Run: 2026-09-02-01M1GSDKD9702MVG41Q47XX8H2

Run Details

Duration:
5m 3s
Cost:
$0.057016
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
The green neon above the Raven's Nest ran down the brickwork and pooled in the cracks of the pavement. Rain fell fine and sideways, the kind that found the seams in everything, and Quinn's coat had given up its waterproofing an hour ago. She stood in a doorway opposite the bar and counted the exits for the fourth time. Herrera came out at quarter past one. Duffel bag over one shoulder, hood up, walking like a man with somewhere to be who refused to look like it. Under the sign, the Saint Christopher at his throat caught the neon. She gave him half a street. Two weeks she'd sat on him. An ex-paramedic with a dead license and a living cash flow — suture kits, blood-line sets, coagulant, all of it paid for in folded notes, none of it ever reaching a hospital. He was the loose thread. Pull Tommy Herrera and the whole clique unraveled. That was the theory she'd told no one, because on paper she was home, asleep. He turned off Wardour Street. She crossed after him, soles hissing on wet stone, and caught the moment he made her: a stationery shop's window, her reflection arriving beside his, half a second of stillness in a man who had been all motion. Then he ran. He ran like a medic, no panic in it, all economy — straight lines, early turns, nothing wasted. She ran the way the army had built her, shoulders square, corners wide, eyes up. He shoved a delivery moped into her path and she went over the saddle, hip clipping a wing mirror. A cyclist swore. A bin went over somewhere behind her. On Old Compton Street she shouted it — police, stop him — and two men under an awning watched him pass with the flat interest of people who had decided years ago that nothing was their business. He cut left onto Charing Cross Road against the lights, and the blue roundel of Tottenham Court Road glowed up ahead through the weather. He skipped the escalator and took the stairs three at a time. At the barriers she held her warrant card up for an attendant who was already stepping aside, then jumped the gate after Herrera anyway, because the card was for later and the man was for now. The platform held four people and none of them were him until the train slid in with a shriek of wet rail. Hood back now, curls plastered dark against his skull, breathing through his nose like a man out on a jog. He boarded at the far door. She caught the near doors on the chime and they thumped shut on her sleeve. The carriage held two drinkers sleeping against each other at the far end. She walked its full length and stopped an arm's reach from him. "Whatever you think is in this bag—" "Sit down, Tommy." "It's medical supplies. I'm a medic. Carrying medicine isn't a crime." "Stitching people shut in back rooms is." He touched the medallion and turned to the black window. His sleeve rode up and the old knife scar showed pale along his left forearm. Goodge Street went past as a smear of white tile. "You've been on me two weeks." His reflection spoke to hers in the glass. "The gray car on Lexington. The woman under the bakery awning who never bought bread." She gave him nothing. "You don't want what you think you want, Detective." Euston came and went. Doors opened on nobody. Between stations he stood and gripped the rail against the sway. "You should have stayed in Soho." Camden Town. He was through the doors before they'd finished opening, over the barrier, past the shouting attendant, up into the rain. The High Street ran empty and shining. Across the canal bridge the water lay black and needled, the lock gates streaming. He cut into the Stables Market where the stalls stood shuttered, corrugated fronts drumming, sodium light breaking on wet cobble. His left hand stayed fisted in his jacket pocket, the shape of something small printing through the soaked cloth. The passage he turned down ended at brick with no windows in it, and set into that brick was a door painted the same red as the wall, with the ghost of a roundel still glazed on the tile above. He knocked — two, a pause, three. The door opened inward on lantern light and swallowed him. She hit it with her palm before the latch caught. An old ticket hall, tiled to shoulder height, everything filmed in dust and candle soot. A broad woman in a waxed jacket sat on a folding chair beside a cash box, a storm lantern at her boots, a paperback cracked open across her knee. Past her shoulder, a stairwell dropped out of sight, and warm air rose up it carrying hot copper, incense, and far below, the murmur of a crowd. "Token." Quinn held up the warrant card. "Police." "Never heard of it." The woman turned a page. Herrera stood three steps down, turned back toward her, brown eyes gone amber in the lantern glow. "You kept up." "Give me the bag, Tommy." "It's medical supplies." "For who?" He looked at her for a long moment, then at the gatekeeper, then away. "Go home. Get your warrant. Come back with dogs for all I care — the Market moves at the full moon, and that's tomorrow night. After that, this door opens onto a wall." He climbed one step back up, palms out. "And you don't follow people in there. That's the whole point of there." She keyed her radio and got a hiss of atmosphere and a dispatcher's voice coming apart. The last unmarked door she had stood in front of was a maintenance hatch under the Embankment, three years back. Morris went in first, because he always went in first. His radio carried eleven seconds of him after the dark took it — a count of steps, then a word that wasn't a word, then a silence no report ever found language for. His effects came back to her in a box: his coat, his wedding ring, and one item the property clerk had logged as decorative bone piece, no inventory match. Three winters that piece had ridden her coat pocket. She dug it out now. A domino of yellowed bone with a spiral drilled through it — darker than the one Herrera had shown the door, the spiral worn shallow, one corner snapped off. She held it out. The gatekeeper took it, held it to the lantern, turned it over twice. The paperback closed on her thumb. "Where'd you get this?" "It came back with a dead man's effects." "Whose?" "Sergeant Morris." The woman turned the bone once more, then jerked her chin at the stairs. "They stopped cutting these before my time. Paid is paid, I suppose. Mind your manners down there, love." Herrera came up two steps. His hand closed around the Saint Christopher. "You shouldn't have that, Detective." "Then here's how it goes." He pointed at the stairwell, at her, at the dark below. "You're with me and you're hurt and you don't talk to anyone. That's the only story down there that keeps you breathing. Stay behind me." She stepped past the lantern. The treads were worn into shallow dishes, the rail polished black by decades of hands, and the murmur rose to meet her, threadbare and constant, like weather made of voices. Herrera went first. Quinn closed her fist around the bone and followed him down.
created_at"2026-09-02T10:07:48.147Z" 9/2/2026, 10:07:48 AM
model"z-ai/glm-5.3-20260816"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency7115
moderation_latency(null)
generation_time302296
tokens_prompt1104
tokens_completion14002
native_tokens_prompt997
native_tokens_completion12757
native_tokens_completion_images(null)
native_tokens_reasoning11173
native_tokens_cached448
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)
usage0.05701588
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.19.0; linux; x64))"
http_referer(null)
request_id"req-1788343668-K2PlD9nV6UQ981jHVlkW"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1788343668-ZCW4DAqrDUObjLi6VqPu"
upstream_id"202609021807489d84d970bc484162"
provider_responses
0
endpoint_id"db975786-e7f6-4838-9e56-3e0b77f1bdc2"
id"202609021807489d84d970bc484162"
is_byokfalse
latency7115
model_permaslug"z-ai/glm-5.3-20260816"
provider_name"Z.AI"
status200
total_cost0.05701588
cache_discount0.00051072
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
totalTags4
adverbTagCount0
adverbTags(empty)
dialogueSentences25
tagDensity0.16
leniency0.32
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1261
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)
92.07% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1261
totalAiIsms2
found
0
word"streaming"
count1
1
word"silence"
count1
highlights
0"streaming"
1"silence"
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
narrationSentences75
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences75
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences96
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen40
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1269
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions4
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions35
wordCount1072
uniqueNames21
maxNameDensity0.65
worstName"Herrera"
maxWindowNameDensity1.5
worstWindowName"Herrera"
discoveredNames
Raven1
Nest1
Quinn3
Saint2
Christopher2
Tommy1
Herrera7
Wardour1
Street4
Old1
Compton1
Charing1
Cross1
Road2
Tottenham1
Court1
Town1
High1
Stables1
Market1
Embankment1
persons
0"Raven"
1"Nest"
2"Quinn"
3"Saint"
4"Christopher"
5"Tommy"
6"Herrera"
places
0"Wardour"
1"Street"
2"Old"
3"Compton"
4"Charing"
5"Cross"
6"Road"
7"Tottenham"
8"Court"
9"Town"
10"High"
11"Stables"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences61
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1269
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences96
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs49
mean25.9
std27.54
cv1.063
sampleLengths
059
140
26
366
443
53
699
772
863
925
107
113
1211
137
1435
1529
164
179
1819
196
2022
21100
2217
2310
2471
251
267
279
2817
293
305
313
322
3368
3416
3592
3643
374
3819
394
408
411
422
4332
4412
455
4641
4735
4814
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences75
matches
0"were worn"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs173
matches
0"was already stepping"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount7
semicolonCount0
flaggedSentences6
totalSentences96
ratio0.063
matches
0"An ex-paramedic with a dead license and a living cash flow — suture kits, blood-line sets, coagulant, all of it paid for in folded notes, none of it ever reaching a hospital."
1"He ran like a medic, no panic in it, all economy — straight lines, early turns, nothing wasted."
2"On Old Compton Street she shouted it — police, stop him — and two men under an awning watched him pass with the flat interest of people who had decided years ago that nothing was their business."
3"He knocked — two, a pause, three."
4"His radio carried eleven seconds of him after the dark took it — a count of steps, then a word that wasn't a word, then a silence no report ever found language for."
5"A domino of yellowed bone with a spiral drilled through it — darker than the one Herrera had shown the door, the spiral worn shallow, one corner snapped off."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1066
adjectiveStacks0
stackExamples(empty)
adverbCount29
adverbRatio0.027204502814258912
lyAdverbCount1
lyAdverbRatio0.0009380863039399625
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences96
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences96
mean13.22
std9.77
cv0.739
sampleLengths
019
124
216
37
421
512
66
76
832
95
108
1115
125
1338
143
1518
1615
1719
183
197
2037
2124
2212
2336
2422
2520
266
2715
2813
2912
307
313
3211
337
3410
3515
3610
3714
3815
394
409
414
424
4311
446
452
4620
477
4814
4920
70.49% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats4
diversityRatio0.4479166666666667
totalSentences96
uniqueOpeners43
45.05% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences74
matches
0"Then he ran."
ratio0.014
47.03% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount32
totalSentences74
matches
0"She stood in a doorway"
1"She gave him half a"
2"He was the loose thread."
3"He turned off Wardour Street."
4"She crossed after him, soles"
5"He ran like a medic,"
6"She ran the way the"
7"He shoved a delivery moped"
8"He cut left onto Charing"
9"He skipped the escalator and"
10"He boarded at the far"
11"She caught the near doors"
12"She walked its full length"
13"He touched the medallion and"
14"His sleeve rode up and"
15"His reflection spoke to hers"
16"She gave him nothing."
17"He was through the doors"
18"He cut into the Stables"
19"His left hand stayed fisted"
ratio0.432
27.57% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount64
totalSentences74
matches
0"The green neon above the"
1"Rain fell fine and sideways,"
2"She stood in a doorway"
3"Herrera came out at quarter"
4"Duffel bag over one shoulder,"
5"She gave him half a"
6"An ex-paramedic with a dead"
7"He was the loose thread."
8"That was the theory she'd"
9"He turned off Wardour Street."
10"She crossed after him, soles"
11"He ran like a medic,"
12"She ran the way the"
13"He shoved a delivery moped"
14"A cyclist swore."
15"A bin went over somewhere"
16"He cut left onto Charing"
17"He skipped the escalator and"
18"The platform held four people"
19"Hood back now, curls plastered"
ratio0.865
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences74
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences46
technicalSentenceCount2
matches
0"She crossed after him, soles hissing on wet stone, and caught the moment he made her: a stationery shop's window, her reflection arriving beside his, half a sec…"
1"On Old Compton Street she shouted it — police, stop him — and two men under an awning watched him pass with the flat interest of people who had decided years ag…"
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
totalTags1
fancyCount1
fancyTags
0"His reflection spoke (speak)"
dialogueSentences25
tagDensity0.04
leniency0.08
rawRatio1
effectiveRatio0.08
86.0732%