Run: 2026-09-02-01M1H5E61934G3K9T9FSTZEQ19

Run Details

Duration:
55.8s
Cost:
$0.000428
Provider:
Z.AI

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 came down in sheets over Soho, turning the neon to smeared watercolor, and Tomás Herrera ran like a man who had done this before. Quinn kept him in sight the way she'd been taught eighteen years ago — not by watching the runner but by watching what the runner watched. He checked left before the alley. He didn't check the right, because he knew the right was safe. That told her he'd planned this, which told her she was right to chase him, which told her nothing she didn't already know at three o'clock in the morning with her coat soaked through and her lungs burning. "Herrera!" Her voice cracked against the brick. "Hospital records, Herrera. I just want to talk." He didn't slow. Of course he didn't. Nobody with a Saint Christopher medallion bouncing against his chest and a face like a confession on legs ever stopped for a detective. She'd seen him twice before, both times at the periphery of cases that had ended badly — cases that ended with DS Morris's name on a plaque in the lobby and no body and no answers. Herrera's face had been in the background of the third crime scene like a bad penny. She'd spent two years finding that penny again. He cut across Greek Street, dodging a delivery van that laid on its horn, and she followed, boots skidding on the wet cobbles. The city was empty at this hour except for the drunk and the desperate and the ones who worked the dark hours because daylight would kill them. She'd stopped believing that last part was a metaphor about a year ago. Herrera ducked down a service road behind the restaurants, past the reeking bins and the kitchen doors propped open for cigarette breaks, and went through a gate she would have missed if she hadn't been watching his shoulders, not his feet. She hit the gate with her hip. It gave. Beyond it, a stairwell descended into the dark, and her suspect was already three steps from gone. Quinn stopped at the top of the stairs. It was the oldest rule in the job, older than the job: never follow them into the box. Never go where they know the exits and you don't. Eighteen years of instinct said call it in, put a cordon on the stairwell, wait for the sun. Her radio was soaked dead anyway. Her backup was a thirty-minute drive away and a two-minute, forty-second response time behind that. Below her, Herrera paused on the landing, looked back up, and — this was the strange part — waited. As if he were checking she was still coming. As if the running had been for someone else's benefit. Then the lights below him came on, and they weren't electric. They were a warm amber, lamplight amber, and they lit a tunnel that hadn't been in the survey of Camden Underground she'd memorized on the drive over. A faded roundel was bolted to the wall, the old station name worn to ghost letters. Abandoned Tube station. She knew this stretch. Nobody had used it since the seventies. And yet the tunnel breathed. She could feel it — a draft rising warm off the stairs, carrying the smell of candle wax and cut herbs and something animal underneath, something like a livestock market on a summer day. Herrera disappeared around the curve of the tunnel. Quinn took her soaked jacket off, wrung it once with quick, hard motions, and put it back on. Her left hand checked the cuff of the watch Morris had given her — you'll need to know when the night ends, Harl — and the worn leather was slick with rain but the watch still ticked. Small mercies. She drew her baton, thought about the pistol on her ankle, and left it where it was. If the Metropolitan Police had jurisdiction down there, she was a fool. If they didn't, the pistol was a worse decoration. "Right," she said to nobody. "But it's your funeral." She took the stairs. The tunnel floor was slick but not with water. The walls had been curtained, in places — heavy drapes hung between the old advertising frames, and behind the gaps she saw stalls. Real stalls. A woman with too many joints in her fingers was weighing something silver into a brass scale. A man in a good suit was buying something that moved in its jar. Nobody looked at her, which was worse than if they'd all stared. Two figures in dark coats by a brazier did turn, and one of them held up a small object toward her — a token, white and carved, some kind of bone. She shook her head. The figure shrugged and let her pass. Entry fee unpaid, apparently, and still admitted. Interesting. She filed it away with the rest of the night's evidence, which was accumulating fast and making less sense with each item. The market opened into the old platform hall, and it was vast — a bazaar built into a cathedral of Edwardian tile and iron, lamps strung on wires between pillars, hundreds of people and people-shaped things trading in low voices. Somewhere a musician played a fiddle. Somewhere something laughed with a sound like grinding stone. She found Herrera at the far end, at a stall draped in green cloth, talking low and urgent to a figure she couldn't see. He was holding out cash. He was also, she noticed, holding out his left arm, sleeve rolled back, the long knife scar along his forearm livid under the lamplight — and the figure was examining it. Not treating it. Examining it. Like evidence. Quinn moved along the shadows of the pillars, closing the distance. Fifty feet. Forty. Herrera's voice carried in fragments over the noise — "...the same pattern... three years... it's not healing wrong, it's healing true—" And then he stopped talking, and turned his head, and looked directly at her. He wasn't surprised. That was the thing that would keep her up tomorrow, if she survived to have a tomorrow. He looked tired and sorry, like a doctor delivering a bad result. "Detective Quinn," he said. "You should have shown me the watch at the top of the stairs. Would have saved us both the run." His warm brown eyes went to her left wrist, to Morris's watch, and something in his face collapsed gently inward. "It's still ticking. Good. That matters more than you know." "Hands where I can see them," she said, baton up, pulse hammering. "You're coming with me." "No," Herrera said, not unkindly. "But I'll walk you to the person who can explain it. The watch. Morris. All of it." He rolled his sleeve down and held out his hand across the green cloth. "You've been chasing the wrong thing for three years, Detective. I'm the medic. You want the disease." Around them the market had gone quiet, hundreds of eyes she couldn't see all pointed her way, and the lamplight guttered once, all down the hall, like the whole place drawing breath. Quinn looked at the watch. It ticked. She holstered the baton and took his hand.
created_at"2026-09-02T13:37:50.13Z" 9/2/2026, 1:37:50 PM
model"z-ai/glm-5.3-flash-20260826"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency1435
moderation_latency(null)
generation_time55726
tokens_prompt888
tokens_completion1697
native_tokens_prompt819
native_tokens_completion1499
native_tokens_completion_images(null)
native_tokens_reasoning4
native_tokens_cached128
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(null)
usage0.000428495
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.19.0; linux; x64))"
http_referer(null)
request_id"req-1788356270-kNaUT3zK5UNXseDh78Wv"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1788356270-aezMsP6dXFR3FuvvTaT7"
upstream_id"20260902213750c6547254d90f4db4"
provider_responses
0
endpoint_id"8e9fe48b-2f91-41c3-a8a7-e4a93a8c4ff0"
id"20260902213750c6547254d90f4db4"
is_byokfalse
latency1435
model_permaslug"z-ai/glm-5.3-flash-20260826"
provider_name"Z.AI"
status200
total_cost0.000428495
cache_discount0.00001536
upstream_inference_cost0
provider_name"Z.AI"
response_cache_source_id(null)
data_region"global"
workspace_id"97e315e5-d303-487d-83c1-83180e8a13d4"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags8
adverbTagCount0
adverbTags(empty)
dialogueSentences13
tagDensity0.615
leniency1
rawRatio0
effectiveRatio0
95.79% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1189
totalAiIsmAdverbs1
found
0
adverb"gently"
count1
highlights
0"gently"
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)
87.38% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1189
totalAiIsms3
found
0
word"electric"
count1
1
word"could feel"
count1
2
word"pulse"
count1
highlights
0"electric"
1"could feel"
2"pulse"
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
narrationSentences82
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount0
narrationSentences82
filterMatches
0"know"
1"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences87
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen41
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1201
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions6
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions29
wordCount1098
uniqueNames16
maxNameDensity0.73
worstName"Herrera"
maxWindowNameDensity1.5
worstWindowName"Herrera"
discoveredNames
Soho1
Tomás1
Herrera8
Saint1
Christopher1
Morris3
Greek1
Street1
Camden1
Underground1
Tube1
Harl1
Metropolitan1
Police1
Edwardian1
Quinn5
persons
0"Tomás"
1"Herrera"
2"Saint"
3"Christopher"
4"Morris"
5"Police"
6"Quinn"
places
0"Soho"
1"Greek"
2"Street"
globalScore1
windowScore1
65.25% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences59
glossingSentenceCount2
matches
0"something like a livestock market on a summe"
1"ry fee unpaid, apparently, and still admitted"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches1
per1kWords0.833
wordCount1201
matches
0"not by watching the runner but by watching what the runner watched"
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences87
matches
0"finding that penny"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs28
mean42.89
std29.62
cv0.69
sampleLengths
026
182
215
390
463
567
68
767
838
968
1039
118
1295
139
144
15108
1641
1755
1860
197
2049
2132
2254
2316
2453
2532
267
278
92.43% Passive voice overuse
Target: ≤2% passive sentences
passiveCount3
totalSentences82
matches
0"been taught"
1"was bolted"
2"been curtained"
0.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount7
totalVerbs182
matches
0"were checking"
1"was still coming"
2"was weighing"
3"was buying"
4"was accumulating"
5"was holding"
6"was examining"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount12
semicolonCount0
flaggedSentences10
totalSentences87
ratio0.115
matches
0"Quinn kept him in sight the way she'd been taught eighteen years ago — not by watching the runner but by watching what the runner watched."
1"She'd seen him twice before, both times at the periphery of cases that had ended badly — cases that ended with DS Morris's name on a plaque in the lobby and no body and no answers."
2"Below her, Herrera paused on the landing, looked back up, and — this was the strange part — waited."
3"She could feel it — a draft rising warm off the stairs, carrying the smell of candle wax and cut herbs and something animal underneath, something like a livestock market on a summer day."
4"Her left hand checked the cuff of the watch Morris had given her — you'll need to know when the night ends, Harl — and the worn leather was slick with rain but the watch still ticked."
5"The walls had been curtained, in places — heavy drapes hung between the old advertising frames, and behind the gaps she saw stalls."
6"Two figures in dark coats by a brazier did turn, and one of them held up a small object toward her — a token, white and carved, some kind of bone."
7"The market opened into the old platform hall, and it was vast — a bazaar built into a cathedral of Edwardian tile and iron, lamps strung on wires between pillars, hundreds of people and people-shaped things trading in low voices."
8"He was also, she noticed, holding out his left arm, sleeve rolled back, the long knife scar along his forearm livid under the lamplight — and the figure was examining it."
9"Herrera's voice carried in fragments over the noise — \"...the same pattern... three years... it's not healing wrong, it's healing true—\" And then he stopped talking, and turned his head, and looked directly at her."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1090
adjectiveStacks0
stackExamples(empty)
adverbCount32
adverbRatio0.029357798165137616
lyAdverbCount5
lyAdverbRatio0.0045871559633027525
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences87
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences87
mean13.8
std11.04
cv0.8
sampleLengths
026
126
26
312
438
57
68
73
84
923
1036
1116
128
1323
1427
1513
1641
177
182
1917
208
2118
2210
2318
246
2515
2619
279
2810
2911
3027
3116
323
334
347
355
3634
378
3818
3937
402
4117
4212
439
445
454
464
479
4823
492
67.43% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats8
diversityRatio0.45977011494252873
totalSentences87
uniqueOpeners40
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount4
totalSentences72
matches
0"Of course he didn't."
1"Then the lights below him"
2"Somewhere a musician played a"
3"Somewhere something laughed with a"
ratio0.056
53.33% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount30
totalSentences72
matches
0"He checked left before the"
1"He didn't check the right,"
2"Her voice cracked against the"
3"He didn't slow."
4"She'd seen him twice before,"
5"She'd spent two years finding"
6"He cut across Greek Street,"
7"She'd stopped believing that last"
8"She hit the gate with"
9"It was the oldest rule"
10"Her radio was soaked dead"
11"Her backup was a thirty-minute"
12"They were a warm amber,"
13"She knew this stretch."
14"She could feel it —"
15"Her left hand checked the"
16"She drew her baton, thought"
17"she said to nobody"
18"She took the stairs."
19"She shook her head."
ratio0.417
78.06% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount55
totalSentences72
matches
0"The rain came down in"
1"Quinn kept him in sight"
2"He checked left before the"
3"He didn't check the right,"
4"That told her he'd planned"
5"Her voice cracked against the"
6"He didn't slow."
7"Nobody with a Saint Christopher"
8"She'd seen him twice before,"
9"Herrera's face had been in"
10"She'd spent two years finding"
11"He cut across Greek Street,"
12"The city was empty at"
13"She'd stopped believing that last"
14"Herrera ducked down a service"
15"She hit the gate with"
16"Quinn stopped at the top"
17"It was the oldest rule"
18"Her radio was soaked dead"
19"Her backup was a thirty-minute"
ratio0.764
100.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount4
totalSentences72
matches
0"As if he were checking"
1"As if the running had"
2"If the Metropolitan Police had"
3"If they didn't, the pistol"
ratio0.056
89.29% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences40
technicalSentenceCount3
matches
0"The rain came down in sheets over Soho, turning the neon to smeared watercolor, and Tomás Herrera ran like a man who had done this before."
1"The city was empty at this hour except for the drunk and the desperate and the ones who worked the dark hours because daylight would kill them."
2"They were a warm amber, lamplight amber, and they lit a tunnel that hadn't been in the survey of Camden Underground she'd memorized on the drive over."
62.50% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags8
uselessAdditionCount1
matches
0"Herrera said, not unkindly"
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags4
fancyCount0
fancyTags(empty)
dialogueSentences13
tagDensity0.308
leniency0.615
rawRatio0
effectiveRatio0
86.3822%