Run: 2026-03-18-01KM09F3RN5AP7ZPQYN5CF98ZY

Run Details

Duration:
22.7s
Cost:
$0.019138
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 sheeted down the Camden streets and turned the pavements into black glass. Quinn stood under the awning of a shuttered chemist, water running off the brim of her collar and ticking from the edge of her sleeve. Her watch sat heavy on her left wrist, the cracked leather dark with damp. She kept her shoulders square, her jaw set, and watched the man ahead of her slip through the knot of late-night stragglers with one hand clamped over a brown paper packet. He moved too fast for a drunk and too careful for a mugger. He cut around a bus shelter, clipped a woman’s umbrella, and kept going. Quinn pushed off the wall. “Oi.” He looked back once. Hood low. Face long and pale under the streetlight. His eyes snagged on her badge and broke away. His mouth twisted. “You picked a bad night for this, Detective.” He went into a run. Quinn crossed the road on the diagonal, boots hammering through standing water. A taxi threw a wave of spray over her trouser leg. She ducked a swinging shop sign, shouldered past a pair of students laughing under one coat, and kept the runner in sight as he cut down a side street lined with closed food stalls and tarpaulins that snapped in the wind. The packet vanished under his coat. He reached for something at his belt, fingers slick with rain. “Drop it and stop making this a circus.” He threw a glance over his shoulder. “You still think this is your city?” He slammed into a stack of milk crates outside a noodle shop. Plastic skidded across the road, lids splitting, broth spilling in steaming ribbons. Quinn hopped over the mess and caught a glimpse of what he carried: a small bone-white disc tied with red thread. Her pace tightened. A bone token. The runner must have seen the change on her face. He grinned without any warmth and bolted harder, boots splashing through the gutter, rain bouncing off his coat in silver flecks. Quinn’s lungs drew in cold, wet air that tasted of diesel and frying oil. Her thigh burned. She ignored it and drove on. A bike courier swore as the runner shoved through him. “Mind your back!” The runner ducked into an alley so narrow that the brick walls seemed to scrape the sky apart above it. Quinn followed, one hand out to steady herself on the wall, fingers brushing old graffiti and grit. The smell changed in the alley. Less traffic. More damp stone, rust, and rot trapped in drains. At the far end, the runner stopped. Quinn slowed a step. He stood in front of a brick wall tagged with fresh paint, one foot planted in a puddle, the bone token dangling from his fingers. He stared at her with rain running from his lashes. “You’re too late,” he said. Then he pressed the token to the brick. Nothing happened for a beat. Then a seam of light cut through the wall, thin as a blade. The bricks shifted with a grinding groan, and a service door swung inward to reveal a stairwell dropping into darkness. Quinn’s hand went to her holster. The runner backed through the opening. “You coming, Detective?” The door started to swing shut again. Quinn lunged forward and caught it with her shoulder. Wet cold poured out from the stairwell, carrying the smell of metal, mildew, and something sour beneath it, something chemical and wrong. She leaned into the gap. “Where does that go?” The runner smiled from three steps down. “Depends who asks.” A figure emerged from the shadow beside the door, broad-shouldered in a fluorescent jacket, face half hidden by a hood. He planted one hand on the frame and blocked the way with a square chest and a dead-eyed stare. “No token, no market.” Quinn lifted her badge. “I’m Metropolitan Police.” The hooded man’s gaze dropped to the badge and stuck there for a moment. “On this side? Means less than the rain.” The runner below laughed. It bounced up the stairwell and struck the walls. Quinn kept her weight forward, her breath steady, her fingers closed hard around the edge of the door. “Move.” The hooded man didn’t. His eyes flicked to the token still in the runner’s hand, then to Quinn’s face. “He took your invitation, not mine.” Quinn shifted and caught the flicker of movement below. Not just stairs. A landing. A platform. Voices. Low, sharp, layered over the hum of hidden power and old electricity. She had chased suspects through estates, council flats, docks, and alleyways where men with knives thought they owned the dark. This was different. The stairwell breathed on her like a throat. Every step below seemed to pull her further from the city she knew and deeper into something that had its own rules. Her radio cracked on her shoulder. “Quinn, report.” She thumbed the mic without taking her eyes off the stairs. “I’ve got him.” Static bit back at her. Then another voice, tinny and distant. “Location?” She looked once over her shoulder. Rain hammered the alley mouth. The street behind her shone slick under the sodium lamps. If she backed out now, she could call for units, sweep the block, wait. The runner would vanish into whatever net this place had thrown beneath Camden, and every answer she’d chased for the past week would sink with him. Below, the runner’s voice drifted up. “You can bring a van if you want. Won’t help.” Quinn’s mouth tightened. The hooded man at the door watched her without blinking. “You got no business down there,” he said. “Not with that face.” Quinn pulled the badge back down and tapped the holster at her hip. “I’ve got business wherever suspects hide.” He let out a breath that sounded like a laugh scraped through gravel. “Then you’d best like surprises.” A burst of sound rolled up from below. Not music. Not exactly. A market’s murmur, crowded and low, with the clink of glass and the scrape of crates and a dozen voices trading in the dark. Something in that noise touched the back of Quinn’s neck. It carried the same private, guarded tone as a room full of men counting money or splitting lies. The runner’s footsteps started again. Faster now. Deeper. Quinn looked at the token in the man’s hand as he descended. Bone-white. Clean. Too clean for the alley, for the rain, for the hands that had passed it on. The hooded man shifted his weight. “He’s got an hour’s head start on you now.” Quinn glanced at the faint mark left on the brick where the token had touched it, a pale smear in the wet stone. “How many of you are down there?” He gave her a flat look. “Enough.” Another set of steps sounded below, then another. A cart rattled over uneven ground. Someone barked a price in a voice roughened by smoke. The station under Camden had opened its mouth and filled with lives that wanted keeping secret. Quinn drew a breath through her nose and tasted rain, rust, and the faint bite of chemicals. Her fingers tightened around the door edge until the skin pinched. A thought of Morris flashed through her head, not soft, not ghostly, just the hard memory of a partner gone missing and a case that had refused to stay dead. She pushed it aside and kept her eyes on the dark. The hooded man moved half a step aside, not enough to welcome her, only enough to leave the choice where it belonged. Quinn reached past him and took the stair rail. It came up cold and slick under her palm. From below, the runner called back once, his voice thin through the shaft. “Coming, Detective?” She held there at the top step, rain dripping from her coat onto the concrete, and stared into the hollow light below.
created_at"2026-03-18T10:57:05.826Z" 3/18/2026, 10:57:05 AM
model"openai/gpt-5.4-mini-20260317"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency19927
moderation_latency(null)
generation_time21487
tokens_prompt1140
tokens_completion2894
native_tokens_prompt984
native_tokens_completion4089
native_tokens_completion_images0
native_tokens_reasoning2438
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"
usage0.0191385
router(null)
provider_responses
0
endpoint_id"9ee065b2-3d1c-43bc-bdd7-28af3b148282"
id"resp_05b89c5293f6b5510169ba8501e3048190b0ef4712fb22f7a7"
is_byokfalse
latency182
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-1773831425-hJ70VN1hK6Btud1HdA8J"
upstream_id"resp_05b89c5293f6b5510169ba8501e3048190b0ef4712fb22f7a7"
total_cost0.0191385
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
totalTags2
adverbTagCount0
adverbTags(empty)
dialogueSentences26
tagDensity0.077
leniency0.154
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1322
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)
77.31% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1322
totalAiIsms6
found
0
word"warmth"
count1
1
word"weight"
count2
2
word"flicked"
count1
3
word"flicker"
count1
4
word"footsteps"
count1
highlights
0"warmth"
1"weight"
2"flicked"
3"flicker"
4"footsteps"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches1
maxInWindow1
found
0
label"let out a breath"
count1
highlights
0"let out a breath"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences114
matches(empty)
92.73% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount3
narrationSentences114
filterMatches
0"watch"
hedgeMatches
0"seemed to"
1"started to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences138
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen41
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1322
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions3
unquotedAttributions0
matches(empty)
66.67% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions24
wordCount1204
uniqueNames3
maxNameDensity1.66
worstName"Quinn"
maxWindowNameDensity3
worstWindowName"Quinn"
discoveredNames
Camden3
Quinn20
Morris1
persons
0"Camden"
1"Quinn"
2"Morris"
places(empty)
globalScore0.669
windowScore0.667
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences82
glossingSentenceCount1
matches
0"sounded like a laugh scraped through grave"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1322
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences138
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs81
mean16.32
std17.88
cv1.095
sampleLengths
084
113
213
35
41
522
63
78
85
964
1017
118
127
137
1445
153
163
1754
1810
193
2054
217
224
2335
245
258
2638
276
286
293
3038
315
324
337
343
3539
364
374
383
3914
408
4113
4218
431
4419
456
4629
4753
486
492
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences114
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs203
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences138
ratio0
matches(empty)
88.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1209
adjectiveStacks2
stackExamples
0"small bone-white disc"
1"same private, guarded tone"
adverbCount28
adverbRatio0.02315963606286187
lyAdverbCount3
lyAdverbRatio0.0024813895781637717
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences138
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences138
mean9.58
std7.26
cv0.758
sampleLengths
014
125
214
331
413
513
65
71
84
92
107
119
123
138
145
1512
1611
1741
186
1911
208
217
227
2312
2412
2521
263
273
2810
2921
3014
313
326
3310
343
3520
3617
376
382
399
407
414
4225
4310
445
458
465
4713
4820
496
55.56% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats5
diversityRatio0.35507246376811596
totalSentences138
uniqueOpeners49
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount5
totalSentences101
matches
0"More damp stone, rust, and"
1"Then he pressed the token"
2"Then a seam of light"
3"Then another voice, tinny and"
4"Too clean for the alley,"
ratio0.05
89.31% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount33
totalSentences101
matches
0"Her watch sat heavy on"
1"She kept her shoulders square,"
2"He moved too fast for"
3"He cut around a bus"
4"He looked back once."
5"His eyes snagged on her"
6"His mouth twisted."
7"He went into a run."
8"She ducked a swinging shop"
9"He reached for something at"
10"He threw a glance over"
11"He slammed into a stack"
12"Her pace tightened."
13"He grinned without any warmth"
14"Her thigh burned."
15"She ignored it and drove"
16"He stood in front of"
17"He stared at her with"
18"She leaned into the gap."
19"He planted one hand on"
ratio0.327
29.31% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount87
totalSentences101
matches
0"Rain had sheeted down the"
1"Quinn stood under the awning"
2"Her watch sat heavy on"
3"She kept her shoulders square,"
4"He moved too fast for"
5"He cut around a bus"
6"Quinn pushed off the wall."
7"He looked back once."
8"His eyes snagged on her"
9"His mouth twisted."
10"He went into a run."
11"Quinn crossed the road on"
12"A taxi threw a wave"
13"She ducked a swinging shop"
14"The packet vanished under his"
15"He reached for something at"
16"He threw a glance over"
17"He slammed into a stack"
18"Plastic skidded across the road,"
19"Quinn hopped over the mess"
ratio0.861
49.50% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount1
totalSentences101
matches
0"If she backed out now,"
ratio0.01
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences54
technicalSentenceCount3
matches
0"She ducked a swinging shop sign, shouldered past a pair of students laughing under one coat, and kept the runner in sight as he cut down a side street lined wit…"
1"Every step below seemed to pull her further from the city she knew and deeper into something that had its own rules."
2"A thought of Morris flashed through her head, not soft, not ghostly, just the hard memory of a partner gone missing and a case that had refused to stay dead."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags2
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags2
fancyCount0
fancyTags(empty)
dialogueSentences26
tagDensity0.077
leniency0.154
rawRatio0
effectiveRatio0
91.6127%