Run: 2026-02-25-01KJBH9GMRS3HFPVWJFEDQAJPW

Run Details

Duration:
44.4s
Cost:
$0.007996
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 cobblestones of Soho, turning the narrow streets into slick, black mirrors. Detective Harlow Quinn pulled her collar up, the dampness seeping through her trench coat to chill the skin beneath. Her salt-and-pepper hair clung to her scalp, heavy with water, but her brown eyes remained fixed ahead. She moved with military precision, her boots finding purchase where a civilian would have slipped. On her left wrist, the worn leather of her watch creaked as she checked the time. Three minutes since the sighting. Three minutes since she’d lost him in the shadow of a pub. She knew where he was going. Tomás Herrera. The former paramedic with the olive skin and the warm brown eyes that usually held a calm she found unnerving. He was running, which meant he was guilty. That was the rule she lived by, the only rule that kept the ghosts at bay. A flash of movement near the corner of a shuttered bakery. Quinn adjusted her course, cutting through an alleyway that smelled of rotting garbage and wet brick. She didn’t bother with the radio. Not here. Not for this. The Metropolitan Police didn’t have a file on what was coming next, and if she asked for backup, she’d have to explain why she was chasing a man who carried a Saint Christopher medallion into a graveyard at midnight. She burst out onto the main road near Camden, the rain intensifying. The city lights blurred into streaks of neon and sodium vapor. Ahead, a figure darted toward the mouth of an abandoned Tube station. Tomás. Quinn saw the glint of silver against his dark shirt as he moved—the medallion swinging against his chest. He wasn’t just running; he was fleeing into something. Quinn sprinted, her lungs burning. Eighteen years on the force had hardened her body, but the ache in her knees was a reminder that she was forty-one, not twenty. She reached the station entrance just as Tomás disappeared into the gloom. The metal gates were rusted shut, twisted into a jagged maw that shouldn’t have opened. Yet, it did. The hinges groaned, a sound like a dying animal, and the darkness within seemed to swallow the light from the streetlamps. She hesitated at the threshold. The air coming from the tunnel didn’t smell like the damp earth of a subway. It smelled of ozone, old paper, and something coppery, like blood. Tomás was already ten feet down the stairs. He paused, turning slightly. Quinn saw the scar running along his left forearm catch the dim light. He held up a small object in his hand. A bone token. It pulsed with a faint, sickly luminescence. He pressed it against the stone wall beside the doorframe. The stone rippled like water, and the path ahead cleared, revealing a corridor that shouldn’t exist. The Veil Market. Quinn knew the rumors. She had seen the files marked as urban legends, dismissed as mass hysteria by her superiors. But she had seen what happened to DS Morris three years ago. She had seen the way the shadows had lengthened around him, the way his eyes had gone wide with a terror that no human threat could inspire. She had found his badge in a place that didn’t have coordinates. She had never found the body. If she stepped down those stairs, she wasn’t just breaking protocol. She was stepping into a realm where the laws of physics bent to the whims of things she couldn’t name. Tomás didn’t wait. He stepped through the threshold. The air pressure dropped, popping the ears. Quinn felt the weight of the decision settle on her shoulders, heavier than her gear. To stop now was to let the trail go cold. To go in was to risk becoming another statistic in the files she kept locked in her safe at home. She looked at her watch again. The leather was stiff with age, just like her resolve. “Herrera!” she shouted. Her voice echoed down the shaft, distorted and thin. Tomás didn’t turn back. He knew she was coming. He was part of the clique, the shadow network she had been circling for months. He was the medic who stitched up wounds that shouldn’t have healed, who treated burns that smelled of sulfur. He was the link. Quinn took the first step. The metal grate beneath her boot felt solid, but the air was wrong. It hummed, a low vibration that rattled her teeth. She descended, her hand resting on her holster. The darkness ahead wasn’t empty. It was full of things watching. Halfway down, the rain stopped. The sound of the city above vanished, replaced by a low murmur of voices and the clinking of glass. The corridor opened up into a vast cavern, illuminated by strange, floating orbs of light. Stalls lined the walls, draped in velvet and shadow. This was the Veil Market. It moved locations every full moon, but tonight, it had settled beneath Camden. Quinn reached the bottom. The floor was paved with cobblestones that felt warm to the touch. She scanned the perimeter, her training kicking in. Cover, exit routes, threats. But there were no exits here, only the tunnel she had come down and the deeper recesses of the market. Tomás was gone, vanished into the crowd of figures that drifted between the stalls. Some looked human; others were indistinct, their edges blurred by the gloom. Quinn tightened her grip on her service revolver. She had no bone token. She had no authority here. She had only the memory of Morris and the need to know. She moved forward, keeping to the shadows of the stalls. A figure near a table piled with jars of glowing liquid turned to look at her. Its eyes were entirely black. Quinn didn’t flinch. She kept her face neutral, her jaw set. She was a detective. This was just another crime scene, even if the evidence defied logic. She spotted Tomás near a stall selling maps. The walls of the market were covered in old maps and black-and-white photographs, just like the Raven’s Nest bar, but these maps showed places that didn’t exist on any globe. Tomás was arguing with a vendor, his hands moving in the frantic gestures of someone trying to buy time. Quinn stepped out of the shadows. The murmur of the market died down. Heads turned. The air grew colder. “Tomás,” she said, her voice cutting through the silence. He spun around, his warm brown eyes wide. He saw her, then saw the gun in her hand. He raised his hands, the scar on his forearm stark against his skin. “You shouldn’t be here, Detective,” he said. His accent was thick, the Spanish rolling over the English. “This isn’t your jurisdiction.” “Jurisdiction ends where the law begins,” Quinn replied, stepping closer. “And I’m the law.” A low hiss rose from the crowd. The vendor behind Tomás reached for something under the counter. Quinn didn’t wait. She didn’t know what the token did, or how the magic worked, but she knew that if she waited, she would be trapped. “Come with me,” she said, nodding toward the tunnel. “Now.” Tomás looked at the vendor, then back at her. He saw the determination in her sharp jawline, the refusal to blink. He knew she wouldn’t leave until he was in custody or dead. He nodded slowly. “Follow me,” he whispered. “But don’t look at the shadows.” Quinn kept her eyes on him as they turned back toward the stairs. The market watched them leave, a thousand eyes in the dark. She didn’t look back. She knew the cost of curiosity. She had paid it once with Morris. She wouldn’t pay it again with her life. She climbed the stairs, the hum of the market fading behind her, the rain waiting for her at the top. She had made the choice. Now she had to survive the consequences.
created_at"2026-02-25T23:14:06.109Z" 2/25/2026, 11:14:06 PM
model"qwen/qwen3.5-27b-20260224"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency775
moderation_latency(null)
generation_time44358
tokens_prompt914
tokens_completion3586
native_tokens_prompt870
native_tokens_completion3223
native_tokens_completion_images(null)
native_tokens_reasoning1531
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"
usage0.0079962
router(null)
provider_responses
0
endpoint_id"f3f88554-0c22-4d57-973b-0dec948808e8"
id"chatcmpl-8fcc0541-9046-9855-982c-d5f1ae4ec7aa"
is_byokfalse
latency775
model_permaslug"qwen/qwen3.5-27b-20260224"
provider_name"Alibaba"
status200
api_type"completions"
id"gen-1772061246-ASpGHsGB85jTo8wvT11O"
upstream_id"chatcmpl-8fcc0541-9046-9855-982c-d5f1ae4ec7aa"
total_cost0.0079962
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
totalTags6
adverbTagCount0
adverbTags(empty)
dialogueSentences10
tagDensity0.6
leniency1
rawRatio0
effectiveRatio0
92.45% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1324
totalAiIsmAdverbs2
found
0
adverb"slightly"
count1
1
adverb"slowly"
count1
highlights
0"slightly"
1"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)
47.13% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1324
totalAiIsms14
found
0
word"chill"
count1
1
word"glint"
count1
2
word"reminder"
count1
3
word"gloom"
count2
4
word"maw"
count1
5
word"pulsed"
count1
6
word"weight"
count1
7
word"resolve"
count1
8
word"echoed"
count1
9
word"velvet"
count1
10
word"scanned"
count1
11
word"silence"
count1
12
word"stark"
count1
highlights
0"chill"
1"glint"
2"reminder"
3"gloom"
4"maw"
5"pulsed"
6"weight"
7"resolve"
8"echoed"
9"velvet"
10"scanned"
11"silence"
12"stark"
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
narrationSentences128
matches
0"was guilty"
98.21% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount2
narrationSentences128
filterMatches
0"watch"
hedgeMatches
0"seemed to"
1"happened to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences132
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen39
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1323
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions7
unquotedAttributions0
matches(empty)
91.86% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions47
wordCount1290
uniqueNames18
maxNameDensity1.16
worstName"Quinn"
maxWindowNameDensity2
worstWindowName"Quinn"
discoveredNames
Soho1
Harlow1
Quinn15
Herrera1
Metropolitan1
Police1
Saint1
Christopher1
Camden2
Tube1
Tomás11
Veil2
Market2
Morris3
Raven1
Nest1
Spanish1
English1
persons
0"Harlow"
1"Quinn"
2"Herrera"
3"Police"
4"Saint"
5"Christopher"
6"Tomás"
7"Market"
8"Morris"
9"Raven"
places
0"Soho"
1"Camden"
2"Nest"
globalScore0.919
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences90
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1323
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences132
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs30
mean44.1
std25.75
cv0.584
sampleLengths
099
152
277
363
480
531
670
73
877
931
1060
1116
1212
1347
1446
1566
1648
1756
1858
1957
2019
219
2231
2321
2414
2543
2610
2736
2810
2981
94.30% Passive voice overuse
Target: ≤2% passive sentences
passiveCount4
totalSentences128
matches
0"were rusted"
1"was paved"
2"was gone"
3"were covered"
0.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount10
totalVerbs232
matches
0"was going"
1"was running"
2"was coming"
3"was chasing"
4"was fleeing"
5"wasn’t just running"
6"wasn’t just breaking"
7"was stepping"
8"was coming"
9"was arguing"
77.92% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount1
semicolonCount2
flaggedSentences3
totalSentences132
ratio0.023
matches
0"Quinn saw the glint of silver against his dark shirt as he moved—the medallion swinging against his chest."
1"He wasn’t just running; he was fleeing into something."
2"Some looked human; others were indistinct, their edges blurred by the gloom."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1296
adjectiveStacks0
stackExamples(empty)
adverbCount24
adverbRatio0.018518518518518517
lyAdverbCount8
lyAdverbRatio0.006172839506172839
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences132
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences132
mean10.02
std6.3
cv0.629
sampleLengths
015
119
217
315
416
55
612
76
82
920
108
1116
1211
1316
146
152
163
1739
1812
1911
2012
211
2218
239
245
2524
2612
2715
283
2921
305
3115
3211
338
344
3513
369
373
387
3910
4016
413
424
4316
4412
4527
4612
476
4811
4920
32.58% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats23
diversityRatio0.26515151515151514
totalSentences132
uniqueOpeners35
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences121
matches(empty)
ratio0
38.18% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount55
totalSentences121
matches
0"Her salt-and-pepper hair clung to"
1"She moved with military precision,"
2"She knew where he was"
3"He was running, which meant"
4"She didn’t bother with the"
5"She burst out onto the"
6"He wasn’t just running; he"
7"She reached the station entrance"
8"She hesitated at the threshold."
9"It smelled of ozone, old"
10"He paused, turning slightly."
11"He held up a small"
12"It pulsed with a faint,"
13"He pressed it against the"
14"She had seen the files"
15"She had seen the way"
16"She had found his badge"
17"She had never found the"
18"She was stepping into a"
19"He stepped through the threshold."
ratio0.455
17.85% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount107
totalSentences121
matches
0"Rain lashed against the cobblestones"
1"Detective Harlow Quinn pulled her"
2"Her salt-and-pepper hair clung to"
3"She moved with military precision,"
4"She knew where he was"
5"The former paramedic with the"
6"He was running, which meant"
7"That was the rule she"
8"A flash of movement near"
9"Quinn adjusted her course, cutting"
10"She didn’t bother with the"
11"The Metropolitan Police didn’t have"
12"She burst out onto the"
13"The city lights blurred into"
14"Quinn saw the glint of"
15"He wasn’t just running; he"
16"Quinn sprinted, her lungs burning."
17"She reached the station entrance"
18"The metal gates were rusted"
19"The hinges groaned, a sound"
ratio0.884
100.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount5
totalSentences121
matches
0"Yet, it did."
1"If she stepped down those"
2"To stop now was to"
3"To go in was to"
4"Now she had to survive"
ratio0.041
83.33% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences60
technicalSentenceCount5
matches
0"Quinn adjusted her course, cutting through an alleyway that smelled of rotting garbage and wet brick."
1"The Metropolitan Police didn’t have a file on what was coming next, and if she asked for backup, she’d have to explain why she was chasing a man who carried a S…"
2"The stone rippled like water, and the path ahead cleared, revealing a corridor that shouldn’t exist."
3"The walls of the market were covered in old maps and black-and-white photographs, just like the Raven’s Nest bar, but these maps showed places that didn’t exist…"
4"Tomás was arguing with a vendor, his hands moving in the frantic gestures of someone trying to buy time."
41.67% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags6
uselessAdditionCount1
matches
0"she said, her voice cutting through the silence"
0.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags6
fancyCount2
fancyTags
0"she shouted (shout)"
1"he whispered (whisper)"
dialogueSentences10
tagDensity0.6
leniency1
rawRatio0.333
effectiveRatio0.333
77.1827%