Run: 2026-07-09-01KX418QR2XNJ7V9K4NNH265VH

Run Details

Duration:
28.2s
Cost:
$0.035925
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 hammered Soho flat and hard, turning the pavement into black glass beneath the green neon raven above the bar. Detective Harlow Quinn burst through the Raven’s Nest door with her coat open and one hand pressed to the radio at her shoulder. “Control, this is Quinn. I’m on foot. Male suspect, dark hood, grey canvas bag. Heading east from Frith Street.” Static swallowed the dispatcher’s reply. The suspect clipped the corner of a parked minicab and vanished between two delivery vans. Quinn drove after him, boots skidding through an oily gutter. Her leather watch slapped against her left wrist. Midnight had passed, but the streets still breathed: taxis hissed through standing water, drunk tourists sheltered beneath shop awnings, a cyclist shouted as Quinn cut across the road without looking. “Police! Stop!” The hooded man looked back. For one clean second, neon caught his face. Olive skin. Short dark curls plastered against his forehead. Warm brown eyes, wide with something sharper than fear. Tomás Herrera. Then he ran. Quinn’s jaw tightened. “Tomás!” He shouldered through a knot of people outside a club. Someone dropped a cigarette into a puddle. Blue sparks spat across the water. Quinn slammed into the crowd after him, shoved past a man in a silver jacket and a woman clutching her shoes to her chest. “Watch where you’re going!” “Tell Herrera to stop running.” Tomás vaulted a low chain barrier, landed in the mouth of a narrow alley, and disappeared beneath a scaffold wrapped in torn plastic. Quinn followed, ducking under the flapping sheet. Rainwater poured off the metal frame in cold ropes and struck the back of her neck. A stack of beer kegs blocked half the passage. Tomás had already cleared them. Quinn planted one hand on a keg, swung over, and hit the ground running. “Tomás, you’re making this worse.” His voice came back from the dark. “You don’t know what worse looks like.” “You came out of a locked back room carrying evidence.” “I came out carrying medicine.” “Medicine doesn’t make a man at the bar door pull a knife on a detective.” “That man wasn’t with me.” “Then why did you run?” Tomás tore around another corner. Quinn followed him into a service lane behind a row of restaurants. Grease slicked the paving stones. Bins stood open in the rain, their lids banging on their hinges. The air stank of old fish, bleach, and wet cardboard. Tomás caught his foot on a broken crate. The grey canvas bag swung from his shoulder. Quinn saw a dark stain across its bottom, blooming wider in the rain. Blood. He righted himself and kept moving. “Stop!” Quinn drew her baton but did not extend it. “You need a medic?” A laugh broke out of him, thin and breathless. “You arrested the last medic who helped.” “I questioned you.” “You put my name in a report.” “You treated a stabbing victim with unlicensed stock in a basement.” “He was dying in a basement.” Tomás reached the end of the service lane and froze. A brick wall rose before him, black with rain. On either side stood rusted security fences topped with curled wire. Quinn closed the distance, her hand moving toward her cuffs. Then Tomás drove his shoulder into a section of fence hidden behind a mound of flattened boxes. The panel swung inward. Quinn caught it before it shut in her face. Beyond lay a stairwell dropping beneath street level, its concrete steps wet and furred with moss. A sign hung crooked above the entrance. CAMDEN TOWN STATION — CLOSED. The symbol for the Underground had been smashed through the centre. Tomás took the stairs two at a time. Quinn stopped at the top. The rain struck her shoulders. Water ticked down the back of her collar. Below, darkness swallowed Tomás after six steps, then swallowed the pale flash of his hand gripping the rail. Her radio crackled. “Quinn? Your signal broke up. Repeat location.” She looked over her shoulder. Behind the fence, the city carried on beneath its blur of rain and electric colour. A bus growled past the lane mouth. Music thumped through a wall. Above the rooftops, the green raven sign blinked on and off. “I’m at a disused entrance near Camden,” Quinn told the radio. “Suspect entered below ground.” “Units are rerouting. Hold position.” Tomás’s voice rose from the stairwell. “Don’t bring them down here.” Quinn stared into the black. “You’re in no position to give orders.” “You bring uniforms through that door, people die.” “Who?” No answer. Quinn stepped down one stair. Concrete groaned under her weight. “Tomás. Put the bag down and come back up.” “Listen to me.” His voice sounded closer now. The stairwell bent beneath the street, carrying every word along the tiled walls. “People down here don’t care about your badge. They don’t care about your warrant. They won’t see a detective. They’ll see a threat.” “You expect me to take that on faith?” “You’ve already walked through enough strange rooms tonight to know faith isn’t the problem.” Quinn’s fingers closed around the rail. The hidden room at the Raven’s Nest had held no drugs, no weapons, no stolen cash. It had held shelves of stoppered glass jars and folded maps marked in inks that shifted when she moved. One map had shown London’s streets as veins around a black shape beneath Camden. Then Tomás had arrived through the bookshelf door with the canvas bag, seen her, and run. Quinn had spent eighteen years building cases from what people left behind: fibres, phone records, lies that split under pressure. Her former partner, DS Morris, had taught her patience. Morris had also vanished three years ago in a warehouse with every exit covered and his weapon still holstered. The report had called it an unexplained disappearance. Quinn called it a failure with paperwork. A warm glow flickered at the bottom of the stairs. Tomás had reached an old ticket hall. She saw him in fragments between the rails: bent shoulders, soaked jacket, one hand pressed to the bag. A Saint Christopher medal glinted at his throat. He turned and looked up at her. “You followed me from the Nest because you think I know what happened to Morris.” Quinn took another step down. “I think you know why people keep vanishing around places like that bar.” “No. You think if you find one thing you can put in an evidence bag, it will turn the last three years into something that makes sense.” “You don’t know a thing about the last three years.” “I know you still wear his old case pin inside your coat.” Her hand went to the lapel without permission. Tomás saw it and looked away. “I treated someone tonight,” he called. “They needed something the hospitals didn’t have. I brought it from the Nest. That’s all.” “Then why the blood?” “Because he was bleeding.” “Who was he?” Tomás lifted the canvas bag. Blood dripped from its corner and pattered onto the tile. “Not a man you can question.” A low sound rolled through the station. Quinn held still. It began under her boots. A vibration. Then metal shrieked somewhere far below, as if an old train dragged itself across tracks buried for decades. The stairwell lights blinked once. Twice. On the third pulse, they came on. Not station lights. Hundreds of tiny lamps burned below in colours that did not belong in any electrical fitting: violet, green, a gold that looked thick enough to pour. The abandoned ticket hall opened beneath Quinn. Stalls crowded the old concourse where commuters had once queued for paper tickets. Tarpaulins stretched between cracked pillars. Glass cases displayed rings, teeth, bottled smoke, knives with handles carved from antler. A woman in a red veil sold black feathers by the handful. Two boys in school uniforms haggled over a jar that held a small, beating blue light. Nobody spoke above a murmur. Yet every face in the market turned toward the stairwell. Quinn felt their attention land on her badge before she could cover it. Tomás stood beside an iron gate at the far end of the hall. Beyond it, a tunnel yawned under a faded roundel sign. He reached into his pocket and drew out a pale disc no larger than a pound coin. Bone. He pressed it into a slot beneath the gate latch. The iron bars unlocked with a sound like a breath drawn through broken teeth. Quinn’s radio burst into life. “Quinn, units are six minutes out. Hold where you are.” A tall man behind a stall of brass instruments leaned across his counter. His eyes reflected the market lamps without pupils. “Police,” he called. “Leave your weapon upstairs.” A dozen hands shifted beneath tables and coats. Quinn looked down at Tomás. He had gone pale beneath the rainwater. His left forearm showed through a torn sleeve, the old scar raised and white against his skin. Fresh blood soaked the canvas bag. His gaze fixed on the tunnel beyond the gate. “You came here before,” Quinn called. Tomás’s mouth pulled tight. “Once.” “For what?” “To save somebody.” “Did you?” The market noise thinned. Even the strange little blue light in its jar stopped beating. Tomás looked at her across the broken station. “No.” The gate began to swing shut behind him. Quinn stood with one boot on the stair and one in the old ticket hall, rainwater dripping from her coat onto tiles marked with symbols she could not read. Her backup remained six minutes away. The suspect had crossed into a place that carried no rules Quinn recognised, where every watching face measured the reach of her badge. Tomás gripped the gate before it closed. “If you’re coming, Detective, come now.” Quinn drew her radio from her shoulder, switched it off, and stepped into the Veil Market.
created_at"2026-07-09T18:12:02.443Z" 7/9/2026, 6:12:02 PM
model"openai/gpt-5.6-terra-20260709"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency2196
moderation_latency(null)
generation_time27905
tokens_prompt1140
tokens_completion2708
native_tokens_prompt984
native_tokens_completion2231
native_tokens_completion_images0
native_tokens_reasoning146
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"default"
usage0.035925
router(null)
provider_responses
0
endpoint_id"3cc89000-ae34-4dde-9c7d-5ce009c8af0b"
id"resp_0f0d9033ae07032d016a4fe47283088193bde4a1217321c449"
is_byokfalse
latency227
model_permaslug"openai/gpt-5.6-terra-20260709"
provider_name"OpenAI"
status200
user_agent"langchainjs-openai/1.0.0 ((node/v24.18.0; linux; x64))"
http_referer(null)
request_id"req-1783620722-ajSzbBUtnn3DqDGu8wzW"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1783620722-oMqPgkXEU9HT1DjBQdCI"
upstream_id"resp_0f0d9033ae07032d016a4fe47283088193bde4a1217321c449"
total_cost0.035925
cache_discount(null)
upstream_inference_cost0
provider_name"OpenAI"
response_cache_source_id(null)
data_region"global"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags5
adverbTagCount0
adverbTags(empty)
dialogueSentences53
tagDensity0.094
leniency0.189
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1643
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)
84.78% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1643
totalAiIsms5
found
0
word"electric"
count1
1
word"weight"
count1
2
word"flickered"
count1
3
word"pulse"
count1
4
word"measured"
count1
highlights
0"electric"
1"weight"
2"flickered"
3"pulse"
4"measured"
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
narrationSentences135
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount1
narrationSentences135
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)
analyzedSentences183
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen30
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1644
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions7
unquotedAttributions0
matches(empty)
36.19% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions64
wordCount1274
uniqueNames15
maxNameDensity2.28
worstName"Quinn"
maxWindowNameDensity3
worstWindowName"Quinn"
discoveredNames
Soho1
Harlow1
Quinn29
Raven2
Nest2
Herrera1
Tomás19
Underground1
London1
Camden1
Morris2
Saint1
Christopher1
Veil1
Market1
persons
0"Harlow"
1"Quinn"
2"Nest"
3"Herrera"
4"Tomás"
5"Underground"
6"Morris"
7"Saint"
8"Christopher"
places
0"Soho"
1"Raven"
2"London"
globalScore0.362
windowScore0.667
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences94
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1644
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences183
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs120
mean13.7
std14.49
cv1.058
sampleLengths
043
119
25
363
42
55
626
72
83
93
101
1147
124
135
1446
1528
165
177
187
1910
205
2115
225
235
245
2539
2629
271
286
2914
309
317
323
337
3411
356
3610
3730
3817
394
4032
415
4211
438
445
4531
463
477
4843
4915
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences135
matches
0"been smashed"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs209
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount1
semicolonCount0
flaggedSentences1
totalSentences183
ratio0.005
matches
0"CAMDEN TOWN STATION — CLOSED."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1273
adjectiveStacks0
stackExamples(empty)
adverbCount21
adverbRatio0.016496465043205028
lyAdverbCount2
lyAdverbRatio0.0015710919088766694
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences183
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences183
mean8.98
std5.85
cv0.651
sampleLengths
020
123
219
35
415
510
68
730
82
95
108
112
127
139
142
153
163
171
1810
197
206
2124
224
235
2423
257
2616
279
285
2914
305
317
327
3310
345
3515
365
375
385
3912
405
4112
4210
438
448
4513
461
476
4810
494
61.02% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats5
diversityRatio0.3825136612021858
totalSentences183
uniqueOpeners70
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount5
totalSentences125
matches
0"Then he ran."
1"Then Tomás drove his shoulder"
2"Then Tomás had arrived through"
3"Then metal shrieked somewhere far"
4"Even the strange little blue"
ratio0.04
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount20
totalSentences125
matches
0"Her leather watch slapped against"
1"He shouldered through a knot"
2"His voice came back from"
3"He righted himself and kept"
4"Her radio crackled."
5"She looked over her shoulder."
6"His voice sounded closer now."
7"It had held shelves of"
8"Her former partner, DS Morris,"
9"She saw him in fragments"
10"He turned and looked up"
11"Her hand went to the"
12"It began under her boots."
13"He reached into his pocket"
14"He pressed it into a"
15"His eyes reflected the market"
16"He had gone pale beneath"
17"His left forearm showed through"
18"His gaze fixed on the"
19"Her backup remained six minutes"
ratio0.16
48.00% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount103
totalSentences125
matches
0"Detective Harlow Quinn burst through"
1"Static swallowed the dispatcher’s reply."
2"The suspect clipped the corner"
3"Quinn drove after him, boots"
4"Her leather watch slapped against"
5"Midnight had passed, but the"
6"The hooded man looked back."
7"Quinn’s jaw tightened."
8"He shouldered through a knot"
9"Someone dropped a cigarette into"
10"Quinn slammed into the crowd"
11"Tomás vaulted a low chain"
12"Quinn followed, ducking under the"
13"Rainwater poured off the metal"
14"A stack of beer kegs"
15"Tomás had already cleared them."
16"Quinn planted one hand on"
17"His voice came back from"
18"Tomás tore around another corner."
19"Quinn followed him into a"
ratio0.824
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences125
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences52
technicalSentenceCount3
matches
0"Quinn had spent eighteen years building cases from what people left behind: fibres, phone records, lies that split under pressure."
1"Then metal shrieked somewhere far below, as if an old train dragged itself across tracks buried for decades."
2"Hundreds of tiny lamps burned below in colours that did not belong in any electrical fitting: violet, green, a gold that looked thick enough to pour."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags5
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags4
fancyCount0
fancyTags(empty)
dialogueSentences53
tagDensity0.075
leniency0.151
rawRatio0
effectiveRatio0
90.9996%