Run: 2026-09-03-01M1M1WZEJZQVEM3004YFJEGSH

Run Details

Duration:
4m 36s
Cost:
$0.043060
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
Rain sluiced off the green neon sign above The Raven's Nest, pooling in the cracks of the Soho pavement. Detective Harlow Quinn stood in the doorway of a closed bookmaker's, shoulders squared, water beading on her closely cropped salt-and-pepper hair. Her left wrist bore a worn leather watch, the strap dark with years of rain and sweat. Forty-one years old, eighteen on the job, and her bearing still carried the drill-square precision she'd never shed. The door of the Nest opened. Tomás Herrera stepped out, olive skin slick with drizzle, short curly hair plastered to his skull. The collar of his jacket shifted, revealing the puckered scar running along his left forearm. A Saint Christopher medallion glinted against his chest as he turned up the street. Quinn pushed off the wall. "Quinn to control. Target's mobile, leaving the Raven's Nest. Green jacket, dark curls, scar on the left arm. I'm going to pull him in." Static hissed in her ear. "Backup is twenty out, Detective. Do not engage alone." "Copy." She crossed the street. Neon painted the puddles a bruised green. Herrera was twenty meters ahead, walking with purpose, head down. "Herrera!" He glanced back. His eyes widened. He ran. Quinn swore and broke into a sprint. Rain slashed at her cheeks. Her boots slapped through a puddle, water bursting over the curb. Herrera shoved past a group of laughing tourists, knocking a paper bag from a man's hands. Apples rolled across the wet stones. "Metropolitan Police! Stop!" He didn't stop. He cut left into a narrow alley, shoulder clipping a brick wall. Quinn followed, her breath tearing in her chest, her watch strap chafing her wrist. He vaulted a stack of empty crates; she hit them with her thigh, felt the bruise bloom, kept going. "Herrera! You're only making it worse!" He threw a look over his shoulder, chest heaving, the medallion jumping. "You think this is about the clique? You have no idea what you're walking into!" "Then tell me what happened to Morris!" His face twisted. He didn't answer. He burst out of the alley onto a wider road, traffic hissing past, headlights splitting the rain. He ran north, toward Camden, and Quinn matched him stride for stride, her sharp jaw set, the city blurring into streaks of light and water. The chase chewed through the wet streets. She lost him for thirty seconds near a market square, found him again by the ring of a boot on a fire escape, by the sudden flare of his jacket as he crossed under a streetlamp. Her radio spat static, then died. Backup was a memory. Camden rose around them, all shuttered shops and graffiti. Herrera darted toward a boarded-up Tube entrance, the old station swallowed by scaffolding and hoardings. A side door stood propped open, a bouncer looming in the gap, face half-hidden by a hood. Herrera reached into his pocket. He slapped a bone token against the reader. The lock buzzed. He slipped inside. Quinn hit the door two seconds later. The bouncer moved to pull it shut. "Oi! Token!" She drove her shoulder into the wood. The door swung wide. The bouncer staggered back with a grunt. Quinn slipped through into the stairwell, the smell of old brick and damp washing over her. "She's got no token!" The shout chased her down the stairwell, boots slapping on the steps. Quinn took the stairs three at a time. Herrera was below, a shadow fleeing down and down. The walls closed in, tiled white, cracked, the air growing thick with the stink of ozone and burnt sugar. The street-level noise vanished. Somewhere below, a low hum vibrated through the soles of her shoes. She hit the bottom landing hard. Herrera was ahead, thirty feet, at a gate of black iron set into a brick arch. A guard stepped from the shadows, bulky, eyes reflective as a cat's in the gloom. Herrera held up his token. The guard waved him through. Herrera was gone into the light. Quinn slowed. The gate stood open. Beyond it, the abandoned Tube station opened into a cavern of stalls and lanterns. Scaffolding frames draped with velvet, glass vials of swirling mist, tables piled with bones and bottled shadows. The crowd shifted, and not all of it walked like people. She stepped forward. The guard's arm barred her path. "Token." Quinn flipped open her warrant card, rain dripping from the laminate. "Detective Quinn. Metropolitan Police. I'm in pursuit of a suspect—" "No token." The guard didn't blink. "No market." She looked past him. The Veil Market breathed in the dark, a hundred deals whispered under the hum of the tunnels. Somewhere in there, Herrera was running. Somewhere in there lay the truth about Morris. Her radio gave one final crackle of static and went silent. Quinn's fingers brushed the worn leather of her watch. Three years. Three years of a partner's blood on a rain-slick road and files that made no sense, of a case the Met had quietly buried. Her pulse beat in her throat. The guard's hand shot out, palm up. "Token. Now." She thought of the stairwell behind her, the clean rain, the world of warrants and backup and rules that had failed Morris. Then she looked at the dark, at the lantern light flickering over a hundred impossible things, and she made her choice. Quinn snatched the bone token from the guard's belt, felt its cold weight in her palm, and stepped past him into the Veil Market.
created_at"2026-09-03T16:33:43.9Z" 9/3/2026, 4:33:43 PM
model"tencent/hy4-preview-20260827"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency1927
moderation_latency(null)
generation_time276317
tokens_prompt1104
tokens_completion18293
native_tokens_prompt1016
native_tokens_completion17162
native_tokens_completion_images(null)
native_tokens_reasoning15890
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)
usage0.043059874
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.19.0; linux; x64))"
http_referer(null)
request_id"req-1788453223-bRTzcudBfATQ6MfDbW4f"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1788453223-LVca8MG5e9B2UPRCpXXt"
upstream_id"b5fa0367-f90d-41d1-882b-62d1dc4e1901"
provider_responses
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endpoint_id"ad2a4093-3d2a-41d9-adf1-8b499ac8445d"
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is_byokfalse
latency1927
model_permaslug"tencent/hy4-preview-20260827"
provider_name"Tencent"
status200
total_cost0.043059874
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
totalTags0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount918
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)
67.32% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount918
totalAiIsms6
found
0
word"looming"
count1
1
word"vibrated"
count1
2
word"gloom"
count1
3
word"velvet"
count1
4
word"pulse"
count1
5
word"weight"
count1
highlights
0"looming"
1"vibrated"
2"gloom"
3"velvet"
4"pulse"
5"weight"
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
narrationSentences81
matches(empty)
89.95% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount3
hedgeCount0
narrationSentences81
filterMatches
0"watch"
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
maxSentenceWordsSeen36
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords918
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions2
unquotedAttributions0
matches(empty)
77.62% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions40
wordCount829
uniqueNames14
maxNameDensity1.45
worstName"Quinn"
maxWindowNameDensity2.5
worstWindowName"Herrera"
discoveredNames
Raven1
Nest2
Soho1
Harlow1
Quinn12
Herrera10
Saint1
Christopher1
Camden2
Tube2
Veil2
Market2
Morris2
Met1
persons
0"Raven"
1"Nest"
2"Harlow"
3"Quinn"
4"Herrera"
5"Saint"
6"Christopher"
7"Morris"
8"Met"
places
0"Soho"
1"Camden"
2"Veil"
globalScore0.776
windowScore0.833
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences57
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount918
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
totalParagraphs45
mean20.4
std19.56
cv0.959
sampleLengths
075
151
25
324
45
59
61
721
81
98
1045
113
1248
136
1412
1515
167
1748
1853
1941
2019
2114
222
2334
244
2512
2652
2747
286
2948
303
316
321
3311
3410
352
364
372
3835
3911
4041
417
422
4343
4424
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences81
matches
0"was gone"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs143
matches
0"was running"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount1
flaggedSentences1
totalSentences96
ratio0.01
matches
0"He vaulted a stack of empty crates; she hit them with her thigh, felt the bruise bloom, kept going."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount837
adjectiveStacks0
stackExamples(empty)
adverbCount14
adverbRatio0.016726403823178016
lyAdverbCount3
lyAdverbRatio0.0035842293906810036
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
mean9.56
std6.87
cv0.718
sampleLengths
019
121
217
318
46
516
615
714
85
924
105
119
121
134
147
1510
161
173
183
192
207
215
2211
2316
246
253
263
2712
2814
2919
306
3112
3215
337
343
353
3617
3725
387
3936
406
414
429
4315
4417
455
468
473
483
497
40.63% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats9
diversityRatio0.2916666666666667
totalSentences96
uniqueOpeners28
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount4
totalSentences78
matches
0"Somewhere below, a low hum"
1"Somewhere in there, Herrera was"
2"Somewhere in there lay the"
3"Then she looked at the"
ratio0.051
96.92% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount24
totalSentences78
matches
0"Her left wrist bore a"
1"She crossed the street."
2"He glanced back."
3"His eyes widened."
4"Her boots slapped through a"
5"He didn't stop."
6"He cut left into a"
7"He vaulted a stack of"
8"He threw a look over"
9"His face twisted."
10"He didn't answer."
11"He burst out of the"
12"He ran north, toward Camden,"
13"She lost him for thirty"
14"Her radio spat static, then"
15"He slapped a bone token"
16"He slipped inside."
17"She drove her shoulder into"
18"She hit the bottom landing"
19"She stepped forward."
ratio0.308
11.28% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount70
totalSentences78
matches
0"Rain sluiced off the green"
1"Detective Harlow Quinn stood in"
2"Her left wrist bore a"
3"The door of the Nest"
4"Tomás Herrera stepped out, olive"
5"The collar of his jacket"
6"A Saint Christopher medallion glinted"
7"Quinn pushed off the wall."
8"Static hissed in her ear."
9"She crossed the street."
10"Neon painted the puddles a"
11"Herrera was twenty meters ahead,"
12"He glanced back."
13"His eyes widened."
14"Quinn swore and broke into"
15"Rain slashed at her cheeks."
16"Her boots slapped through a"
17"Herrera shoved past a group"
18"Apples rolled across the wet"
19"He didn't stop."
ratio0.897
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences78
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences35
technicalSentenceCount1
matches
0"Quinn followed, her breath tearing in her chest, her watch strap chafing her wrist."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags0
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags0
89.4574%