Run: 2026-02-25-01KJBFXYDV1HHVY2J46SRRQAJD

Run Details

Duration:
28.3s
Cost:
$0.012476
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
The rain did not fall in London so much as it hung in the air, a cold, wet shroud that turned the streetlamps into blurred halos of sodium orange. Detective Harlow Quinn pulled her collar up, the damp wool scratching against her sharp jawline, but she did not slow down. Her boots struck the wet pavement with a rhythmic, military precision that belied the exhaustion dragging at her shoulders. Eighteen years on the force had taught her that the city breathed differently at night, exhaling secrets through the grates and alleyways that the daylight hid. Ahead, the figure in the dark coat scrambled over a low brick wall. Harlow didn't hesitate. She vaulted it, her breath pluming in the chill. She had been tracking him for three blocks, ever since he slipped out of the back exit of The Raven's Nest. The bar’s distinctive green neon sign had buzzed in the distance, a sickly glow against the grey brick of Soho, marking the last known location of the man she was hunting. She knew his name was Tomás Herrera, though the file said he was a paramedic. The file didn't mention the way he moved, fluid and silent, like a man who knew where the shadows were deepest. She rounded a corner onto a narrower thoroughfare, the smell of wet asphalt and rotting garbage thick in her nose. The streetlights here were fewer, the darkness more absolute. She caught a glimpse of olive skin and dark curly hair as he ducked under a rusted fire escape. Harlow checked her left wrist. The worn leather strap of her watch was slick with rain. 11:43 PM. Time was a luxury she didn't have. Three years ago, DS Morris had vanished in a place like this. No body, no evidence, just a radio call that cut off mid-sentence. The official report said accidental drowning. Harlow knew better. She had seen the look in Morris's eyes before he left that day, a look of terrified recognition. She had spent the last three years chasing ghosts, and now, she thought, staring at the dark mouth of the abandoned Tube station beneath Camden, she might have found the one that took him. The suspect reached the entrance to the station. It was a concrete throat, choked with weeds and debris, the iron gates rusted shut. But the man didn't stop. He didn't try to open the gate. He stepped into the shadows of the ticket booth, where the darkness seemed to ripple. Harlow skidded to a halt ten yards away, her hand hovering over the grip of her service weapon. "Police!" she shouted, her voice cutting through the drumming rain. "Herrera! Stop!" The figure paused. He turned, just enough for the streetlight to catch the glint of metal around his neck. A Saint Christopher medallion. She saw the scar running along his left forearm, white against his skin, a jagged line from a knife attack that never made the papers. He looked at her, his warm brown eyes unreadable, then he vanished into the gloom of the station. Harlow moved forward, her heart hammering against her ribs. She reached the ticket booth. There was no door, no gate. Just a wall of shadow that felt heavier than the rain. She stepped closer, her boots crunching on broken glass. The air here was different. It was stale, smelling of ozone and old copper, a scent that triggered a visceral memory of Morris's final case. The hair on her arms stood up. She could hear him moving deeper inside. Footsteps on metal grating. He was leading her somewhere. She knew the protocols. She knew she should call for backup, secure the perimeter, wait for the tactical team. But the tactical team didn't understand the things that lived in the gaps between the bricks. They didn't know about the Veil Market, the rumors she had chased in the margins of case files that were too classified for the general public. The entry requirement was a bone token. She knew that now. She had read the intelligence reports that had been redacted until they were barely legible. Without it, the market was a labyrinth that could swallow a person whole. She didn't have a token. She had a gun, a badge, and a memory of a partner who had died because she wasn't fast enough, or smart enough, to see the truth. She looked back at the street. The rain was relentless, washing the city clean of its sins, but this place was different. This place kept its sins. If she turned back, she would write it up as a dead end. Another ghost. Another night where the city swallowed something and gave nothing back. If she went in, she risked becoming the next entry in the cold case files. Her hand tightened on her weapon. The leather of her watch creaked as she flexed her wrist. She thought of Morris's face, the way he had laughed at her coffee order that morning. She thought of the green neon sign of the Raven's Nest fading in the rearview mirror of her mind. The footsteps inside stopped. A voice, low and urgent, echoed from the darkness. "You can't stay here, Detective. The walls listen." Harlow didn't answer. She took a step forward. The air pressure dropped, popping her ears. The darkness ahead wasn't empty; it was waiting. It was a threshold, a line between the world of law and the world of things that hunted in the dark. She knew she wouldn't find a token. She would have to force her way through, or die trying. She holstered her gun. It wouldn't help her here. She pulled her flashlight from her belt, the beam cutting a weak cone through the gloom. She stepped over the threshold, into the abandoned station. The rain stopped hitting her face. The sound of the city vanished, replaced by a low, humming vibration that she felt in her teeth. She was in. The decision was made. There was no going back. She walked deeper into the dark, toward the sound of breathing that wasn't hers, toward the market that moved with the moon, toward the truth that had cost her partner his life. She kept her eyes on the path, her military bearing rigid, her jaw set. If this was a trap, she would walk into it with her eyes open. If this was a door, she would kick it down. The tunnel stretched ahead, lined with tiles that shimmered with a wet, unnatural sheen. She moved forward, one foot in front of the other, leaving the rain behind her. The Veil Market waited, and for the first time in three years, Harlow Quinn felt like she was finally close to the light.
created_at"2026-02-25T22:50:18.433Z" 2/25/2026, 10:50:18 PM
model"qwen/qwen3.5-122b-a10b-20260224"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency510
moderation_latency(null)
generation_time28149
tokens_prompt914
tokens_completion4162
native_tokens_prompt870
native_tokens_completion3790
native_tokens_completion_images(null)
native_tokens_reasoning2377
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"
usage0.012476
router(null)
provider_responses
0
endpoint_id"33d466d1-d2f7-45ed-8ff3-fda7d2e32351"
id"chatcmpl-264b138f-c6ad-9dd4-8198-1a25d9623029"
is_byokfalse
latency510
model_permaslug"qwen/qwen3.5-122b-a10b-20260224"
provider_name"Alibaba"
status200
api_type"completions"
id"gen-1772059818-3FsS66aMeWea1U3TpHYZ"
upstream_id"chatcmpl-264b138f-c6ad-9dd4-8198-1a25d9623029"
total_cost0.012476
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
totalTags1
adverbTagCount0
adverbTags(empty)
dialogueSentences3
tagDensity0.333
leniency0.667
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1127
totalAiIsmAdverbs0
found(empty)
highlights(empty)
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)
55.63% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1127
totalAiIsms10
found
0
word"rhythmic"
count1
1
word"chill"
count1
2
word"glint"
count1
3
word"unreadable"
count1
4
word"gloom"
count2
5
word"footsteps"
count2
6
word"echoed"
count1
7
word"shimmered"
count1
highlights
0"rhythmic"
1"chill"
2"glint"
3"unreadable"
4"gloom"
5"footsteps"
6"echoed"
7"shimmered"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches1
maxInWindow1
found
0
label"hung in the air"
count1
highlights
0"hung in the air"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences93
matches(empty)
81.41% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount2
narrationSentences93
filterMatches
0"watch"
hedgeMatches
0"try to"
1"seemed to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences95
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
totalWords1127
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions5
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions30
wordCount1116
uniqueNames15
maxNameDensity0.72
worstName"Harlow"
maxWindowNameDensity1.5
worstWindowName"Harlow"
discoveredNames
London1
Harlow8
Quinn2
Raven2
Nest2
Soho1
Tomás1
Herrera1
Morris4
Tube1
Camden1
Saint1
Christopher1
Veil2
Market2
persons
0"Harlow"
1"Quinn"
2"Raven"
3"Nest"
4"Tomás"
5"Herrera"
6"Morris"
7"Saint"
8"Christopher"
places
0"London"
1"Soho"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences69
glossingSentenceCount1
matches
0"felt like she was finally close to the"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1127
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences95
matches
0"left that day"
58.04% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs17
mean66.29
std23.41
cv0.353
sampleLengths
095
1113
273
385
468
512
666
772
877
971
1068
1152
1221
1362
1458
1582
1652
97.72% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences93
matches
0"been redacted"
1"was made"
88.27% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount3
totalVerbs179
matches
0"was hunting"
1"was leading"
2"was waiting"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount1
flaggedSentences1
totalSentences95
ratio0.011
matches
0"The darkness ahead wasn't empty; it was waiting."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1117
adjectiveStacks0
stackExamples(empty)
adverbCount20
adverbRatio0.017905102954341987
lyAdverbCount5
lyAdverbRatio0.004476275738585497
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences95
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences95
mean11.86
std7.58
cv0.639
sampleLengths
029
121
219
326
413
53
69
721
831
915
1021
1120
129
1319
145
1511
162
177
1812
1912
206
213
2218
2334
248
2515
265
277
2815
2918
3010
312
323
3316
344
3525
3618
379
385
396
4011
419
425
4320
447
457
464
475
484
4915
33.16% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats16
diversityRatio0.2736842105263158
totalSentences95
uniqueOpeners26
36.63% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences91
matches
0"Just a wall of shadow"
ratio0.011
30.99% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount43
totalSentences91
matches
0"Her boots struck the wet"
1"She vaulted it, her breath"
2"She had been tracking him"
3"She knew his name was"
4"She rounded a corner onto"
5"She caught a glimpse of"
6"She had seen the look"
7"She had spent the last"
8"It was a concrete throat,"
9"He didn't try to open"
10"He stepped into the shadows"
11"she shouted, her voice cutting"
12"He turned, just enough for"
13"She saw the scar running"
14"He looked at her, his"
15"She reached the ticket booth."
16"She stepped closer, her boots"
17"It was stale, smelling of"
18"She could hear him moving"
19"He was leading her somewhere."
ratio0.473
36.92% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount77
totalSentences91
matches
0"The rain did not fall"
1"Detective Harlow Quinn pulled her"
2"Her boots struck the wet"
3"Harlow didn't hesitate."
4"She vaulted it, her breath"
5"She had been tracking him"
6"The bar’s distinctive green neon"
7"She knew his name was"
8"The file didn't mention the"
9"She rounded a corner onto"
10"The streetlights here were fewer,"
11"She caught a glimpse of"
12"Harlow checked her left wrist."
13"The worn leather strap of"
14"Time was a luxury she"
15"The official report said accidental"
16"Harlow knew better."
17"She had seen the look"
18"She had spent the last"
19"The suspect reached the entrance"
ratio0.846
100.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount4
totalSentences91
matches
0"If she turned back, she"
1"If she went in, she"
2"If this was a trap,"
3"If this was a door,"
ratio0.044
68.45% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences48
technicalSentenceCount5
matches
0"The rain did not fall in London so much as it hung in the air, a cold, wet shroud that turned the streetlamps into blurred halos of sodium orange."
1"Her boots struck the wet pavement with a rhythmic, military precision that belied the exhaustion dragging at her shoulders."
2"It was stale, smelling of ozone and old copper, a scent that triggered a visceral memory of Morris's final case."
3"It was a threshold, a line between the world of law and the world of things that hunted in the dark."
4"The tunnel stretched ahead, lined with tiles that shimmered with a wet, unnatural sheen."
0.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags1
uselessAdditionCount1
matches
0"she shouted, her voice cutting through the drumming rain"
0.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags1
fancyCount1
fancyTags
0"she shouted (shout)"
dialogueSentences3
tagDensity0.333
leniency0.667
rawRatio1
effectiveRatio0.667
79.5742%