Run: 2026-02-25-01KJBHQZR21CVFM8QFPPYSA1X5

Run Details

Duration:
54.7s
Cost:
$0.008164
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
Rain lashed against the back of Detective Harlow Quinn’s neck, cold and relentless, turning the collar of her trench coat into a sodden weight. She didn’t check her watch, not because she didn’t need to, but because she could feel the rhythm of the pursuit in the pulse hammering against the worn leather strap on her left wrist. Eighteen years of service had taught her that time was a luxury, and right now, she was running out of both. Ahead, a shadow detached itself from the gloom of a Camden alleyway. The suspect moved with a fluidity that didn’t match the clumsy panic of a standard criminal. They knew where the cameras were blind spots. They knew where the pavement was slick with oil. Quinn adjusted her grip on the flashlight clipped to her belt, her military-trained stride eating up the distance despite the fatigue screaming in her calves. She was forty-one, and the ache in her joints was a constant reminder of the years spent chasing ghosts in the city’s underbelly, but tonight, the ghost was running. The suspect darted toward a service entrance behind a shuttered electronics store. Quinn didn’t hesitate. She hit the brick wall with her shoulder, the impact jarring her teeth, and vaulted over the low barrier separating the sidewalk from the service yard. Her boots skidded on wet concrete, but her sharp jaw set with determination. She saw the figure pause at a heavy steel door, one hand fumbling with a keycard. “Police!” Quinn’s voice cracked through the downpour, authoritative and flat. “Freeze!” The figure didn’t freeze. Instead, they slammed the door shut. Quinn hit it a second later, the steel groaning under the force of her body. It was locked, reinforced. She cursed under her breath, the sound swallowed by the thunder rolling overhead. She stepped back, scanning the perimeter. There was no other exit, not unless the suspect had wings. Then she saw it—a ventilation grate near the ground, rusted and loose. Too small for a grown man, but the suspect had slipped through the alley with unnatural speed. She dropped to her knees, the rain soaking her trousers instantly. Her fingers found the rusted hinges and pried. Metal shrieked against metal. She shoved the grate aside and peered into the darkness beyond. It wasn’t a basement. It was a tunnel, descending steeply into the earth. The air rising from it didn’t smell of damp sewage or old trash. It smelled of ozone, copper, and something sweetly rotting, like lilies left too long in a vase. Quinn’s stomach tightened. She had chased leads like this before, three years ago, when Morris had vanished. The unexplained circumstances of his death were a scar she wore beneath her badge, a wound that never fully healed. She had sworn then that she would find the truth, no matter how deep it lay. She holstered her radio, knowing the signal wouldn’t penetrate the underground. She checked her sidearm, the cold steel familiar and comforting. Then she looked down the tunnel. The walls were tiled in the faded green of the London Underground, but the tracks were gone, replaced by a dirt path winding into the dark. A faint, pulsating light emanated from the depths, shifting colors like oil on water. The Veil Market. She had heard the whispers in the precinct, the rumors of a black market that moved with the moon. She had dismissed them as urban legend until the pattern of disappearances started matching the lunar cycle. Tonight was the full moon. Tonight, the market was here, beneath Camden. Quinn crouched at the edge of the tunnel entrance. The rain above was a roar, but down here, the silence was heavy, pressing against her eardrums. She could hear breathing that wasn’t hers. The suspect was down there. She needed a bone token to enter legally. That was the intel she had scraped together from informants who wouldn’t meet her in daylight. Without it, she was trespassing in a sovereign territory of the supernatural. If she went down, she was off the grid. No backup, no jurisdiction, no rules. She looked at her watch. The second hand swept past the twelve. She thought of Morris. He had gone down a hole like this. He had come back wrong, or maybe he hadn’t come back at all. The thought was a cold stone in her gut. But the suspect was in there. The suspect knew about the clique. The suspect knew about the alchemical substances that were fueling the violence in Soho. If she turned back, the trail went cold. If she went in, she might end up like Morris. Quinn took a deep breath, the air tasting of wet wool and old earth. She didn’t wait for permission. She didn’t wait for a warrant. She slid into the tunnel, her boots finding purchase on the slick dirt. The descent was steep. The light grew stronger as she moved deeper, revealing stalls set up along the walls of the abandoned station. Canvases were draped over rusted supports, hiding the faces of the vendors. She saw a figure selling jars of glowing liquid, the contents swirling with a life of their own. She saw a table piled with feathers that moved on their own accord. The air was thick with the hum of conversation, a low murmur that vibrated in her teeth. No one looked at her. They were too busy with their trade, their eyes fixed on the goods or the coins changing hands. Quinn kept her head down, moving with the military precision of a soldier in a war zone. She scanned the crowd, looking for the shape of the suspect. She found him near the far end, haggling over a vial of silver dust. He wore a hood, but she recognized the gait. He turned, sensing her presence. His eyes were dark, too dark, reflecting the neon glow of the market. He didn’t run. He just looked at her, a small, knowing smile playing on his lips. Quinn’s hand hovered over her weapon. She was outnumbered. She was out of her depth. But she was also the only one who knew how to handle a gun, and the only one who knew the law, even if it didn’t apply down here. She stepped forward, her boots crunching on the gravel. “I’m not leaving,” she said, her voice cutting through the hum. The vendor behind the silver dust looked up, his eyes glowing faintly in the dim light. He didn’t speak, but he reached for something under the counter. Quinn didn’t flinch. She had made her choice the moment she slid into the tunnel. The rain above was a memory now. Here, in the dark, the only thing that mattered was the truth, and the price she was willing to pay for it. She tightened her grip on her sidearm. The market held its breath. The chase was over. The confrontation had just begun.
created_at"2026-02-25T23:22:00.33Z" 2/25/2026, 11:22:00 PM
model"qwen/qwen3.5-27b-20260224"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency879
moderation_latency(null)
generation_time54672
tokens_prompt914
tokens_completion3642
native_tokens_prompt870
native_tokens_completion3293
native_tokens_completion_images(null)
native_tokens_reasoning1829
native_tokens_cached0
num_media_prompt(null)
num_input_audio_prompt(null)
num_media_completion0
num_search_results(null)
origin"https://ncbench.com/"
is_byokfalse
finish_reason"stop"
native_finish_reason"stop"
usage0.0081642
router(null)
provider_responses
0
endpoint_id"f3f88554-0c22-4d57-973b-0dec948808e8"
id"chatcmpl-7953496e-e084-98b9-bcef-8c53f18daa8e"
is_byokfalse
latency879
model_permaslug"qwen/qwen3.5-27b-20260224"
provider_name"Alibaba"
status200
api_type"completions"
id"gen-1772061720-U8C6w3XvctmJ2JFF8zir"
upstream_id"chatcmpl-7953496e-e084-98b9-bcef-8c53f18daa8e"
total_cost0.0081642
cache_discount(null)
upstream_inference_cost0
provider_name"Alibaba"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags2
adverbTagCount0
adverbTags(empty)
dialogueSentences3
tagDensity0.667
leniency1
rawRatio0
effectiveRatio0
95.69% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1160
totalAiIsmAdverbs1
found
0
adverb"sweetly"
count1
highlights
0"sweetly"
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)
39.66% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1160
totalAiIsms14
found
0
word"weight"
count1
1
word"could feel"
count1
2
word"pulse"
count1
3
word"gloom"
count1
4
word"standard"
count1
5
word"reminder"
count1
6
word"scanning"
count1
7
word"stomach"
count1
8
word"familiar"
count1
9
word"comforting"
count1
10
word"depths"
count1
11
word"silence"
count1
12
word"vibrated"
count1
13
word"scanned"
count1
highlights
0"weight"
1"could feel"
2"pulse"
3"gloom"
4"standard"
5"reminder"
6"scanning"
7"stomach"
8"familiar"
9"comforting"
10"depths"
11"silence"
12"vibrated"
13"scanned"
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
emotionTells1
narrationSentences102
matches
0"t with determination"
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount0
narrationSentences102
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences103
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen34
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1159
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions3
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions23
wordCount1154
uniqueNames10
maxNameDensity0.95
worstName"Quinn"
maxWindowNameDensity2
worstWindowName"Quinn"
discoveredNames
Detective1
Harlow1
Quinn11
Camden2
Morris3
London1
Underground1
Veil1
Market1
Soho1
persons
0"Harlow"
1"Quinn"
2"Morris"
3"Market"
places
0"London"
1"Soho"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences81
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1159
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences103
matches(empty)
96.89% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs23
mean50.39
std24.65
cv0.489
sampleLengths
079
199
270
311
410
578
677
753
867
93
1048
1138
1251
1346
1444
1538
1666
1768
1857
1944
2020
2171
2221
94.94% Passive voice overuse
Target: ≤2% passive sentences
passiveCount3
totalSentences102
matches
0"were tiled"
1"were gone"
2"were draped"
3"was outnumbered"
26.39% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount5
totalVerbs192
matches
0"was running"
1"was running"
2"was trespassing"
3"were fueling"
4"was willing"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount1
semicolonCount0
flaggedSentences1
totalSentences103
ratio0.01
matches
0"Then she saw it—a ventilation grate near the ground, rusted and loose."
94.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1157
adjectiveStacks1
stackExamples
0"heavy, pressing against her"
adverbCount25
adverbRatio0.021607605877268798
lyAdverbCount11
lyAdverbRatio0.00950734658599827
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences103
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences103
mean11.25
std6.57
cv0.584
sampleLengths
024
134
221
312
416
58
69
725
829
912
103
1126
1213
1316
1410
151
164
176
1815
194
2013
216
2211
2312
2417
2511
268
274
2811
294
309
3113
3217
333
3414
3520
3616
3711
3810
396
4026
4114
423
4319
4417
455
467
479
4817
497
30.58% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats20
diversityRatio0.27184466019417475
totalSentences103
uniqueOpeners28
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount4
totalSentences102
matches
0"Instead, they slammed the door"
1"Then she saw it—a ventilation"
2"Too small for a grown"
3"Then she looked down the"
ratio0.039
31.76% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount48
totalSentences102
matches
0"She didn’t check her watch,"
1"They knew where the cameras"
2"They knew where the pavement"
3"She was forty-one, and the"
4"She hit the brick wall"
5"Her boots skidded on wet"
6"She saw the figure pause"
7"It was locked, reinforced."
8"She cursed under her breath,"
9"She stepped back, scanning the"
10"She dropped to her knees,"
11"Her fingers found the rusted"
12"She shoved the grate aside"
13"It wasn’t a basement."
14"It was a tunnel, descending"
15"It smelled of ozone, copper,"
16"She had chased leads like"
17"She had sworn then that"
18"She holstered her radio, knowing"
19"She checked her sidearm, the"
ratio0.471
33.53% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount87
totalSentences102
matches
0"Rain lashed against the back"
1"She didn’t check her watch,"
2"The suspect moved with a"
3"They knew where the cameras"
4"They knew where the pavement"
5"Quinn adjusted her grip on"
6"She was forty-one, and the"
7"The suspect darted toward a"
8"Quinn didn’t hesitate."
9"She hit the brick wall"
10"Her boots skidded on wet"
11"She saw the figure pause"
12"Quinn’s voice cracked through the"
13"The figure didn’t freeze."
14"Quinn hit it a second"
15"It was locked, reinforced."
16"She cursed under her breath,"
17"She stepped back, scanning the"
18"She dropped to her knees,"
19"Her fingers found the rusted"
ratio0.853
100.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount3
totalSentences102
matches
0"If she went down, she"
1"If she turned back, the"
2"If she went in, she"
ratio0.029
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences53
technicalSentenceCount2
matches
0"The suspect moved with a fluidity that didn’t match the clumsy panic of a standard criminal."
1"The suspect knew about the alchemical substances that were fueling the violence in Soho."
0.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags2
uselessAdditionCount1
matches
0"she said, her voice cutting through the hum"
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags1
fancyCount0
fancyTags(empty)
dialogueSentences3
tagDensity0.333
leniency0.667
rawRatio0
effectiveRatio0
84.7814%