Run: 2026-09-02-01M1H5JXXR4QEK87BTMXXCR040

Run Details

Duration:
1m 8s
Cost:
$0.000481
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 outside The Raven's Nest, bouncing neon green off every puddle along Frith Street. Detective Harlow Quinn stood beneath the awning of a shuttered kebab shop, collar up, watching the bar's door through the steam of her own breath. Three years. Three years of dead ends since Morris, and the trail kept circling back to this place. The door swung open. A man stepped out, hood up, shoulders hunched against the downpour. Olive skin, sharp jaw, the particular way he checked the street before moving—left, right, then over his shoulder without turning his head. Trained. Or hunted. Tomás Herrera. Former paramedic. Struck off. Currently supplying medical care to people who, by every law she knew, shouldn't exist. She let him get twenty paces before she moved. He cut west on Old Compton Street, and she matched him stride for stride, keeping a taxi between them, rain drumming her hood. His pace quickened past the theatre. Not running. Not yet. But his head tilted at her reflection in the dark glass of a closed bakery, and she knew the second he clocked her. He broke. Boots slapping wet stone, he vaulted a recycling bin and swerved into Monmouth Street. She tore after him, lungs burning, the cold air like glass in her throat. He shoulder-checked a woman out of his path and shouted something apologetic over his hood. "Police! Stop!" He didn't. Of course he didn't. They never did. He ducked beneath a railway arch near Camden, threads of steam curling off him now. His white breath came fast. Rain dripped from his sleeves, his curls flattened dark against his forehead. The Saint Christopher medallion swung free of his collar as he backed toward a service door set into the arch's brickwork. "End of the line, Herrera." She fanned her stance, one hand raised, the other resting on her baton. "I just want to talk." "Nobody who says that wants to talk." His accent curled around the words, Seville bleeding through. His hand found the door handle behind him without looking. "Tomás Herrera. Struck off for treating patients at St. Mary's who officially don't exist. I've read the file. Two of those patients were connected to a murder I worked three years ago." Something shifted in his face. Not fear. Recognition. "You're the one who lost her partner." He didn't open the door. Didn't run. The rain filled the silence between them, drumming the arch roof like fingers on a drum skin. "You knew my partner." "I treated someone who did. Once." His eyes flicked past her shoulder, then back. "Listen to me, señorita. You don't want to arrest me. You want answers. And I can't give them to you here." "Then give them to me at the station." "And vanish into a paperwork chain that eats everything? No." He pulled the door open a crack. Beyond it, stairs descended into yellow-orange dark, the kind of light that came from old sodium bulbs and never touched daylight. Warm air spilled out, smelling of candle smoke and wet stone and something coppery underneath. "There is a place. Below. If you want the truth about your partner, you follow me. If you want an arrest, call for backup, and by the time it arrives, the door behind me won't be here anymore." "That's not how doors work." "That's not how doors work," he agreed, "where we're going." He stepped backward into the dark. One heartbeat, two. The stairwell swallowed him. Quinn stood alone under the arch, rain streaming off her hood. Her radio crackled against her shoulder—dispatch checking in, she ignored it. Eighteen years of procedure said wait. Cordon the door. Bring the squad. Do it by the book. The book had never found DS Morris's killer. She keyed her radio. "Quinn. Foot pursuit, Camden, subject lost in the railway arches. I'm going in. Solo." "Quinn, hold position—" She thumbed it off and followed. The stairs were slick, stone worn concave in the middle by feet far older than any Tube station should have been. Sodium light gave way to candlelight, then to strings of glowing lanterns she had no name for, hanging along a curved brick tunnel. The temperature rose with every step. Her rain-soaked coat steamed faintly. The tunnel opened. She stopped breathing for a moment. An abandoned platform stretched before her, tiled in cracked cream and oxblood, and on it sprawled a market. Stalls of black cloth under lamplight. Traders selling jars of something that glowed faintly, herbs that moved when nobody touched them, ink being brushed onto paper by a woman whose shadow didn't match her hands. Perhaps two hundred people milled through the aisles, and not one of them looked entirely ordinary. Some did, it was true—office clothes, sensible shoes. But others had eyes that caught the light like a cat's, or ink crawling along their throats, or fingers with one joint too many. Herrera waited at the foot of the stairs, hands raised in a peace gesture, rain still dripping off him onto the platform tiles. "Bone tokens," he said, nodding at the crowd. "Everyone down here pays in bone tokens. You have none. So walk close to me and touch nothing, and if a trader speaks to you, say nothing. Nod if you must." "You're kidding." "I have never been more serious in my life." He lowered his hands. "These people know what I am. They don't know what you are, and you don't want them guessing. Your service weapon, your warrant card—leave them behind your back and keep walking." She thought of Morris. Of the closed file. Of the word the coroner had used in the final report—unexplained—and the way he'd refused to meet her eyes. She slid her warrant card into her inside pocket and left the baton where it was, hidden beneath her coat. "Where are you taking me?" "Somewhere quiet." He started down the platform, and the crowd parted around him like water around a stone. "There's a woman here. She trades in memory. She saw what happened to your partner three years ago." "Then she can come to a station and make a statement." Herrera stopped. Turned. Rain had stopped dripping; the lantern light caught the scar on his forearm where his sleeve had ridden up. "Detective. Look around you. Do any of these people exist in your station's records?" He held her gaze. "The thing that killed your partner is still out there. It walks through locked doors. It took someone from me too, four years ago, and the police wrote it down as a hit and run. There was no car. There is no report that will ever help you. Only this place. Only her. So you can turn around and climb those stairs and keep doing everything by your book, and it will keep taking people." Above them, faintly, came the sound of the service door swinging shut. "And it will hear you coming," he finished quietly. The market breathed around them—haggling voices, lantern hiss, the clink of bone tokens being counted. Quinn rolled her shoulders once, squared her jaw, and stepped off the platform's edge into the crowd after him. "Move," she said. "And Herrera—if you're leading me into a trap, I won't need backup to end you." "There it is." He almost smiled, warm and tired. "Keep that. You'll want it down here."
created_at"2026-09-02T13:40:25.662Z" 9/2/2026, 1:40:25 PM
model"z-ai/glm-5.3-flash-20260826"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency4598
moderation_latency(null)
generation_time67787
tokens_prompt1104
tokens_completion1822
native_tokens_prompt997
native_tokens_completion1623
native_tokens_completion_images(null)
native_tokens_reasoning8
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)
usage0.000480525
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.19.0; linux; x64))"
http_referer(null)
request_id"req-1788356425-OFu0aBETASwFMEqMqGyT"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1788356425-CUPOclNcwHo3b57A9iCZ"
upstream_id"20260902214025a08627162c744535"
provider_responses
0
endpoint_id"8e9fe48b-2f91-41c3-a8a7-e4a93a8c4ff0"
id"20260902214025a08627162c744535"
is_byokfalse
latency4598
model_permaslug"z-ai/glm-5.3-flash-20260826"
provider_name"Z.AI"
status200
total_cost0.000480525
cache_discount(null)
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
75.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags12
adverbTagCount2
adverbTags
0"His accent curled around [around]"
1"he finished quietly [quietly]"
dialogueSentences32
tagDensity0.375
leniency0.75
rawRatio0.167
effectiveRatio0.125
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1238
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)
83.84% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1238
totalAiIsms4
found
0
word"quickened"
count1
1
word"silence"
count1
2
word"flicked"
count1
3
word"streaming"
count1
highlights
0"quickened"
1"silence"
2"flicked"
3"streaming"
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
narrationSentences86
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences86
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences105
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen75
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1230
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
totalMentions28
wordCount823
uniqueNames18
maxNameDensity0.36
worstName"Street"
maxWindowNameDensity1.5
worstWindowName"Street"
discoveredNames
Raven1
Nest1
Frith1
Street3
Harlow1
Quinn3
Morris3
Herrera3
Old1
Compton1
Monmouth1
Camden1
Saint1
Christopher1
Seville1
Didn1
Tube1
Rain3
persons
0"Harlow"
1"Quinn"
2"Morris"
3"Herrera"
4"Saint"
5"Christopher"
6"Rain"
places
0"Raven"
1"Frith"
2"Street"
3"Old"
4"Compton"
5"Monmouth"
6"Camden"
7"Seville"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences50
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1230
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences105
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs48
mean25.63
std23.52
cv0.918
sampleLengths
042
118
240
320
49
556
62
743
82
99
1053
1123
1226
1332
148
1531
164
1735
188
1991
205
2110
2213
2339
248
2518
263
276
2855
293
306
31101
3223
3339
342
3544
3627
3720
385
3936
4011
4122
4293
4312
449
4534
4618
4716
93.02% Passive voice overuse
Target: ≤2% passive sentences
passiveCount3
totalSentences86
matches
0"being brushed"
1"was, hidden"
2"being counted"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs144
matches(empty)
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount6
semicolonCount1
flaggedSentences6
totalSentences105
ratio0.057
matches
0"Olive skin, sharp jaw, the particular way he checked the street before moving—left, right, then over his shoulder without turning his head."
1"Her radio crackled against her shoulder—dispatch checking in, she ignored it."
2"Some did, it was true—office clothes, sensible shoes."
3"Of the word the coroner had used in the final report—unexplained—and the way he'd refused to meet her eyes."
4"Rain had stopped dripping; the lantern light caught the scar on his forearm where his sleeve had ridden up."
5"The market breathed around them—haggling voices, lantern hiss, the clink of bone tokens being counted."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount832
adjectiveStacks0
stackExamples(empty)
adverbCount23
adverbRatio0.027644230769230768
lyAdverbCount6
lyAdverbRatio0.007211538461538462
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences105
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences105
mean11.71
std10.4
cv0.888
sampleLengths
017
125
22
316
44
511
622
71
82
92
102
112
1214
139
1423
156
162
172
1823
192
2014
2114
2215
232
242
254
263
2715
285
2912
3021
3118
325
3316
3410
3532
365
372
381
3912
402
4117
424
4314
4421
458
4617
4721
4815
4938
87.30% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats5
diversityRatio0.5523809523809524
totalSentences105
uniqueOpeners58
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount3
totalSentences69
matches
0"Currently supplying medical care to"
1"Of course he didn't."
2"Perhaps two hundred people milled"
ratio0.043
57.68% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount28
totalSentences69
matches
0"She let him get twenty"
1"He cut west on Old"
2"His pace quickened past the"
3"She tore after him, lungs"
4"He shoulder-checked a woman out"
5"They never did."
6"He ducked beneath a railway"
7"His white breath came fast."
8"She fanned her stance, one"
9"His accent curled around the"
10"His hand found the door"
11"He didn't open the door"
12"His eyes flicked past her"
13"He pulled the door open"
14"He stepped backward into the"
15"Her radio crackled against her"
16"She keyed her radio."
17"She thumbed it off and"
18"Her rain-soaked coat steamed faintly."
19"She stopped breathing for a"
ratio0.406
75.94% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount53
totalSentences69
matches
0"Detective Harlow Quinn stood beneath"
1"The door swung open."
2"A man stepped out, hood"
3"Olive skin, sharp jaw, the"
4"She let him get twenty"
5"He cut west on Old"
6"His pace quickened past the"
7"Boots slapping wet stone, he"
8"She tore after him, lungs"
9"He shoulder-checked a woman out"
10"They never did."
11"He ducked beneath a railway"
12"His white breath came fast."
13"Rain dripped from his sleeves,"
14"The Saint Christopher medallion swung"
15"She fanned her stance, one"
16"His accent curled around the"
17"His hand found the door"
18"Something shifted in his face."
19"He didn't open the door"
ratio0.768
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences69
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences33
technicalSentenceCount1
matches
0"Beyond it, stairs descended into yellow-orange dark, the kind of light that came from old sodium bulbs and never touched daylight."
41.67% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags12
uselessAdditionCount2
matches
0"His accent curled around, Seville bleeding through"
1"He started, and the crowd parted around him like water around a stone"
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags3
fancyCount1
fancyTags
0"he agreed (agree)"
dialogueSentences32
tagDensity0.094
leniency0.188
rawRatio0.333
effectiveRatio0.063
87.1487%