Run: 2026-02-25-01KJBHF2R3QYE86ZYRMP1KAC2Z

Run Details

Duration:
22.0s
Cost:
$0.007143
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 hammered against the asphalt, a relentless drumbeat masking the sound of Quinn’s boots. She didn’t run like a civilian. She moved with the economy of motion drilled into her during eighteen years of service. Her boots splashed through deep puddles reflecting the fractured orange of streetlights, her trench coat heavy with water, plastered to her back. She checked her watch, the worn leather strap digging into her left wrist. The digital readout glowed faintly through the downpour: 02:14. Time was a luxury she didn’t have. Ahead, the suspect slipped between the parked cars like smoke. Tomás Herrera. She had seen his face often enough during the surveillance ops in Soho, often enough to know the way he favored his left side, nursing an old injury. He was breathing hard now, the rhythm of his respiration audible over the rain even at this distance. He ducked into the shadow of an alleyway that smelled of stale urine and wet cardboard. Quinn didn’t hesitate. She pressed her back against the brick, scanned the corners, then moved forward. Tomás emerged from the alley, but not back onto the street. He turned into a gaping maw of concrete that shouldn’t have existed here. An abandoned Tube station entrance, the one that had been shuttered after Camden closure. The iron doors were rusted and chained, but the lock hanging from the side had been cut, the chain hanging slack in the wind. He didn’t go through the gates; he climbed over the barrier, dropping down into the tunnel darkness below. Quinn halted at the top of the stairs. The smell rising from the abyss was wrong. It wasn’t the usual mold and dust of a sealed station. It smelled of ozone, dried herbs, and something sharp, like blood on copper. She holstered her sidearm. This was past procedure. This was past jurisdiction. The rain was still pouring, but the sound changed as she stood at the precipice. The roar of the city faded, replaced by a low hum vibrating through the soles of her shoes. Tomás’s voice drifted up, muffled but clear. ‘You can’t stop it, Quinn. You saw what happened to Morris.’ Her jaw tightened, the muscles locking hard. The memory of that night three years ago flashed behind her eyes. Morris wasn’t just dead; he was unravelled. They found his badge in a alleyway like this, but his body was never found, only a stain. The official report said drug overdose. Quinn knew better. She had known better ever since the supernatural whispers started curling around the edges of the Met. She looked back at the street level, then down at the darkness. Her hand drifted to the worn leather of her watch. She was a detective, not a soldier, but the bearing remained. She was a blade in a world that was becoming too soft, too forgiving. ‘Herrera!’ she shouted, her voice cutting through the drumming rain. He paused at the top of the stairs, looking up. His short curly hair was plastered to his forehead, water streaming down his olive skin. He looked exhausted, his chest heaving under his damp jacket. A silver chain glinted against his throat, a Saint Christopher medallion swinging with his movement. He wasn’t fleeing anymore. He was waiting. ‘You think I’m the criminal?’ Tomás asked. His warm brown eyes were filled with a mixture of pity and fear. ‘I’m the one patching the holes you refuse to acknowledge.’ ‘The clique is smuggling something,’ Quinn said, taking one step down onto the first concrete tread. Her boots made no sound on the stone. ‘And you’re running.’ ‘I’m protecting people. You know what happened to the patients. The NHS wouldn’t touch them. You think the police would?’ He held up his hand. In his palm sat a small, irregular object. It was bone. Pale, calcified, carved into a rough token shape. ‘They need this place. It moves. Tonight, it’s here. If they lose this access, people die.’ Quinn looked at the bone token. It wasn’t just a key. It was a signal. She had heard the rumors about the Veil Market, the black market that operated under the full moon, shifting beneath the capital like a parasite. She had suspected it for months. Her partner’s case had led her down this path, and Morris had vanished into it. The silence from below stretched. The low hum grew louder, a thrumming that felt like it was settling in her teeth. The air grew cold, the warmth of the rain suddenly turning into a biting chill that seeped through her coat. ‘Turn back, Quinn,’ Tomás said, his voice dropping an octave. ‘You know what’s down there. Not the drugs. Not the weapons. The things that don’t come back from the dead. Morris didn’t run. He went in.’ Quinn felt the weight of her weapon against her hip. It was steel and fire. It didn’t know the difference between a man and a monster. But her orders didn’t cover the supernatural. If she went in, she went alone. If she didn’t, Tomás and everyone else down there were left to whatever darkness lurked in that station. She stepped again. The concrete was cold, damp with condensation that shouldn’t have been there. The light from the street above was fading, leaving her in a gloom that felt ancient. She looked up at Tomás one last time. He held the bone token up, shielding it from her gaze. ‘I’m not arresting you tonight, Tomás,’ Quinn said, her voice steady despite the tremor in her fingers. ‘I’m arresting the truth.’ She reached for the medallion at her neck, though she didn’t wear one. She adjusted her grip on her gun. She was thinking of Morris’s voice, the way he had laughed before the hunt ended. She was thinking of the clique, the way they used the city as a canvas for their crimes. Quinn stepped into the tunnel. The water dripped from the ceiling now, pooling around her boots. The smell of ozone was overwhelming, mixing with the scent of old dust. ‘Come,’ Tomás said, turning back into the shadow. Quinn followed. She didn’t draw her gun. Not yet. The darkness swallowed the steps, the sound of the rain fading into nothingness. She was a woman with a badge and a warrant, walking into a place that shouldn’t exist. Her military bearing kept her spine straight, her jaw sharp, but her heart was hammering against her ribs like a trapped bird. She checked her watch one last time. The digital glow was all she had to see by. 02:32. She didn’t know if she was hunting a suspect or stepping into a grave. But as the darkness closed around her, she made herself a promise. She would find the line between the two, and she would cross it. She stepped forward into the abyss, the sound of her breathing the only anchor she had left.
created_at"2026-02-25T23:17:08.528Z" 2/25/2026, 11:17:08 PM
model"qwen/qwen3.5-35b-a3b-20260224"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency455
moderation_latency(null)
generation_time21818
tokens_prompt914
tokens_completion3814
native_tokens_prompt870
native_tokens_completion3463
native_tokens_completion_images(null)
native_tokens_reasoning1979
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"
usage0.0071435
router(null)
provider_responses
0
endpoint_id"48c1210f-b7d2-4688-a340-f7424fc99d2b"
id"chatcmpl-a325a855-5618-96e7-9ebd-940d2555d05f"
is_byokfalse
latency455
model_permaslug"qwen/qwen3.5-35b-a3b-20260224"
provider_name"Alibaba"
status200
api_type"completions"
id"gen-1772061428-hsGFruuUClDD6CSRNYSY"
upstream_id"chatcmpl-a325a855-5618-96e7-9ebd-940d2555d05f"
total_cost0.0071435
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
totalTags4
adverbTagCount0
adverbTags(empty)
dialogueSentences0
tagDensity1
leniency1
rawRatio0
effectiveRatio0
95.66% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1152
totalAiIsmAdverbs1
found
0
adverb"suddenly"
count1
highlights
0"suddenly"
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)
56.60% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1152
totalAiIsms10
found
0
word"fractured"
count1
1
word"scanned"
count1
2
word"maw"
count1
3
word"streaming"
count1
4
word"silence"
count1
5
word"warmth"
count1
6
word"chill"
count1
7
word"weight"
count1
8
word"gloom"
count1
9
word"grave"
count1
highlights
0"fractured"
1"scanned"
2"maw"
3"streaming"
4"silence"
5"warmth"
6"chill"
7"weight"
8"gloom"
9"grave"
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
emotionTells0
narrationSentences103
matches(empty)
45.77% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount5
hedgeCount2
narrationSentences103
filterMatches
0"watch"
1"know"
2"think"
hedgeMatches
0"happened to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences116
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen28
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1152
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions7
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions29
wordCount1041
uniqueNames13
maxNameDensity0.77
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Quinn"
discoveredNames
Quinn8
Herrera1
Soho1
Tube1
Camden1
Met1
Saint1
Christopher1
Veil1
Market1
Morris4
Tomás5
You3
persons
0"Quinn"
1"Herrera"
2"Saint"
3"Christopher"
4"Morris"
5"Tomás"
6"You"
places
0"Soho"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences75
glossingSentenceCount1
matches
0"felt like it was settling in her teeth"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches1
per1kWords0.868
wordCount1152
matches
0"not a soldier, but the bearing remained"
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences116
matches(empty)
94.64% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs24
mean48
std23.1
cv0.481
sampleLengths
086
190
280
352
440
511
670
747
810
957
1030
1127
1260
1361
1441
1536
1658
1750
1821
1953
2029
218
2279
2356
84.82% Passive voice overuse
Target: ≤2% passive sentences
passiveCount6
totalSentences103
matches
0"been shuttered"
1"were rusted"
2"was unravelled"
3"was plastered"
4"were filled"
5"were left"
0.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount7
totalVerbs175
matches
0"was breathing"
1"was still pouring"
2"wasn’t fleeing"
3"was waiting"
4"was settling"
5"was hammering"
6"was hunting"
93.60% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount2
flaggedSentences2
totalSentences116
ratio0.017
matches
0"He didn’t go through the gates; he climbed over the barrier, dropping down into the tunnel darkness below."
1"Morris wasn’t just dead; he was unravelled."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount348
adjectiveStacks0
stackExamples(empty)
adverbCount7
adverbRatio0.020114942528735632
lyAdverbCount1
lyAdverbRatio0.0028735632183908046
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences116
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences116
mean9.93
std5.69
cv0.573
sampleLengths
014
16
215
322
413
59
67
710
82
928
1018
1116
123
1313
1411
1513
1614
1724
1818
198
208
2111
2213
234
244
254
2615
2718
287
295
306
317
3212
337
3418
356
363
3717
3812
3910
4011
4114
4210
4310
4415
4510
4615
474
483
497
30.00% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats23
diversityRatio0.24347826086956523
totalSentences115
uniqueOpeners28
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences97
matches(empty)
ratio0
5.57% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount52
totalSentences97
matches
0"She didn’t run like a"
1"She moved with the economy"
2"Her boots splashed through deep"
3"She checked her watch, the"
4"She had seen his face"
5"He was breathing hard now,"
6"He ducked into the shadow"
7"She pressed her back against"
8"He turned into a gaping"
9"He didn’t go through the"
10"It wasn’t the usual mold"
11"It smelled of ozone, dried"
12"She holstered her sidearm."
13"You saw what happened to"
14"Her jaw tightened, the muscles"
15"They found his badge in"
16"She had known better ever"
17"She looked back at the"
18"Her hand drifted to the"
19"She was a detective, not"
ratio0.536
16.70% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount86
totalSentences97
matches
0"She didn’t run like a"
1"She moved with the economy"
2"Her boots splashed through deep"
3"She checked her watch, the"
4"The digital readout glowed faintly"
5"Time was a luxury she"
6"She had seen his face"
7"He was breathing hard now,"
8"He ducked into the shadow"
9"Quinn didn’t hesitate."
10"She pressed her back against"
11"Tomás emerged from the alley,"
12"He turned into a gaping"
13"An abandoned Tube station entrance,"
14"The iron doors were rusted"
15"He didn’t go through the"
16"Quinn halted at the top"
17"The smell rising from the"
18"It wasn’t the usual mold"
19"It smelled of ozone, dried"
ratio0.887
100.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount3
totalSentences97
matches
0"If they lose this access,"
1"If she went in, she"
2"If she didn’t, Tomás and"
ratio0.031
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences53
technicalSentenceCount3
matches
0"She was a blade in a world that was becoming too soft, too forgiving."
1"She had heard the rumors about the Veil Market, the black market that operated under the full moon, shifting beneath the capital like a parasite."
2"Her military bearing kept her spine straight, her jaw sharp, but her heart was hammering against her ribs like a trapped bird."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags4
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags0
80.7785%