Run: 2026-07-09-01KX41DECXMJ6XT0K2CRN8N4V0

Run Details

Duration:
31.6s
Cost:
$0.038490
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 hard enough to flatten the cigarette ends in the gutters and turn the road into a black mirror. Detective Harlow Quinn ran through it with her coat open, one hand clamped around the radio at her shoulder, the other clearing bodies from the pavement. “North on Dean Street,” she barked. “Male, dark hood, grey courier bag. He’s carrying evidence from a homicide scene. Seal the next junction.” Static hissed in her ear. “Quinn, repeat. Your signal’s breaking—” The suspect cut across the road without looking. A taxi screamed to a stop, its tyres skidding in a fan of filthy water. The driver leaned on the horn and thrust a fist through his open window. “Watch where you’re—” The hooded man vanished into the gap between a florist’s shuttered frontage and a kebab shop with its lights still burning. Quinn followed. The alley stank of wet cardboard, hot grease and old beer. Her shoes struck puddles, each step sending cold water over the tops of her socks. A stack of plastic crates blocked the far end. The man vaulted them, his grey bag thumping against his hip. “Police! Stop!” He looked back. Not for long. Long enough. His hood had slipped. Pale skin. Shaved scalp. A burn puckered the left side of his neck, red and glossy beneath the rain. Quinn recognised him from the station photographs. Elias Venn. Dock runner. Fence. Witness in three investigations, missing in all three before anyone got him into an interview room. He twisted through a steel fire door at the end of the alley. Quinn hit the crates at speed, planted a foot on the lower stack and hauled herself over. The plastic bowed under her weight. A crate split with a crack behind her. The fire door swung shut. She rammed it with her shoulder. The corridor beyond belonged to some closed restaurant. Grease gleamed on white tiles. A row of aprons hung from hooks beside a metal sink. Venn crashed through the kitchen ahead, scattering pans from a drying rack. Quinn drove after him. “Elias! Put the bag down!” He snatched a cleaver from the counter as he passed, not to fight, but to chop at the chain looped around the rear exit. Once. Twice. The chain broke. The door flew outward. Quinn reached the exit in time to watch him race across the back yard toward the street. Her phone vibrated against her ribs. She ignored it. Outside, he slipped on the wet paving stones, caught himself on a wall, and flung something over his shoulder. Quinn ducked. A glass bottle shattered against the brick beside her head. The smell hit at once: sharp, metallic, like pennies soaked in vinegar. Smoke curled from the fragments. Her eyes watered. She dragged her sleeve across her mouth and pushed through it. Venn had reached Charing Cross Road. He ran south, weaving between late-night shoppers and umbrella clusters. Green neon bled across the rain from the Raven’s Nest sign, turning the pavement the colour of pond scum. The front door stood open. Warm light spilled onto the street. Venn went inside. Quinn slowed at the threshold, hand near her holster. The bar’s old maps and black-and-white photographs flashed around her beneath low amber bulbs. A dozen faces turned. Glasses stopped halfway to mouths. Behind the bar, Tomás Herrera held a white towel against a man’s forearm. Blood had soaked through it. His Saint Christopher medallion caught the light as he looked up. “Detective.” “Where did he go?” Tomás’s warm brown eyes moved to the back of the room. A narrow bookshelf stood against the far wall, packed with battered hardbacks and dusty atlases. “No one came through here.” “Don’t play games with me.” A wet footprint marked the floorboards. Then another. They led to the bookshelf. Tomás pressed harder on the wounded man’s arm when he shifted. “You brought a trail of uniforms last time you visited,” Tomás muttered. “Silas nearly threw you through his own front window.” “Elias Venn just ran into this building carrying material from a murder scene.” “Then you should have caught him before he chose this door.” Quinn crossed the room. The patrons watched her without blinking. A woman in a red velvet jacket drew a finger around the rim of her glass. A thin man with silver filings under his nails smiled into his drink. The bookshelf had no handle. No hinge. Quinn ran her palm along the wood, found a recessed brass stud shaped like a raven’s eye. Tomás made a small sound through his teeth. “Don’t.” The shelf swung inward. Cold air poured through the opening. Not cellar cold. The deep, mineral chill of earth that had never seen sunlight. Stone steps fell away beneath a bare bulb. Venn’s footsteps clattered somewhere below, fast and fading. Quinn took out her torch. Tomás left the injured man with a woman in a leather waistcoat and came around the bar. He grabbed Quinn’s sleeve. “You didn’t see him come in because he didn’t come in. He went where he wanted to go.” “Then he knew this place.” “Most people who knew this place had sense enough not to chase someone down those stairs.” “I’ve met your customers.” His fingers tightened. “You had no idea what you’d find.” “That’s never stopped me.” “It stopped Morris.” The bar emptied of sound. Quinn looked down at Tomás’s hand on her sleeve. His scar ran pale and raised along his left forearm where his coat had ridden back. “Take your hand off me.” Tomás did. His jaw worked once. “I treated men after they came back from the Market,” he said. “Some had bites. Some had burns that moved under the skin. One brought home a voice in his lungs. You wanted a warning. There it was.” Quinn held his gaze. “You knew DS Morris?” “Everyone who worked nights knew Morris.” “Did he come through this door?” Tomás looked at the steps. “He asked questions in places that ate questions.” Quinn’s radio crackled. “Detective Quinn? We’ve got officers at the junction. No sight of your suspect.” She pressed the transmit button. “Hold the perimeter. Venn entered a private property. I’m continuing on foot.” Tomás gave a humourless shake of his head. “Private property. That was one name for it.” Quinn stepped into the stairwell. The bookshelf shut behind her with a soft wooden knock. The steps narrowed after the first turn. Rainwater dripped from Quinn’s hair onto the stone. The air smelled of rust, damp wool and something sweet that sat wrong at the back of her throat. Posters peeled from the walls, their faces long faded. A paper strip flapped beneath her torch beam. CAMDEN TOWN, it read. The old Underground station had been closed for decades. Quinn knew the history: rerouted platforms, fire damage, sealed tunnels. London had buried half its bones under development and called the rest heritage. Venn’s bag scraped a wall somewhere below. She moved faster. At the base of the stairs, an iron gate filled the passage. A ticket barrier stood beyond it, its metal arms twisted into knots. On the other side, a broad platform stretched beneath dead advertising boards. Lamps hung from cables, each globe glowing blue-white. The place had no business being alive. Stalls crowded the platform from end to end. Tarpaulins formed crooked roofs. Candles burned in jars filled with black fluid. A woman sold bottled teeth arranged by size. A boy in a school blazer stirred a pot of pale worms with a spoon. Men and women haggled over knives, jars, locks of hair, feathers too large for any British bird. The noise reached Quinn in layers: low voices, coins clinking, a child laughing, a train screeching through a tunnel where no rails remained. Venn stood twenty yards away, near a ticket booth lit from within by green fire. He had one hand buried in the grey courier bag. In the other, he held a small white disc between finger and thumb. Bone. He pushed it through a slot in the gate. The iron bars shuddered apart. A figure sat in the ticket booth, hidden behind a curtain of yellowed beads. Its hand appeared beneath the counter. The fingers bent backwards at every joint. “Payment,” it rasped. Venn slapped a bundle of notes onto the counter. The hand ignored the money. Venn’s shoulders rose. He dug into the bag and produced a glass evidence vial. Inside, something dark floated in clear fluid. A sliver of skin, perhaps. No. It moved against the glass. The hand closed around the vial. The gate opened wider. Quinn pressed herself into the stairwell’s shadow as Venn passed through. He glanced back once, rainwater still running from the edge of his hood. Then the crowd swallowed him. The gate began to close. Quinn reached for her radio. No signal. The display had gone blank. Her leather watch ticked on her left wrist. The sound carried in the stone passage. Behind the gate, the booth creature shifted beneath its beads. “Visitor,” it called. Every face near the entrance turned toward her. Quinn stepped into the blue light. “I’m police.” A woman at the nearest stall laughed, showing teeth filed into points. The creature’s backward hand slid across the counter. “Bone token.” “I don’t have one.” “Then you didn’t enter.” The gate ground shut another inch. Quinn looked through the narrowing gap. Venn’s grey bag surfaced between two stalls, moving toward the dark mouth of a tunnel. He had reached the edge of the market. In seconds, he would disappear again. Tomás’s warning sat in her head. Burns that moved. A voice in a man’s lungs. Morris asking questions in places that ate them. Quinn took her warrant card from inside her coat and held it up to the booth. “Harlow Quinn. Metropolitan Police. I’m pursuing a suspect wanted in connection with murder. Open that gate.” The creature’s hand remained flat on the counter. “Your dead carried paper too.” Quinn’s jaw tightened. “What did you call him?” The beads swayed. Something behind them breathed, wet and slow. “Bone token,” it repeated. Quinn looked down at the rain-slick floor. Near the wall, half-hidden under a torn Underground map, lay a pale chip no bigger than a pound coin. She crouched and picked it up. It felt warm in her palm.
created_at"2026-07-09T18:14:36.713Z" 7/9/2026, 6:14:36 PM
model"openai/gpt-5.6-terra-20260709"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency2255
moderation_latency(null)
generation_time31365
tokens_prompt1140
tokens_completion2936
native_tokens_prompt984
native_tokens_completion2402
native_tokens_completion_images0
native_tokens_reasoning182
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"
usage0.03849
router(null)
provider_responses
0
endpoint_id"3cc89000-ae34-4dde-9c7d-5ce009c8af0b"
id"resp_040d492e97e604f7016a4fe50cd71c819392419efc99677d56"
is_byokfalse
latency240
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-1783620876-FmwiXaQNU80dPPxMcd68"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1783620876-w8KoN8s6gYwkjRYowexD"
upstream_id"resp_040d492e97e604f7016a4fe50cd71c819392419efc99677d56"
total_cost0.03849
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
totalTags6
adverbTagCount0
adverbTags(empty)
dialogueSentences42
tagDensity0.143
leniency0.286
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1724
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)
79.70% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1724
totalAiIsms7
found
0
word"weight"
count1
1
word"race"
count1
2
word"vibrated"
count1
3
word"shattered"
count1
4
word"velvet"
count1
5
word"chill"
count1
6
word"footsteps"
count1
highlights
0"weight"
1"race"
2"vibrated"
3"shattered"
4"velvet"
5"chill"
6"footsteps"
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
narrationSentences179
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount1
narrationSentences179
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)
analyzedSentences215
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen26
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1724
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions11
unquotedAttributions0
matches(empty)
33.33% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions62
wordCount1432
uniqueNames16
maxNameDensity1.82
worstName"Quinn"
maxWindowNameDensity4
worstWindowName"Quinn"
discoveredNames
Soho1
Harlow1
Quinn26
Static1
Venn11
Charing1
Cross1
Road1
Raven1
Nest1
Tomás11
Herrera1
Saint1
Christopher1
Underground2
British1
persons
0"Harlow"
1"Quinn"
2"Static"
3"Venn"
4"Tomás"
5"Herrera"
6"Saint"
7"Christopher"
places
0"Soho"
1"Charing"
2"Cross"
3"Road"
4"Raven"
globalScore0.592
windowScore0.333
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences109
glossingSentenceCount1
matches
0"appeared beneath the counter"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches1
per1kWords0.58
wordCount1724
matches
0"not to fight, but to chop at the chain looped around the rear exit"
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences215
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs128
mean13.47
std12.58
cv0.934
sampleLengths
047
123
25
35
437
53
621
72
846
92
103
115
1230
1321
1413
1531
165
176
1836
194
205
2133
2217
239
2419
252
2627
2714
2835
2911
303
3132
3229
331
344
3526
365
375
3813
3911
4021
4113
4211
4339
4424
458
461
474
4836
495
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences179
matches
0"been closed"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs232
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences215
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1438
adjectiveStacks0
stackExamples(empty)
adverbCount30
adverbRatio0.02086230876216968
lyAdverbCount0
lyAdverbRatio0
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences215
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences215
mean8.02
std5.2
cv0.648
sampleLengths
021
126
26
317
45
55
68
715
814
93
1021
112
1211
1315
149
1511
162
173
183
192
204
212
222
2315
247
252
262
271
2816
2913
3017
316
328
335
346
358
365
3711
3812
394
405
4124
421
431
443
454
4617
476
483
4919
58.14% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats5
diversityRatio0.3627906976744186
totalSentences215
uniqueOpeners78
21.23% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences157
matches
0"Then the crowd swallowed him."
ratio0.006
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount29
totalSentences157
matches
0"Her shoes struck puddles, each"
1"He looked back."
2"His hood had slipped."
3"He twisted through a steel"
4"She rammed it with her"
5"He snatched a cleaver from"
6"Her phone vibrated against her"
7"She ignored it."
8"Her eyes watered."
9"She dragged her sleeve across"
10"He ran south, weaving between"
11"His Saint Christopher medallion caught"
12"They led to the bookshelf."
13"He grabbed Quinn’s sleeve."
14"His fingers tightened."
15"His scar ran pale and"
16"His jaw worked once."
17"She pressed the transmit button."
18"She moved faster."
19"He had one hand buried"
ratio0.185
17.32% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount139
totalSentences157
matches
0"Detective Harlow Quinn ran through"
1"Static hissed in her ear."
2"The suspect cut across the"
3"A taxi screamed to a"
4"The driver leaned on the"
5"The hooded man vanished into"
6"The alley stank of wet"
7"Her shoes struck puddles, each"
8"A stack of plastic crates"
9"The man vaulted them, his"
10"He looked back."
11"His hood had slipped."
12"A burn puckered the left"
13"Quinn recognised him from the"
14"He twisted through a steel"
15"Quinn hit the crates at"
16"The plastic bowed under her"
17"A crate split with a"
18"The fire door swung shut."
19"She rammed it with her"
ratio0.885
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences157
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences56
technicalSentenceCount0
matches(empty)
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags6
uselessAdditionCount0
matches(empty)
78.57% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags5
fancyCount3
fancyTags
0"she barked (bark)"
1"Tomás muttered (mutter)"
2"it repeated (repeat)"
dialogueSentences42
tagDensity0.119
leniency0.238
rawRatio0.6
effectiveRatio0.143
86.2766%