Run: 2026-06-24-01KVX2J5V0TB8KVM8YFMQJAB8D

Run Details

Duration:
57.0s
Cost:
$0.007969
Provider:
Z.AI

Model input

System
You are an expert fiction writer. Always keep the following rules in mind: - Write in past tense and use General English spelling, grammar, and colloquialisms/slang. - Write in active voice - Always follow the "show, don't tell" principle. - Avoid adverbs and cliches and overused/commonly used phrases. Aim for fresh and original descriptions. - Convey events and story through dialogue. - Mix short, punchy sentences with long, descriptive ones. Drop fill words to add variety. - Skip "he/she said said" dialogue tags and convey people's actions or face expressions through their speech - Avoid mushy dialog and descriptions, have dialogue always continue the action, never stall or add unnecessary fluff. Vary the descriptions to not repeat yourself. - Put dialogue on its own paragraph to separate scene and action. - Reduce indicators of uncertainty like "trying" or "maybe" When writing text: - NEVER conclude the scene on your own, follow the beat instructions very closely. - NEVER end with foreshadowing. - NEVER write further than what I prompt you with. - AVOID imagining possible endings, NEVER deviate from the instructions. - STOP EARLY if the continuation contains what was required in the instructions. You do not need to fill out the full amount of words possible.
User/Human
Take into account the following glossary of characters/locations/items/lore... when writing your response: <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> Continue the story using the following instructions: <instructions> 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. </instructions>

Model response

AI
Rain hammered the pavement in sheets, turning Soho's greasy puddles into shattered mirrors that caught the neon and threw it back in ugly fragments. Harlow Quinn's boots struck the cobblestones in a rhythm she knew from a hundred foot pursuits — measured, relentless, the kind of pace that broke men who thought they could sprint forever. The figure ahead of her dodged past a cluster of umbrellas outside a chip shop, close enough that she caught the glint of a Saint Christopher medallion swinging from his neck. He moved with a strange confidence for someone being hunted, weaving between bodies and evening commuters like he'd memorised every obstacle in his path. His dark curls plastered to his forehead. Olive skin gone pale under the streetlights. "Herrera!" He didn't turn. Didn't slow. Instead, he cut left down a service alley, vaulting a stack of broken pallets with the kind of fluid grace that told her he'd done this before. Quinn followed, her worn leather watch catching on a nail as she squeezed past the same pallets. She didn't stop to check the scratch. The alley opened onto a parallel street, quieter, lined with shuttered storefronts. Herrera was twenty metres ahead and pulling away. His jacket flared behind him — a paramedic's jacket, navy blue, faded at the shoulders. She'd seen him twice before. Once outside The Raven's Nest, that dim bar with the green neon sign where her surveillances kept leading her. Once at St. Thomas' Hospital, three weeks ago, when a patient with injuries no surgeon could explain had checked out against medical advice at two in the morning. Herrera had been the one to sign the discharge papers. Except Herrera wasn't on staff at St. Thomas'. Herrera wasn't on staff anywhere. Not since the NHS stripped his license for administering what the disciplinary report called "unauthorized and unverified treatments" to patients whose files had since been sealed. Quinn's lungs burned. She pushed harder. He disappeared around another corner. When she reached it, she stopped. The street ended at a construction barrier, orange mesh fluttering in the wind. Beyond it, a chain-link fence with a gap cut at the bottom, and beyond that, the mouth of an abandoned Tube station. The entrance gaped like a wound in the earth, steps descending into darkness. A faded roundel hung at an angle above the stairs, the station name too weathered to read. She'd run this route in her head a dozen times during the Morris investigation. The abandoned stations beneath Camden were a maze — service tunnels, old platforms, sealed corridors that went God knew where. Three years ago, Morris had followed a lead into one of them. The last thing he'd said on the phone was that he'd found something. A market, he'd called it. A place where things were sold that shouldn't exist. They'd found his body two days later in the Thames. No water in his lungs. No marks on his skin. His watch had stopped at 11:47, though the coroner placed time of death somewhere between 3 and 5 a.m. Quinn stared down the steps. The rain had slicked them to black ice. Somewhere below, a light flickered — warm, amber, nothing like the cold fluorescent of maintenance crews. She pulled her phone. No signal. Of course. The thought of backup was a luxury she'd abandoned weeks ago. Her inspector thought she was obsessed. Her colleagues exchanged looks when she mentioned the Raven's Nest or the names in Morris's case file. She was two weeks from a mandatory psych evaluation and one missed appointment from losing her badge. Herrera was somewhere down there. So were answers. She drew her baton but left the holster latched. The weight of the ASP in her hand was grounding as she took the first step down. The descent took longer than it should have. The steps spiral-languaged in a way that didn't match London's standard station architecture, and the walls on either side shifted from tiled Victorian to rough-hewn brick to something older — stone blocks fitted together without mortar, their surfaces carved with symbols she didn't recognise. The amber glow grew stronger, and with it came sound: low murmurs, the clink of metal, a distant hum that vibrated in her molars. The stairs leveled out onto a platform. Quinn pressed herself against the wall and looked. The station had been transformed. Stalls lined both platforms, their awnings made from materials that moved wrong — fabric that rippled without wind, leather that seemed to breathe. Candles floated above vendor tables, flames burning in colours she didn't have names for. The air smelled of copper and burnt sage and something sweet that made her jaw ache. People moved between the stalls. Some of them looked human. Some of them didn't. A woman with too many fingers examined a row of glass bottles, each one containing a liquid of a different, impossible shade. A figure in a heavy cloak haggled with a vendor whose face was obscured by a mask of bone. Two children — if they were children — chased each other between the stalls, their laughter high and wrong, like wind through cracked glass. Herrera stood at a stall near the far end, his back to her. He was talking to the vendor, gesturing with both hands. The medallion caught the candlelight as he moved. Quinn counted the exits. The tunnel to her left stretched into darkness. The tunnel to her right had been blocked with a barrier of interwoven branches that were still growing, she realised, their tips unfurling in slow motion. The platform across the tracks connected to a corridor lit by phosphorescent moss. She could go back. Climb the stairs, wait at the entrance, catch Herrera when he emerged. That was the smart play. The tactical play. The kind of thing Morris would have done before whatever happened to him down here. But Morris had waited. And Morris was dead. A vendor three stalls down turned to look at her. Its eyes — if they were eyes — were solid black, no whites, no iris. It opened its mouth, and the sound that came out was a language that made her teeth vibrate. The stall closest to her sold tokens. Small, white, carved from bone. The vendor — an elderly man with papery skin and cloudy eyes — held one out to her between two fingers. "Entry fee," he said. His voice was dry as paper too. "You crossed without one. They'll notice." Quinn looked at the bone token. She looked at Herrera, still at the far stall, still unaware. She looked at the vendor with the black eyes, who hadn't stopped watching her. The old man pressed the token into her palm. It was warm, like it had been held against living skin. "On the house," he said. "Detective." Her fingers closed around it.
created_at"2026-06-24T15:04:17.516Z" 6/24/2026, 3:04:17 PM
model"z-ai/glm-5.2-20260616"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency5236
moderation_latency(null)
generation_time56946
tokens_prompt1140
tokens_completion1805
native_tokens_prompt997
native_tokens_completion1494
native_tokens_completion_images(null)
native_tokens_reasoning65
native_tokens_cached0
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)
usage0.0079694
router(null)
provider_responses
0
endpoint_id"442ea97f-ad5e-40d5-b9a6-66e9e0417dce"
id"202606242304186197f8bd48ea42cb"
is_byokfalse
latency5236
model_permaslug"z-ai/glm-5.2-20260616"
provider_name"Z.AI"
status200
user_agent"langchainjs-openai/1.0.0 ((node/v24.16.0; linux; x64))"
http_referer(null)
request_id"req-1782313457-6rnfhsDJsNApW7RNYZHY"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1782313457-MY9kQVmQHceNnBSPikaV"
upstream_id"202606242304186197f8bd48ea42cb"
total_cost0.0079694
cache_discount(null)
upstream_inference_cost0
provider_name"Z.AI"
response_cache_source_id(null)
data_region"global"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags3
adverbTagCount0
adverbTags(empty)
dialogueSentences6
tagDensity0.5
leniency1
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1134
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)
64.73% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1134
totalAiIsms8
found
0
word"shattered"
count1
1
word"measured"
count1
2
word"glint"
count1
3
word"the last thing"
count1
4
word"flickered"
count1
5
word"weight"
count1
6
word"standard"
count1
7
word"vibrated"
count1
highlights
0"shattered"
1"measured"
2"glint"
3"the last thing"
4"flickered"
5"weight"
6"standard"
7"vibrated"
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
narrationSentences97
matches(empty)
83.95% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount2
narrationSentences97
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)
analyzedSentences102
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen45
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1146
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions8
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions36
wordCount1103
uniqueNames15
maxNameDensity0.73
worstName"Herrera"
maxWindowNameDensity2
worstWindowName"Herrera"
discoveredNames
Soho1
Quinn7
Saint1
Christopher1
Raven2
Nest2
St2
Herrera8
Tube1
Morris6
Camden1
God1
Thames1
London1
Victorian1
persons
0"Quinn"
1"Saint"
2"Christopher"
3"Raven"
4"Herrera"
5"Morris"
6"God"
places
0"Soho"
1"St"
2"Thames"
3"London"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences66
glossingSentenceCount1
matches
0"leather that seemed to breathe"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1146
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences102
matches
0"was that he'd"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs31
mean36.97
std25.09
cv0.679
sampleLengths
056
169
21
356
487
549
66
711
865
973
1039
1129
128
1351
148
1526
1676
1715
1858
1979
2031
2151
2239
238
2443
2533
2617
2731
2820
296
305
76.32% Passive voice overuse
Target: ≤2% passive sentences
passiveCount8
totalSentences97
matches
0"being hunted"
1"been sealed"
2"were sold"
3"was obsessed"
4"been transformed"
5"was obscured"
6"been blocked"
7"been held"
89.50% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount3
totalVerbs181
matches
0"was grounding"
1"was talking"
2"were still growing"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount12
semicolonCount0
flaggedSentences9
totalSentences102
ratio0.088
matches
0"Harlow Quinn's boots struck the cobblestones in a rhythm she knew from a hundred foot pursuits — measured, relentless, the kind of pace that broke men who thought they could sprint forever."
1"His jacket flared behind him — a paramedic's jacket, navy blue, faded at the shoulders."
2"The abandoned stations beneath Camden were a maze — service tunnels, old platforms, sealed corridors that went God knew where."
3"Somewhere below, a light flickered — warm, amber, nothing like the cold fluorescent of maintenance crews."
4"The steps spiral-languaged in a way that didn't match London's standard station architecture, and the walls on either side shifted from tiled Victorian to rough-hewn brick to something older — stone blocks fitted together without mortar, their surfaces carved with symbols she didn't recognise."
5"Stalls lined both platforms, their awnings made from materials that moved wrong — fabric that rippled without wind, leather that seemed to breathe."
6"Two children — if they were children — chased each other between the stalls, their laughter high and wrong, like wind through cracked glass."
7"Its eyes — if they were eyes — were solid black, no whites, no iris."
8"The vendor — an elderly man with papery skin and cloudy eyes — held one out to her between two fingers."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount248
adjectiveStacks0
stackExamples(empty)
adverbCount8
adverbRatio0.03225806451612903
lyAdverbCount1
lyAdverbRatio0.004032258064516129
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences102
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences102
mean11.24
std8.12
cv0.723
sampleLengths
024
132
231
324
47
57
61
73
82
927
1017
117
1212
138
1415
155
1619
173
1825
1910
207
211
225
2326
243
253
265
276
2813
2922
3013
3117
3214
3320
3412
3513
365
379
3810
395
405
4119
425
438
4416
454
462
472
4811
496
53.27% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats14
diversityRatio0.4019607843137255
totalSentences102
uniqueOpeners41
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount4
totalSentences92
matches
0"Instead, he cut left down"
1"Once outside The Raven's Nest,"
2"Once at St."
3"Somewhere below, a light flickered"
ratio0.043
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount25
totalSentences92
matches
0"He moved with a strange"
1"His dark curls plastered to"
2"He didn't turn."
3"She didn't stop to check"
4"His jacket flared behind him"
5"She'd seen him twice before."
6"She pushed harder."
7"He disappeared around another corner."
8"She'd run this route in"
9"They'd found his body two"
10"His watch had stopped at"
11"She pulled her phone."
12"Her inspector thought she was"
13"Her colleagues exchanged looks when"
14"She was two weeks from"
15"She drew her baton but"
16"He was talking to the"
17"She could go back."
18"Its eyes — if they"
19"It opened its mouth, and"
ratio0.272
57.83% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount74
totalSentences92
matches
0"Harlow Quinn's boots struck the"
1"The figure ahead of her"
2"He moved with a strange"
3"His dark curls plastered to"
4"Olive skin gone pale under"
5"He didn't turn."
6"Quinn followed, her worn leather"
7"She didn't stop to check"
8"The alley opened onto a"
9"Herrera was twenty metres ahead"
10"His jacket flared behind him"
11"She'd seen him twice before."
12"Herrera had been the one"
13"Herrera wasn't on staff anywhere."
14"Quinn's lungs burned."
15"She pushed harder."
16"He disappeared around another corner."
17"The street ended at a"
18"The entrance gaped like a"
19"A faded roundel hung at"
ratio0.804
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences92
matches(empty)
ratio0
65.22% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences46
technicalSentenceCount5
matches
0"Rain hammered the pavement in sheets, turning Soho's greasy puddles into shattered mirrors that caught the neon and threw it back in ugly fragments."
1"The abandoned stations beneath Camden were a maze — service tunnels, old platforms, sealed corridors that went God knew where."
2"The steps spiral-languaged in a way that didn't match London's standard station architecture, and the walls on either side shifted from tiled Victorian to rough…"
3"The tunnel to her right had been blocked with a barrier of interwoven branches that were still growing, she realised, their tips unfurling in slow motion."
4"It opened its mouth, and the sound that came out was a language that made her teeth vibrate."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags3
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags2
fancyCount0
fancyTags(empty)
dialogueSentences6
tagDensity0.333
leniency0.667
rawRatio0
effectiveRatio0
86.3604%