Run: 2026-03-18-01KM088PACBSYYTGF7E9Y7JH47

Run Details

Duration:
48.3s
Cost:
$0.026523
Provider:
OpenAI

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 had turned Soho into a mirror with cracks in it. Green neon from The Raven’s Nest smeared across the pavement, shook in puddles, broke against taxi tyres and the iron ribs of a bus stop. Detective Harlow Quinn stood under the narrow awning by the bar door with her coat buttoned to her throat and her close-cropped hair darkened by spray. The worn leather watch on her left wrist ticked against the pulse at the base of her thumb. She kept her eyes on the man who had just come out of Silas’s bar. He wore a charcoal hooded coat and moved with the stiff, careful step of someone carrying more than he wanted to admit. One hand stayed buried at his side. The other clamped a folded packet to his ribs. He did not look like a drinker. He looked like a courier who had seen too much and wanted the night to swallow him before anyone asked questions. Quinn had spent forty minutes in the back room behind the bookshelf, listening to whispers over the low scrape of glasses. A name had surfaced in the noise. Camden. Bone token. Down below. Then the man had slipped out of the secret room, glanced once at the bar mirrors, and headed for the door. Now he cut down the pavement, head tucked, rain bouncing off his shoulders. Quinn stepped off the kerb and followed. He reached the end of the street and swung right, too fast for a drunk, too clean for a man with nothing to hide. A cab nosed through a spray of water and braked. The man yanked the rear door open, ducked inside, and threw himself across the seat. Quinn crossed the road at a stride and lifted her badge at the driver before he could pull away. “Move,” she barked. The cabbie’s eyes flicked to her badge, then to the back seat. The man twisted round, face pale under the cabin light, and recognition sharpened his mouth. “You’re persistent, Quinn.” He shoved cash at the driver, slammed the door, and the cab shot off through the rain with its brake lights flaring red against the wet street. Quinn was already moving. She cut back to her car, a plain black unmarked estate parked half a block away beneath a row of drooping plane trees. The engine caught on the first turn. She swung into traffic, one hand on the wheel, the other steadying the radio that crackled with useless chatter. The cab slipped between buses and black cabs, its wipers thrashing. Quinn kept it in sight for three junctions, through a sheet of rain and traffic lights, past shuttered shops and boarded fronts, north through the city’s slick veins until Camden’s signs began to crowd the roads. The cab jerked to the kerb outside a side street off Camden High. The rear door opened before the car stopped. The man spilled out and hit the pavement at a run. Quinn cursed under her breath and braked hard. Her tyres hissed across the wet road. She came up fast, badge in her coat pocket, hand on the grip at her hip, eyes locked on the hood vanishing between a fried chicken shop and a tattoo parlour. He had a head start, not much more. Camden at night wore a meaner face than the daylight one. Neon bled off wet signs. Half-shut shutters rattled in the wind. Flyers clung to lamp posts in damp scraps. The crowd thinned with the hour, leaving drunks, buskers packing up, a pair of boys arguing over a bike lock, a woman in a red scarf hurrying with her collar up and her phone pressed to her ear. Quinn pushed through them without breaking stride. The suspect slammed through a narrow passage between two brick buildings. His shoulder clipped a steel bin, sending it skidding across the slick ground. Quinn took the turn tight, boots hitting the wet concrete with a hard slap. The alley stank of old oil and piss. A cat shot past her legs and vanished beneath a delivery ramp. The man hit the far end and vaulted a locked gate meant to keep the night from swallowing the service yard beyond. Quinn grabbed the top rail, hauled herself over, and landed in a crouch that saved her knees from the impact. He glanced back then, just once, rain flashing on his face. His mouth split in a grin that held no humour. “You won’t like where this goes.” She answered by closing the gap. He bolted through a service road lined with bins and blind brick walls, then cut left into another alley that dropped steeply toward the station quarter. Quinn heard the distant groan of trains, the hiss of brakes, the scrape of a shutter being forced down in some unseen shop front. Her lungs burned. The rain had soaked through her coat and found the seam at her neck. Water dripped from her sleeves and ran cold over her hands. He knew the ground better than she did. That irritated her more than the weather. He took the steps two at a time, vanishing beneath the street into a disused entrance framed by old advertising panels and a stained white sign that had once named a Tube line. Now it looked like a hole someone had tried to patch and given up on. The light under the canopy glowed weak amber, and it came from bulbs too weak to reach the bottom of the stairs. Quinn slowed at the top. The suspect had vanished down there. She heard his boots hit the landing. Then a second set of footsteps. Then a pause, and a voice too low to catch the words. She moved down the stairs with one hand on the rail. Water ran in silver threads off the concrete steps. The air changed as she descended, the city smell stripped away and replaced by damp stone, ozone, and something metallic under it, sharp enough to make the back of her throat tighten. The walls bore fresh chalk marks over old grime, sigils scratched beside graffiti tags and faded station warnings. Someone had layered a new world over the dead one and made the bones of the old place serve it. At the landing, a narrow gate stood open between two brick pillars. Beyond it, a corridor sloped into darkness lit by lanterns hung from wires. A man in a flat cap and wax jacket blocked the way with one palm up. “Token.” Quinn stopped just short of him. Her badge stayed hidden. Her hand rested near her holster, not enough to threaten, enough to remind. The suspect stood ten paces beyond the gate, half-turned now, his hood thrown back. Rain clung to his dark hair. He held a pale disc no bigger than a coin between two fingers. Bone. Carved smooth. He tapped it against the guard’s palm. The gatekeeper stepped aside. The man looked at Quinn over his shoulder, eyes bright with the thrill of a cliff edge. “Too late.” He went through. Quinn moved to follow and the gatekeeper’s hand blocked her path. “Token.” She held his stare, then let her gaze drop to the suspect’s back as he disappeared into the corridor. The sound of voices drifted up from below, dozens of them, layered and shifting, a market’s noise muffled by stone. Something clinked down there. Glass. Metal. A laugh cut sharp and quick, then died. Quinn slipped her hand into her coat and came up with the thing she had snatched from the stairs without thinking when the suspect had taken the turn too fast and a pale disc had skittered across the step beside her boot. Bone, rough on one side, polished on the other, a hole drilled through the middle. It still held the damp from the rain. The gatekeeper’s eyes narrowed. “You found one.” “It fell.” He looked at her badge when she opened her coat enough to show the corner of it, then at the token, then at her face. His expression never changed, but his body shifted aside by a fraction, just enough to say he had decided she was someone else’s problem now. Quinn took one breath. Then another. The corridor beyond the gate opened wider than it had any right to, the old station reborn under layers of tarps, wire, and hanging bulbs. The ceiling arched low and black above rows of stalls pressed into the platform edges. Tables crowded the passage with bottles of cloudy liquid, brass locks, folded maps, bones strung as charms, silver rings that caught the light and threw it back in hard splinters. People moved through it in coats slick with rain, some human, some not, all of them with the same careful look of those who knew the law had not followed them this far. The suspect had already begun to disappear into the crush. Quinn rolled the bone token across her knuckles once, felt its rough edge bite her skin, and stepped over the threshold.
created_at"2026-03-18T10:36:06.872Z" 3/18/2026, 10:36:06 AM
model"openai/gpt-5.4-mini-20260317"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency45134
moderation_latency(null)
generation_time47700
tokens_prompt1140
tokens_completion4141
native_tokens_prompt984
native_tokens_completion5730
native_tokens_completion_images0
native_tokens_reasoning3900
native_tokens_cached0
num_media_prompt(null)
num_input_audio_prompt(null)
num_media_completion0
num_search_results(null)
origin"https://ncbench.com/"
is_byokfalse
finish_reason"stop"
native_finish_reason"completed"
usage0.026523
router(null)
provider_responses
0
endpoint_id"9ee065b2-3d1c-43bc-bdd7-28af3b148282"
id"resp_0ce9c63a91aa981b0169ba8016ed4c8196a9bf6e46a68c2499"
is_byokfalse
latency179
model_permaslug"openai/gpt-5.4-mini-20260317"
provider_name"OpenAI"
status200
user_agent"langchainjs-openai/1.0.0 ((node/v24.14.0; linux; x64))"
http_referer(null)
api_type"completions"
id"gen-1773830166-iQXaz7dRhoZfyHxI3USJ"
upstream_id"resp_0ce9c63a91aa981b0169ba8016ed4c8196a9bf6e46a68c2499"
total_cost0.026523
cache_discount(null)
upstream_inference_cost0
provider_name"OpenAI"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags1
adverbTagCount0
adverbTags(empty)
dialogueSentences8
tagDensity0.125
leniency0.25
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1509
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)
90.06% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1509
totalAiIsms3
found
0
word"pulse"
count1
1
word"flicked"
count1
2
word"footsteps"
count1
highlights
0"pulse"
1"flicked"
2"footsteps"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches1
maxInWindow1
found
0
label"eyes widened/narrowed"
count1
highlights
0"eyes narrowed"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences111
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount2
narrationSentences111
filterMatches
0"watch"
hedgeMatches
0"began to"
1"tried to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences118
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen42
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1509
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions2
unquotedAttributions0
matches(empty)
89.60% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions32
wordCount1490
uniqueNames10
maxNameDensity1.21
worstName"Quinn"
maxWindowNameDensity2
worstWindowName"Quinn"
discoveredNames
Soho1
Raven1
Nest1
Harlow1
Quinn18
Silas1
Camden4
High1
Tube1
Bone3
persons
0"Nest"
1"Harlow"
2"Quinn"
3"Silas"
4"Camden"
places
0"Soho"
1"Raven"
globalScore0.896
windowScore1
95.65% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences92
glossingSentenceCount2
matches
0"looked like a courier who had seen too mu"
1"looked like a hole someone had tried to p"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1509
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences118
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs50
mean30.18
std29.08
cv0.963
sampleLengths
080
115
266
354
413
57
649
719
83
927
103
1127
12100
1332
1446
158
1668
177
1858
1953
2010
216
226
2378
2415
2570
265
276
2825
2990
3041
311
3223
3343
344
3517
362
373
3811
391
4053
4165
424
433
442
4550
466
47103
4810
4921
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences111
matches
0"being forced"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs243
matches
0"was already moving"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences118
ratio0
matches(empty)
94.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1493
adjectiveStacks1
stackExamples
0"plain black unmarked estate"
adverbCount47
adverbRatio0.03148024112525117
lyAdverbCount1
lyAdverbRatio0.0006697923643670462
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences118
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences118
mean12.79
std9.1
cv0.712
sampleLengths
011
125
226
318
415
522
67
79
87
921
1021
117
121
132
142
1521
1613
177
1824
1910
2015
2119
223
2312
2415
253
2627
274
2823
297
3019
3111
3236
3313
348
3511
368
377
3831
398
4011
415
426
438
4438
457
4611
4713
4814
498
42.37% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats9
diversityRatio0.2966101694915254
totalSentences118
uniqueOpeners35
98.04% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount3
totalSentences102
matches
0"Then the man had slipped"
1"Then a second set of"
2"Then a pause, and a"
ratio0.029
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount30
totalSentences102
matches
0"She kept her eyes on"
1"He wore a charcoal hooded"
2"He did not look like"
3"He looked like a courier"
4"He reached the end of"
5"He shoved cash at the"
6"She cut back to her"
7"She swung into traffic, one"
8"Her tyres hissed across the"
9"She came up fast, badge"
10"He had a head start,"
11"His shoulder clipped a steel"
12"He glanced back then, just"
13"His mouth split in a"
14"She answered by closing the"
15"He bolted through a service"
16"Her lungs burned."
17"He knew the ground better"
18"He took the steps two"
19"She heard his boots hit"
ratio0.294
13.92% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount91
totalSentences102
matches
0"Rain had turned Soho into"
1"Detective Harlow Quinn stood under"
2"The worn leather watch on"
3"She kept her eyes on"
4"He wore a charcoal hooded"
5"The other clamped a folded"
6"He did not look like"
7"He looked like a courier"
8"Quinn had spent forty minutes"
9"A name had surfaced in"
10"Quinn stepped off the kerb"
11"He reached the end of"
12"A cab nosed through a"
13"The man yanked the rear"
14"Quinn crossed the road at"
15"The cabbie’s eyes flicked to"
16"The man twisted round, face"
17"He shoved cash at the"
18"Quinn was already moving."
19"She cut back to her"
ratio0.892
98.04% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount2
totalSentences102
matches
0"Now he cut down the"
1"Now it looked like a"
ratio0.02
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences66
technicalSentenceCount2
matches
0"His mouth split in a grin that held no humour."
1"He took the steps two at a time, vanishing beneath the street into a disused entrance framed by old advertising panels and a stained white sign that had once na…"
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags1
uselessAdditionCount0
matches(empty)
25.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags1
fancyCount1
fancyTags
0"she barked (bark)"
dialogueSentences8
tagDensity0.125
leniency0.25
rawRatio1
effectiveRatio0.25
91.5561%