Run: 2026-09-03-01M1K4VF6RVK1SE74Y41M4H8ZH

Run Details

Duration:
2m 11s
Cost:
$0.030081
Provider:
Meta

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 of The Raven's Nest washed Dean Street in sick colour and turned each puddle to absinthe. Rain hit the Soho pavements in sheets and bounced back in spray and hissed off the extractor vents and drummed on the black cabs parked wing to wing along the kerb. Through the front glass Quinn made out the low amber gloom inside, the walls papered with old maps and black-and-white photographs of boxers and jazz men and long dead regulars, the bar a dark slab at the rear. Quinn held position in the doorway of the closed bookies opposite with her shoulders square and her feet planted and her chin level. Eighteen years with the Met had cut that stance into her spine. Rain ran off her cropped salt-and-pepper hair and down the line of her sharp jaw and into her collar. Brown eyes tracked the door of the bar without blink. She checked the worn leather watch on her left wrist against the church clock. Her warrant card sat warm in her inside pocket next to the folded photograph of DS Morris at the Thames on the last summer before the case three years back took him in a basement in Limehouse with no wound Quinn had the words for and no report that made sense. The door of the Nest swung out and threw a bar of light across the wet asphalt. Tomás Herrera stepped into the rain with his collar up. Quinn knew the face from the surveillance stills. Olive skin gone pale under the neon. Short curls of dark brown hair plastered to his forehead. Warm brown eyes flicked left and right in one quick scan. A Saint Christopher medallion glinted at his throat when he turned into the wind. He shoved his sleeves back against the damp and exposed the white ridge of a scar along his left forearm where a blade had opened him years before. Quinn pushed off the wall and crossed the street straight through the traffic with her hand high and her card out. "Met Police! Herrera! Stand where you are!" Herrera froze for half a heartbeat with rain on his lashes. His gaze locked on her face, on the card, on the street behind her. Then he spun on his heel and sprinted north toward Berwick Street with his boots throwing up fans of water. Quinn went after him. "Out of the way! Police!" Market traders hauled their striped awnings down as the wind ripped them. Crates of damp coriander and plantain spilled across the cobbles. Herrera vaulted a stack of milk crates and landed in a shallow river running down the gutter and kept his footing and lengthened his stride. Quinn drove through the same water with her arms pumping and her breath tearing out of her chest and her shoes losing grip then finding it again on the slick stone. A delivery bloke with a cage of chickens stepped into her path and she shouldered past him and sent feathers up into the rain. "You run and I add resist! Stop!" Herrera glanced back over his shoulder with his medallion bouncing on his chest. "You let Morris bleed out! You want that on your hands again!" The words hit Quinn harder than the rain. She bared her teeth and poured more speed into her legs and closed the gap to six feet as they hit the wide glare of Oxford Street where buses threw walls of spray and brake lights smeared red across the tarmac. Herrera cut across the front of a night bus with inches to spare. Horns blared. Quinn swung around the rear of the bus with her palm slapping the wet metal for balance and kept her eyes on his back as he dived for the white tiled mouth of Tottenham Court Road station. Commuters surged up the steps with umbrellas like black sails. Herrera shouldered down through them against the flow with elbows out. Quinn flashed the card at the barrier staff and jumped the gate in one clean vault. "Police! Hold those gates!" The southbound Northern line train stood with doors open and hot air rolling out and smelling of brake dust and wet wool. Herrera slipped inside sideways at the last chime. Quinn threw herself through the closing doors and landed hard against a pole as the car lurched forward. The carriage swayed. Water dripped from her hair onto her shirt. Passengers stared then looked away. Herrera braced in the far doorway with his chest heaving and his scarred forearm wrapped round the vertical rail and his eyes fixed on her. The train rattled through the dark tunnel with rainwater leaking down the windows. "You patch people off the books! You lost your NHS licence for it! Talk to me before someone else drops!" Herrera laughed without humour and shook water from his curls. "I patch people you lot leave for dead! Seville taught me triage! London taught me silence! The clique did nothing to Morris!" Quinn edged down the carriage with one hand on the rail and her feet wide against the sway. "Then stand and give me names! Give me the back room behind the bookshelf! Give me where you get your supply!" Doors sighed open at Euston. A crush of bodies broke between them. Quinn shoved through with her card up. Herrera used the surge and rode it onto the platform and sprinted for the connecting passage with his boots slapping on the tiles. Quinn stayed on his heels up the escalators two steps at a time past adverts peeling in the damp. They burst out at Camden Town into a different rain, harder and colder, hammering the corrugated shutters of the High Street stalls. Neon from a tattoo parlour and a fried chicken shop smeared across the flooded pavement. The full moon hid behind cloud and turned the sky the colour of wet concrete. Herrera ran past the locked gates of the market with its tarpaulins bellied with water and cut left down a service alley choked with black bags split open and reeking of rot and curry fat. Quinn ran with her lungs raw and her watch face fogged. "Last chance! Stop now!" The alley ended at a rusted fence crowned with razor wire and a faded Underground roundel bolted to brick. Behind the fence a concrete stairwell dropped straight down into blackness with water sheeting down the steps. The padlock hung open with fresh scratches bright on the shackle. Warm air rose from below, thick with ozone and incense and scorched sugar and the low thump of bass. Voices echoed up, haggling, laughing, chanting numbers in languages Quinn half recognised. Herrera hit the fence and scaled it in three moves and dropped to the landing below. A broad figure detached from the shadows at the foot of the steps, bald head shining with rain, arms folded across a chest straining a high-vis vest. The man extended a palm the size of a dinner plate. Herrera dug in his pocket and held up a small yellowed disc of bone etched with a spiral. The token caught the security light for an instant. The big man nodded once and stepped aside and let Herrera pass down into the throat of the abandoned Tube station beneath Camden where lantern light flickered on tiled walls and stalls glowed with vials of green powder and caged things that clicked and strings of teeth and bottles stoppered with wax. Quinn reached the fence and gripped the cold wet mesh with both hands. Rain hammered her back and ran down her neck. Her breath fogged. Below her boots the stairwell fell away into noise and heat and smoke and a press of bodies moving between stalls piled with banned alchemical substances and charms and folded slips of paper that changed hands for information. No uniform backup knew this hole existed. No warrant covered a market that moved every full moon. Procedure demanded a call, a perimeter, a team with shields and radios. Eighteen years of decorated service demanded she hold the line. Morris grinned up at her from the photograph in her pocket with Thames light on his face. Quinn tightened the strap of the worn leather watch on her left wrist and swung over the fence after Herrera.
created_at"2026-09-03T08:06:05.791Z" 9/3/2026, 8:06:05 AM
model"meta/muse-spark-1.3-20260902"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency4809
moderation_latency(null)
generation_time130907
tokens_prompt1104
tokens_completion2114
native_tokens_prompt989
native_tokens_completion6787
native_tokens_completion_images0
native_tokens_reasoning4982
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"completed"
service_tier"auto"
usage0.030081
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.19.0; linux; x64))"
http_referer(null)
request_id"req-1788422765-qrp4wTNoboo2znaHJlwv"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1788422765-KFd0V10Dt4Ph14oXburg"
upstream_id"resp_6a992a6f3dcb0223b0a74202"
provider_responses
0
endpoint_id"cf4f1b4e-1719-4b65-9111-7dd7635e5a2f"
id"resp_6a992a6f3dcb0223b0a74202"
is_byokfalse
latency1752
model_permaslug"meta/muse-spark-1.3-20260902"
provider_name"Meta"
status200
total_cost0.030081
cache_discount(null)
upstream_inference_cost0
provider_name"Meta"
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)
wordCount1386
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)
71.14% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1386
totalAiIsms8
found
0
word"gloom"
count1
1
word"flicked"
count1
2
word"pumping"
count1
3
word"lurched"
count1
4
word"silence"
count1
5
word"echoed"
count1
6
word"etched"
count1
7
word"flickered"
count1
highlights
0"gloom"
1"flicked"
2"pumping"
3"lurched"
4"silence"
5"echoed"
6"etched"
7"flickered"
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
narrationSentences77
matches(empty)
87.20% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount3
hedgeCount0
narrationSentences77
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences86
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen52
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1386
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions1
unquotedAttributions0
matches(empty)
79.91% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions66
wordCount1284
uniqueNames26
maxNameDensity1.4
worstName"Quinn"
maxWindowNameDensity2.5
worstWindowName"Quinn"
discoveredNames
Raven1
Nest2
Dean1
Street4
Soho1
Quinn18
Met1
Morris2
Thames2
Limehouse1
Herrera15
Saint1
Christopher1
Berwick1
Oxford1
Tottenham1
Court1
Road1
Northern1
Euston1
Camden2
Town1
High1
Underground1
Tube1
Rain3
persons
0"Raven"
1"Nest"
2"Quinn"
3"Met"
4"Morris"
5"Herrera"
6"Saint"
7"Christopher"
8"Rain"
places
0"Dean"
1"Street"
2"Soho"
3"Thames"
4"Limehouse"
5"Berwick"
6"Oxford"
7"Tottenham"
8"Court"
9"Road"
10"Euston"
11"Camden"
12"Town"
13"High"
globalScore0.799
windowScore0.833
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences71
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1386
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences86
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs34
mean40.76
std36.32
cv0.891
sampleLengths
088
1129
217
388
421
57
645
74
85
9102
107
1113
1212
1349
1473
1516
164
1789
1813
1920
2010
2122
2218
2321
2461
2587
2611
274
2878
2954
3079
31102
3217
3320
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences77
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs191
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences86
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1289
adjectiveStacks0
stackExamples(empty)
adverbCount15
adverbRatio0.011636927851047323
lyAdverbCount0
lyAdverbRatio0
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences86
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences86
mean16.12
std10.21
cv0.633
sampleLengths
019
131
238
323
412
519
610
714
851
917
1010
118
127
1310
1411
1514
1628
1721
187
1911
2014
2120
224
235
2412
2510
2625
2731
2824
297
3013
3112
328
3341
3413
352
3637
3710
3811
3916
404
4122
428
4318
443
458
465
4725
4813
4920
71.32% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats2
diversityRatio0.4418604651162791
totalSentences86
uniqueOpeners38
43.86% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences76
matches
0"Then he spun on his"
ratio0.013
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount7
totalSentences76
matches
0"She checked the worn leather"
1"Her warrant card sat warm"
2"He shoved his sleeves back"
3"His gaze locked on her"
4"She bared her teeth and"
5"They burst out at Camden"
6"Her breath fogged."
ratio0.092
52.11% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount62
totalSentences76
matches
0"The green neon of The"
1"Quinn held position in the"
2"Rain ran off her cropped"
3"Brown eyes tracked the door"
4"She checked the worn leather"
5"Her warrant card sat warm"
6"The door of the Nest"
7"Tomás Herrera stepped into the"
8"Quinn knew the face from"
9"Olive skin gone pale under"
10"A Saint Christopher medallion glinted"
11"He shoved his sleeves back"
12"Quinn pushed off the wall"
13"Herrera froze for half a"
14"His gaze locked on her"
15"Quinn went after him."
16"Market traders hauled their striped"
17"Herrera vaulted a stack of"
18"Quinn drove through the same"
19"A delivery bloke with a"
ratio0.816
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences76
matches(empty)
ratio0
96.02% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences61
technicalSentenceCount4
matches
0"Her warrant card sat warm in her inside pocket next to the folded photograph of DS Morris at the Thames on the last summer before the case three years back took…"
1"Quinn drove through the same water with her arms pumping and her breath tearing out of her chest and her shoes losing grip then finding it again on the slick st…"
2"The big man nodded once and stepped aside and let Herrera pass down into the throat of the abandoned Tube station beneath Camden where lantern light flickered o…"
3"Below her boots the stairwell fell away into noise and heat and smoke and a press of bodies moving between stalls piled with banned alchemical substances and ch…"
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
90.0516%