Run: 2026-09-02-01M1GWFQPTTJAWCFHMGFBJWG0K

Run Details

Duration:
8m 37s
Cost:
$0.055103
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 came off the Camden rooftops in ropes, and Detective Harlow Quinn stood in it, because dry hiding places got you made. Number 41 had swallowed Tomás Herrera fifty minutes ago. He came out now with the canvas bag riding his shoulder, hood up, and walked toward the High Street without locking the door behind him. People afraid of arrest locked doors. People afraid of something else couldn't be bothered. She gave him half a street and matched his pace, shoulders square under the soaked waxed jacket. Three nights she'd tailed him — a basement in Kentish Town, a lockup in Islington, tonight Camden — and in all that time he had never once looked back. Civilians looked back. Civilians fussed with their phones and doubled back on themselves. Herrera walked like a man who counted every footstep behind him and had already decided none of them mattered. At the cash machine outside the shuttered pub, he stopped. The screen lit his face in the glass, and she watched him do the arithmetic: his reflection, the street, the woman forty feet back with rain bleeding off her cropped hair. He cut across the road between two night buses. "Herrera!" She held up her warrant card, rain crawling across the laminate. "Police! Stop where you are!" He gave her one look over his shoulder. The Saint Christopher medallion jumped against his chest. "You've got nothing on me, Detective!" "I've got you running!" He ran. Down the High Street, past a kebab shop bleeding steam onto the pavement, past a hen party that shrieked as he took their table's edge in his palm and vaulted it. He rounded the corner by the off-license with one hand on a lamppost, whipping himself around it. Quinn followed. Eighteen years on the job and she could still hold a sprint, but he was twenty-nine and had spent his working life hauling bodies down stairwells; he ran like a man who knew exactly how much body he had and what it could pay. The canal bought her half a second. He took the lock footbridge steps three at a time, sleeve raked back, the old knife scar along his forearm gone white under a lamp. Below them the water lay black and shivering. "Tomás!" Her voice tore on his name. "I want to talk!" "You picked a bad night for talking!" He cut through a gap in the hoarding into the dead market — shuttered stalls, awnings gulping rain, the iron horse standing in the dark with water running off its mane. No one was coming behind her. She'd logged off at eleven, and the only person who knew she was in Camden was a bartender who thought she'd gone home. Behind a row of dumpsters, one railway arch sat darker than its brothers. A steel door, gray on gray, looked welded shut. Herrera dug something pale from his jacket and rapped the steel with it — twice, a pause, three — and the door gave like it had been unlatched all along. He looked back at her once. The look paramedics gave people they couldn't save. Then he went in, and the door drifted toward its frame on a breath of warm air that smelled of candle smoke and copper. Quinn caught it with her palm flat on the wet steel. No backup. No cameras. No one who would ever ask her about this door. In eighteen years, everything she had ever chased had stopped in the end — leaned on a wall, dropped the bag, cried, bargained. This was the first thing she'd chased that had somewhere to go. That alone made it worth following. She slipped through before the door could decide for her. A stairwell of green glazed tile went down, one bulb in five alive, all of them the amber of old teeth. Decades of paint ghosted over a platform sign — WAY OUT, with an arrow pointing up at a city that had forgotten this hole existed. Chalked on the tiles ran a row of moons, three crossed out. Warm air climbed past her, tasting of hot fat, smoke, old stone, and from far below came a sound like a hive that had moved into a cathedral: a hundred voices, none of them raised. She went down. At the foot of the stairs, a brazier burned in a cut-down oil drum. Beside it, on a stool, an old woman knitted with needles as long as her forearms and yellowed like piano keys. The wool was the color of ditch water. Ahead, Herrera's silhouette crossed toward an archway strung with a curtain of threaded bones. The curtain clicked around him and swallowed him. The needles never paused. A pale disc arced from his hand into the old woman's apron. "Token." She rolled it under her thumb the way jewelers handled stones, then dropped it in with a pocketful of knuckle-sized discs. "Mind the wax, love. It's everywhere tonight." Quinn stopped in the dark at the bottom step and let the warmth move past her. Three years ago she had chased a man through a door like this. Bermondsey. Boarded-up, breathing cold. A basement, and Morris two steps behind her torch, the way he'd been for nine years. The coroner's report had described injuries that matched no weapon anyone could name. The file had gone into a cabinet that needed two keys, and no one had ever given her either one. Since then she had learned to eat the not-knowing — three meals a day, no relief between them. Morris would have told her to wait for backup. Morris wasn't here to tell her anything. That was the whole of it. She stepped out onto the platform. The needles stopped. "Token." "I'm not carrying one." "Then carry yourself back up the stairs. They're free." The old woman's eyes came up, the gray of wet slate. "Everything past me isn't." Quinn unbuckled the watch — the leather strap worn to a shine, creased by another wrist before hers — and set it in the crone's palm. The old woman turned it over, thumbnail tapping the glass. "Steel and dead skin. There's no bone in this." "Whose was it?" "A dead man's." The crone weighed it on her palm, and weighed Quinn in the same motion. "Closer to bone than most of what your lot carries." It dropped into the apron among the discs, landing without a sound. "Mind what you touch down there. Nothing is only what it looks." She knocked one needle against the drum, twice, and somewhere behind the curtain a chain ran through a pulley. The bones parted on their own, and heat rolled out — candle stubs burning in jam jars on trestles, sweet smoke, the clink of coin and glass, stalls climbing away into the dark of the old tunnel in both directions. Quinn walked through. The bones clicked around her shoulders like cold rain. Somewhere in the crush, a canvas bag rode a shoulder between two candlelit stalls. She kept it in sight and followed.
created_at"2026-09-02T11:01:23.811Z" 9/2/2026, 11:01:23 AM
model"z-ai/glm-5.3-20260816"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency3127
moderation_latency(null)
generation_time516943
tokens_prompt1104
tokens_completion13690
native_tokens_prompt997
native_tokens_completion12455
native_tokens_completion_images(null)
native_tokens_reasoning10985
native_tokens_cached960
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)
usage0.0551034
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.19.0; linux; x64))"
http_referer(null)
request_id"req-1788346883-ROSZqRMcex9jJc9PZR7r"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1788346883-AqkNsn4jnuTneHwyaFxR"
upstream_id"20260902190123cce22a2919ea4db6"
provider_responses
0
endpoint_id"db975786-e7f6-4838-9e56-3e0b77f1bdc2"
id"20260902190123cce22a2919ea4db6"
is_byokfalse
latency3127
model_permaslug"z-ai/glm-5.3-20260816"
provider_name"Z.AI"
status200
total_cost0.0551034
cache_discount0.0010944
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
totalTags5
adverbTagCount0
adverbTags(empty)
dialogueSentences18
tagDensity0.278
leniency0.556
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1167
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)
95.72% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1167
totalAiIsms1
found
0
word"warmth"
count1
highlights
0"warmth"
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
narrationSentences79
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences79
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences92
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
totalWords1178
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions3
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions29
wordCount1087
uniqueNames14
maxNameDensity0.64
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Quinn"
discoveredNames
Camden3
Detective1
Harlow1
Quinn7
Tomás1
Herrera4
High2
Street2
Kentish1
Town1
Islington1
Saint1
Christopher1
Morris3
persons
0"Harlow"
1"Quinn"
2"Tomás"
3"Herrera"
4"Saint"
5"Christopher"
6"Morris"
places
0"Camden"
1"High"
2"Street"
3"Kentish"
4"Town"
5"Islington"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences68
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1178
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences92
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs40
mean29.45
std26.63
cv0.904
sampleLengths
022
148
278
341
49
517
616
76
84
996
1040
1111
127
1360
1452
1538
1611
1755
1810
1993
203
2165
2216
2329
2416
2584
2622
276
283
291
304
3124
3226
3319
343
353
3648
3759
3812
3921
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences79
matches
0"been unlatched"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs172
matches
0"was coming"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount11
semicolonCount1
flaggedSentences9
totalSentences92
ratio0.098
matches
0"Three nights she'd tailed him — a basement in Kentish Town, a lockup in Islington, tonight Camden — and in all that time he had never once looked back."
1"Eighteen years on the job and she could still hold a sprint, but he was twenty-nine and had spent his working life hauling bodies down stairwells; he ran like a man who knew exactly how much body he had and what it could pay."
2"He cut through a gap in the hoarding into the dead market — shuttered stalls, awnings gulping rain, the iron horse standing in the dark with water running off its mane."
3"Herrera dug something pale from his jacket and rapped the steel with it — twice, a pause, three — and the door gave like it had been unlatched all along."
4"In eighteen years, everything she had ever chased had stopped in the end — leaned on a wall, dropped the bag, cried, bargained."
5"Decades of paint ghosted over a platform sign — WAY OUT, with an arrow pointing up at a city that had forgotten this hole existed."
6"Since then she had learned to eat the not-knowing — three meals a day, no relief between them."
7"Quinn unbuckled the watch — the leather strap worn to a shine, creased by another wrist before hers — and set it in the crone's palm."
8"The bones parted on their own, and heat rolled out — candle stubs burning in jam jars on trestles, sweet smoke, the clink of coin and glass, stalls climbing away into the dark of the old tunnel in both directions."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1082
adjectiveStacks0
stackExamples(empty)
adverbCount26
adverbRatio0.024029574861367836
lyAdverbCount2
lyAdverbRatio0.0018484288354898336
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences92
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences92
mean12.8
std9.36
cv0.731
sampleLengths
022
19
225
36
48
517
629
73
810
919
1010
1131
129
1312
145
158
168
176
184
192
2031
2117
222
2344
247
2525
268
277
284
297
3031
316
3223
3313
349
3530
366
378
3824
3911
402
412
4210
4323
4412
456
4610
4721
4825
4912
66.67% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats7
diversityRatio0.44565217391304346
totalSentences92
uniqueOpeners41
90.09% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences74
matches
0"Then he went in, and"
1"Somewhere in the crush, a"
ratio0.027
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount18
totalSentences74
matches
0"He came out now with"
1"She gave him half a"
2"He cut across the road"
3"She held up her warrant"
4"He gave her one look"
5"He rounded the corner by"
6"He took the lock footbridge"
7"Her voice tore on his"
8"He cut through a gap"
9"She'd logged off at eleven,"
10"He looked back at her"
11"She slipped through before the"
12"She went down."
13"She rolled it under her"
14"She stepped out onto the"
15"It dropped into the apron"
16"She knocked one needle against"
17"She kept it in sight"
ratio0.243
88.38% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount55
totalSentences74
matches
0"Rain came off the Camden"
1"Number 41 had swallowed Tomás"
2"He came out now with"
3"People afraid of arrest locked"
4"People afraid of something else"
5"She gave him half a"
6"Civilians looked back."
7"Civilians fussed with their phones"
8"Herrera walked like a man"
9"The screen lit his face"
10"He cut across the road"
11"She held up her warrant"
12"He gave her one look"
13"The Saint Christopher medallion jumped"
14"He rounded the corner by"
15"The canal bought her half"
16"He took the lock footbridge"
17"Her voice tore on his"
18"He cut through a gap"
19"She'd logged off at eleven,"
ratio0.743
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences74
matches(empty)
ratio0
91.84% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences42
technicalSentenceCount3
matches
0"Down the High Street, past a kebab shop bleeding steam onto the pavement, past a hen party that shrieked as he took their table's edge in his palm and vaulted i…"
1"Eighteen years on the job and she could still hold a sprint, but he was twenty-nine and had spent his working life hauling bodies down stairwells; he ran like a…"
2"Warm air climbed past her, tasting of hot fat, smoke, old stone, and from far below came a sound like a hive that had moved into a cathedral: a hundred voices, …"
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags5
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags0
91.0896%