Run: 2026-09-03-01M1KY0WR5JDTHY842D1NYPR9Q

Run Details

Duration:
4m 11s
Cost:
$0.035844
Provider:
Tencent

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 sign above The Raven's Nest burned through the rain, a smear of algae-light on the wet tarmac of a Soho backstreet. Beyond the window, old maps and black-and-white photographs papered the walls, the dim interior a warren of shadows. Detective Harlow Quinn stood in a doorway opposite, rain running off her closely cropped salt-and-pepper hair, her sharp jaw set. Eighteen years of the job had drilled her spine straight; even in the downpour she held that military precision. She snapped her left wrist up. The worn leather watch creaked against her skin. 1:04 a.m. The door of the Nest swung open. A man stepped out, shrugging into a canvas satchel. Short curly dark brown hair, olive skin, a Saint Christopher medallion at his throat. Tomás Herrera. The scar on his left forearm caught the neon as he pushed wet curls back from his face. Quinn pushed off the wall. "Tomás Herrera. Police. Don't run." He froze for half a second, then his warm brown eyes locked on hers and he bolted. She swore and went after him. His trainers slapped through puddles that mirrored the fractured neon. Quinn's longer stride ate the distance; her coat flared behind her. A black cab blared its horn as Herrera cut across its bonnet. She followed, shoulder clipping the wing mirror. Metal cracked; the driver leaned on the horn, the blast swallowed by the rain. "Herrera!" Her boots struck wet grit. "You patch up their soldiers, I know about the bookshelf!" He laughed, the sound ragged. "You think a secret room makes you clever, Detective?" "It makes you a suspect. You sewed up a dead man tonight." "Not dead." He vaulted a bin; the lid clanged. "Not yet, anyway." She hooked the bin with her shin and kept running. Rain filled her mouth, tasted of petrol and city filth. Her pulse beat a hard rhythm at the base of her throat. She'd lost Morris in weather like this, three years ago, and the file still made no sense. She shoved the thought down. They hammered north, past shuttered record shops and kebab steam, the crowd thin at this hour. Herrera knew the rat-runs; he took them through an alley reeking of chip fat and piss. Quinn's watch face flashed under a security light as she leapt a stack of wet cardboard. "You were a paramedic," she called. "Seville to the NHS. Then you lost your licence treating things that don't exist." He glanced back. The medallion bounced against his sternum. "They exist. Your lot just look away." "I don't look away." "Then explain Morris." His voice cracked on the name. Quinn's teeth ground together. "What do you know about Morris?" "Only that he went down a hole like the one you're chasing me toward, and he didn't come back the same." The words hit her like a shove. Three years. Her partner, DS Morris, gone under circumstances the pathologist had filed as animal attack and then quietly buried. She remembered the photographs she shouldn't have seen: the claw marks, the way his eyes had gone empty. Supernatural origins, something she didn't understand. That not-knowing had eaten a hole in her chest. "Keep running, Tommy," she said. "You're leading me somewhere." "Am I?" He burst out of the alley onto Camden High Street. The rain hadn't eased. A boarded-up Tube station squatted on the corner, its mouth sealed by corrugated iron and graffiti tags. Beneath the iron, a stairwell dropped into the earth. Quinn knew the rumours. The Veil Market. The full moon had dragged the underground black market here three nights past; by the next moon it would move again. Enchanted goods, banned alchemical substances, information that could gut a case. The kind of place that ate police. Herrera skidded to a stop at the rusted panel beside the sealed gate. He dug into his pocket and pulled out a bone token. A knuckle of yellowed bone, carved with a spiral, worn smooth by fingers. Quinn stopped ten feet away, chest heaving. She planted her feet, shoulders square. "That's far enough, Tommy." He pressed the token to the panel. It clicked. A slab of wall ground back, spilling a breath of spice and burnt herbs and something coppery. A stairwell yawned, lit by a sick green glow from below. Voices murmured in the depths, the clink of glass, the rustle of things that shouldn't breathe. Herrera turned at the top step. Rain plastered his curls to his skull. "You need a bone token, Detective. You don't have one." Quinn's gaze flicked to the stairwell, then back to him. The market breathed its alchemical stench into her face. She thought of Morris, of the file that lied, of the claw marks no one would explain. She needed answers more than she needed her badge. "Last chance," Herrera said. "Down there your warrant card is toilet paper. The Market doesn't care about the Met." She dragged her sleeve across her mouth. The worn leather watch dug into her wrist as she flexed her hand. "Then I'll just have to be convincing." She broke into a run. Herrera's eyes widened. "Quinn, no—" She hit the threshold as the slab began to grind shut. Her shoulder took the edge; pain flared along her arm, but she was through. The stairwell swallowed her, the noise of the market rising to meet her, and Camden's rain cut off above like a slamming door.
created_at"2026-09-03T15:25:57.905Z" 9/3/2026, 3:25:57 PM
model"tencent/hy4-preview-20260827"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency1814
moderation_latency(null)
generation_time251413
tokens_prompt1104
tokens_completion15290
native_tokens_prompt1016
native_tokens_completion14277
native_tokens_completion_images(null)
native_tokens_reasoning13026
native_tokens_cached896
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)
usage0.035844489
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.19.0; linux; x64))"
http_referer(null)
request_id"req-1788449157-fQcnazrDIJ4lLA7xR6jh"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1788449157-VCEotJ831McrHPwVrSR8"
upstream_id"614c1417-58f9-4bbb-b092-a70085230a8b"
provider_responses
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endpoint_id"ad2a4093-3d2a-41d9-adf1-8b499ac8445d"
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is_byokfalse
latency1814
model_permaslug"tencent/hy4-preview-20260827"
provider_name"Tencent"
status200
total_cost0.035844489
cache_discount0.000709632
upstream_inference_cost0
provider_name"Tencent"
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
totalTags7
adverbTagCount0
adverbTags(empty)
dialogueSentences23
tagDensity0.304
leniency0.609
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount906
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)
77.92% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount906
totalAiIsms4
found
0
word"fractured"
count1
1
word"pulse"
count1
2
word"depths"
count1
3
word"flicked"
count1
highlights
0"fractured"
1"pulse"
2"depths"
3"flicked"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches1
maxInWindow1
found
0
label"eyes widened/narrowed"
count1
highlights
0"eyes widened"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences76
matches(empty)
67.67% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount3
hedgeCount1
narrationSentences76
filterMatches
0"watch"
hedgeMatches
0"began to"
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
maxSentenceWordsSeen25
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords906
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions5
unquotedAttributions0
matches(empty)
97.09% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions32
wordCount756
uniqueNames15
maxNameDensity1.06
worstName"Quinn"
maxWindowNameDensity2
worstWindowName"Herrera"
discoveredNames
Raven1
Nest2
Soho1
Harlow1
Quinn8
Saint1
Christopher1
Herrera7
Morris3
Camden2
High1
Street1
Tube1
Veil1
Market1
persons
0"Raven"
1"Nest"
2"Harlow"
3"Quinn"
4"Saint"
5"Christopher"
6"Herrera"
7"Morris"
8"Market"
places
0"Soho"
1"Camden"
2"High"
3"Street"
globalScore0.971
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences58
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount906
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences92
matches
0"held that military"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs36
mean25.17
std23.02
cv0.915
sampleLengths
097
150
25
35
417
56
654
716
814
912
1012
1154
1248
1320
1416
154
169
1710
1821
1960
209
2112
2276
2337
2413
254
2653
2713
2810
2945
3019
3120
327
335
345
3548
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences76
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs135
matches(empty)
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount7
flaggedSentences7
totalSentences92
ratio0.076
matches
0"Eighteen years of the job had drilled her spine straight; even in the downpour she held that military precision."
1"Quinn's longer stride ate the distance; her coat flared behind her."
2"Metal cracked; the driver leaned on the horn, the blast swallowed by the rain."
3"\"Not dead.\" He vaulted a bin; the lid clanged."
4"Herrera knew the rat-runs; he took them through an alley reeking of chip fat and piss."
5"The full moon had dragged the underground black market here three nights past; by the next moon it would move again."
6"Her shoulder took the edge; pain flared along her arm, but she was through."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount764
adjectiveStacks0
stackExamples(empty)
adverbCount11
adverbRatio0.014397905759162303
lyAdverbCount3
lyAdverbRatio0.003926701570680628
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
mean9.85
std5.45
cv0.553
sampleLengths
024
118
220
319
46
58
62
77
89
914
102
1118
125
135
1417
156
1610
1711
1812
197
2014
216
2210
235
249
2512
269
273
2810
2910
3012
3117
325
3316
3416
3516
366
3714
383
396
407
414
429
434
446
4521
467
472
4818
4918
56.16% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats5
diversityRatio0.3695652173913043
totalSentences92
uniqueOpeners34
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences69
matches(empty)
ratio0
63.48% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount27
totalSentences69
matches
0"She snapped her left wrist"
1"He froze for half a"
2"She swore and went after"
3"His trainers slapped through puddles"
4"She followed, shoulder clipping the"
5"Her boots struck wet grit"
6"He laughed, the sound ragged."
7"He vaulted a bin; the"
8"She hooked the bin with"
9"Her pulse beat a hard"
10"She'd lost Morris in weather"
11"She shoved the thought down."
12"They hammered north, past shuttered"
13"He glanced back."
14"His voice cracked on the"
15"Her partner, DS Morris, gone"
16"She remembered the photographs she"
17"He burst out of the"
18"He dug into his pocket"
19"She planted her feet, shoulders"
ratio0.391
3.48% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount63
totalSentences69
matches
0"The green neon sign above"
1"Detective Harlow Quinn stood in"
2"She snapped her left wrist"
3"The worn leather watch creaked"
4"The door of the Nest"
5"A man stepped out, shrugging"
6"The scar on his left"
7"Quinn pushed off the wall."
8"He froze for half a"
9"She swore and went after"
10"His trainers slapped through puddles"
11"Quinn's longer stride ate the"
12"A black cab blared its"
13"She followed, shoulder clipping the"
14"Metal cracked; the driver leaned"
15"Her boots struck wet grit"
16"He laughed, the sound ragged."
17"He vaulted a bin; the"
18"She hooked the bin with"
19"Rain filled her mouth, tasted"
ratio0.913
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
analyzedSentences36
technicalSentenceCount2
matches
0"Enchanted goods, banned alchemical substances, information that could gut a case."
1"Voices murmured in the depths, the clink of glass, the rustle of things that shouldn't breathe."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags7
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags3
fancyCount0
fancyTags(empty)
dialogueSentences23
tagDensity0.13
leniency0.261
rawRatio0
effectiveRatio0
82.1933%