416c-b407-1772eb796c9a"
id"MsJkapHrGuWOjrEP5Lzk0Qk"
is_byokfalse
latency458
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-1784988210-7Di1MLUHWZLaWRuDxwOU"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1784988210-XpV45AHkceumVLXiTk0e"
upstream_id"MsJkapHrGuWOjrEP5Lzk0Qk"
total_cost0.0030056
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
83.18% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount892
totalAiIsmAdverbs3
found
0
adverb"sharply"
count1
1
adverb"completely"
count1
2
adverb"slowly"
count1
highlights
0"sharply"
1"completely"
2"slowly"
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)
49.55% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount892
totalAiIsms9
found
0
word"flickered"
count1
1
word"weight"
count2
2
word"echoed"
count1
3
word"navigating"
count1
4
word"gloom"
count1
5
word"scanned"
count1
6
word"imposing"
count1
7
word"predictable"
count1
highlights
0"flickered"
1"weight"
2"echoed"
3"navigating"
4"gloom"
5"scanned"
6"imposing"
7"predictable"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches1
maxInWindow1
found
0
label"clenched jaw/fists"
count1
highlights
0"clenched her fist"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences48
matches(empty)
53.57% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount1
narrationSentences48
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)
analyzedSentences48
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen46
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords888
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions3
unquotedAttributions0
matches(empty)
93.69% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions28
wordCount888
uniqueNames16
maxNameDensity1.13
worstName"Quinn"
maxWindowNameDensity2
worstWindowName"Quinn"
discoveredNames
Soho2
Harlow1
Quinn10
Greek1
Street1
Friday1
Raven1
Nest1
Morris2
London2
Camden1
Veil1
Market1
Tube1
British1
Isles1
persons
0"Harlow"
1"Quinn"
2"Raven"
3"Morris"
4"Camden"
places
0"Soho"
1"Greek"
2"Street"
3"Friday"
4"London"
5"Market"
6"British"
globalScore0.937
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences43
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount888
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences48
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs18
mean49.33
std34.4
cv0.697
sampleLengths
063
1122
250
373
417
542
663
75
862
942
104
11125
128
1366
1458
1548
1631
179
97.95% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences48
matches
0"was caught"
59.15% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount3
totalVerbs142
matches
0"was chasing"
1"was already dissolving"
2"was heading"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount4
semicolonCount0
flaggedSentences3
totalSentences48
ratio0.063
matches
0"She knew that dimly lit Soho bar well enough—the walls plastered with old maps and black-and-white photographs, the green neon sign buzzing above the entrance like a dying insect."
1"Three years gone, swallowed up by a case that the paperwork said was a tragic accident, but which Quinn’s gut—and the strange, cold weight she still felt in her chest—insisted was something else entirely."
2"He was heading for a heavily guarded archway cut into the back retaining wall—a narrow bottleneck flanked by two imposing figures whose bulky coats couldn't entirely hide the unnatural breadth of their shoulders."
99.94% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount897
adjectiveStacks0
stackExamples(empty)
adverbCount30
adverbRatio0.033444816053511704
lyAdverbCount18
lyAdverbRatio0.020066889632107024
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences48
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences48
mean18.5
std9.99
cv0.54
sampleLengths
032
131
216
317
417
521
629
722
85
930
103
1112
1215
138
1434
156
1610
1717
183
1924
2015
217
2234
2322
245
2531
267
2724
2821
2921
304
3124
3210
3311
3445
3535
368
3721
3816
3911
4018
4125
4233
4324
4424
4515
4616
479
62.50% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats5
diversityRatio0.4375
totalSentences48
uniqueOpeners21
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences48
matches(empty)
ratio0
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount14
totalSentences48
matches
0"She had been trailing the"
1"She knew that dimly lit"
2"She had questioned the owner"
3"He was agile, weaving through"
4"She tracked him past the"
5"Her thoughts flickered back, uninvited,"
6"She wasn't about to lose"
7"She hit the top step"
8"She was below the surface"
9"She slowed her descent, easing"
10"It was a market, but"
11"Her runner was already dissolving"
12"He was heading for a"
13"She stepped off the platform"
ratio0.292
53.75% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount39
totalSentences48
matches
0"The rain in Soho was"
1"Detective Harlow Quinn adjusted the"
2"Quinn lengthened her stride, her"
3"She had been trailing the"
4"She knew that dimly lit"
5"She had questioned the owner"
6"The runner didn't look back."
7"He was agile, weaving through"
8"Quinn didn't shout."
9"Shouting wasted breath, and breath"
10"She tracked him past the"
11"Her thoughts flickered back, uninvited,"
12"Something the coroner's report couldn't"
13"She wasn't about to lose"
14"The runner cut hard to"
15"Quinn didn't hesitate."
16"She hit the top step"
17"The metallic clang of her"
18"The air changed instantly as"
19"The clean, wet bite of"
ratio0.813
100.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount5
totalSentences48
matches
0"Now, it was alive."
1"If she went deeper into"
2"Yet, as she scanned the"
3"To follow him meant stepping"
4"To turn back meant admitting"
ratio0.104
7.72% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences37
technicalSentenceCount7
matches
0"The rain in Soho was the kind that had no intention of stopping, a needle-sharp drizzle that soaked through wool coats and made the asphalt gleam like black gla…"
1"He was agile, weaving through a knot of pedestrians outside a curry house with the desperate speed of a man who knew what waited for him if he was caught."
2"The runner cut hard to the left, diving down an iron-railed stairwell that plunged into the earth."
3"Quinn had heard the rumors in the precinct whispers, the wild tales told by weary cops working the midnight shift about an underground bazaar that shifted its l…"
4"Torches flared in iron brackets along the curved brick walls, casting long, writhing shadows that stretched across hundreds of figures moving through the gloom."
5"Quinn caught brief, jarring glimpses of wares that defied the logic of the city above: glass jars swirling with luminescent purple smoke, racks of wicked-lookin…"
6"Yet, as she scanned the crowd, she spotted a flash of the dark jacket near a rusted turnstile that marked the perimeter of the platform."
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
82.0342%