Run: 2026-09-23-01M37J06Z1WFF7RXMM7FBTK3WN

Run Details

Duration:
42.0s
Cost:
$0.023698
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
The man came out of the alley with blood on his sleeve and collided with a cyclist outside Camden Town station. The bicycle hit the kerb. Its rider went over the handlebars. By the time the man had found his footing, Detective Harlow Quinn had crossed the road. “Tomás Herrera. Stop.” Herrera looked back. Rain flattened his dark curls against his forehead. His hand went to the Saint Christopher medallion at his throat, then dropped to the front of his jacket. He ran. Quinn stepped around the fallen bicycle and gave chase. A bus swung between them, its tyres throwing gutter water across her trousers. She caught a glimpse of Herrera through the windows, weaving past a knot of people under the station awning. When the bus cleared, he had turned into Camden High Street. “Police. Make room.” Two men carrying takeaway boxes pressed themselves against a shutter. Quinn drove through the gap. Her shoes struck wet pavement and skidded on a crushed paper cup. Ahead, Herrera crossed against the lights. A taxi braked hard enough to sound its horn in one long blast. Quinn took a narrower crossing behind him, close enough to see the strip of pale skin along his left forearm. She knew that scar from the photographs pinned to her office wall. Former paramedic. Lost his licence. Treated people who refused hospitals and, if her source at the Raven’s Nest had told the truth, charged nothing when they came in close to death. Tonight he had come out of the bar’s hidden room carrying a canvas satchel. He had left without it. At the next junction, Herrera glanced over his shoulder. Quinn held up her warrant card. “I need to speak to you about the man at the Nest.” Herrera’s face changed. He knocked into a woman’s umbrella, caught its handle before it fell, and pushed on. “He was alive when I left,” he called. “Then help me find him.” A motorbike cut across Quinn’s path. She stopped short, let it pass and reached for her radio. “Control, Detective Quinn. Pursuing Tomás Herrera on foot, northbound Camden High Street. Olive skin, dark jacket, thirty. Possible connection to an assault in Soho.” Static broke across the reply. She caught her name, then nothing. The display showed a signal. She tried again. Herrera had left the crowded frontage. He cut through a side street where the shop lights gave way to brick walls, locked gates and overflowing bins. Quinn followed the sound of his shoes slapping through puddles. A chain-link fence ran along one side of the road, screening a strip of derelict railway land. At the far end, a work lamp shone above a boarded service entrance. Herrera reached it first. He leaned against the boards, breathing hard. “Don’t come down here.” Quinn slowed. Ten yards separated them. Water dripped off the end of his nose. Blood had soaked the cuff of his jacket and run over the old knife scar. “Show me your hands.” He raised one. In it lay a small white disc with a hole through its centre. Bone, Quinn thought. His other hand stayed pressed against his ribs. “You’re hurt. I can get you an ambulance.” “Send one to the Nest. The man upstairs needs it more.” “The man upstairs gave me your name.” Herrera shut his eyes for a beat. Behind him, a thin line of darkness appeared between two boards. Quinn heard metal scrape against metal. “What did he tell you?” “He told me you could explain what happened to him.” “He could still talk?” “He could when I left.” Herrera looked at the opening behind him. For the first time since the chase began, he seemed less concerned with Quinn than with reaching whatever lay below. “Go back and keep him talking. Ask what he bought.” “Bought where?” The gap widened. A slab of plywood swung inward on concealed hinges, revealing concrete steps under an old railway sign. The lettering had peeled away, but Quinn could make out the edge of a station roundel. Herrera slipped through. Quinn ran the last few yards. A figure stood beyond the opening, wrapped in a long oilskin coat. One hand caught Herrera’s elbow; the other took the white disc. The figure turned its head towards Quinn. Strips of cloth covered its nose and mouth. “Two of you?” it asked. “One,” Herrera answered. “Close it.” Quinn planted her palm against the plywood. The figure shoved from the other side. For a second the opening narrowed around Quinn’s arm, trapping the sleeve of her coat. She twisted free and forced her shoulder through. The board struck the wall behind her. The figure retreated down the stairs. Herrera stumbled after it. “Police,” Quinn called. “Stay where you are.” The figure stopped at the next landing. Quinn took two steps down, then saw three more shapes below it, standing in the dark between the railings. One carried a hooked pole. Another held a glass jar against its chest. Pale things moved inside the jar, striking the sides with small dry taps. Quinn kept her right hand near her cuffed holster. She had come in without a partner. Control had her last street-level position, if the radio transmission had reached anyone. Behind her, rain struck the pavement; ahead, the stairs dropped past the reach of the work lamp. Herrera gripped the railing. Blood ran between his fingers. He looked up at Quinn. “You can arrest me when I come back.” “What’s down there?” “A place where I can get something to keep him alive.” “Name it.” Herrera opened his mouth. The figure in oilskin lifted the bone token into the light. It showed a pattern of tiny punched holes. Its knuckles bent where knuckles did not belong. “The Veil Market,” Herrera told her. “Under the old station.” Quinn glanced at the figure’s hand. The cloth over its face rose and fell with a slow breath. Far below, voices travelled up the stairwell: a man calling prices, someone arguing in a language Quinn did not know, a woman laughing once before a gate crashed shut. Light flickered across the concrete walls, green, then amber. An old rail map hung at the landing, its glass painted over in black. Three years earlier, DS Morris had vanished at a sealed station entrance during a case that should have ended with a search warrant and two arrests. Quinn had stood on the other side of a steel door while he struck it from within. By the time the fire crew cut through, the room had been empty. She had kept the recording of his last radio call on a drive in her desk. Every review had given her the same burst of static and one breath. The figure in oilskin lowered the token towards a brass slot in the wall. “Is she coming?” it asked. “No,” Herrera answered. Quinn took another step down. “I’ll answer for myself.” Herrera swore under his breath. “You don’t have a token.” “I’m not buying anything.” “That won’t matter to the gate.” The oilskin figure pressed the token into the slot. Somewhere below, machinery clanked into motion. A barred gate at the foot of the flight began to rise, inch by inch. Quinn studied the three waiting figures. None had moved towards her. Herrera had reached the landing but stopped there, one arm folded tight across his ribs. Beneath his jacket, the wound must have opened during the run. He had enough strength to make it to the gate. She could turn back, call for officers and an ambulance, and put a car on every exit she could find. The man at the Nest might die before Herrera returned. The gate might shut with Herrera on the other side. Her radio crackled. “Quinn? Repeat your location.” She pulled it free. “Abandoned station access off the service road east of Camden High Street. Need medical response at the Raven’s Nest, Soho. Male victim with serious injuries. Send officers to my location.” “Received. Are you with the suspect?” Quinn looked at Herrera. He watched the gate rise. “Yes. I’m following him.” She clipped the radio back to her coat and descended. The figure with the jar stepped aside. As Quinn passed, one of the pale things inside opened a mouth against the glass. She kept her eyes on Herrera. At the bottom, the gate stood high enough for a person to duck beneath. Beyond it stretched a disused platform packed with stalls. Bare bulbs hung from pipes above tables covered in jars, knives, folded clothing and bundles of paper tied with red string. Rainwater ran down the tiled wall and collected along the platform edge. Across the tracks, a train carriage sat without wheels, its windows lit from inside. A woman at the nearest stall pulled a tarpaulin over her goods when she spotted Quinn’s warrant card. Two customers moved away from the gate. Neither wore a coat, though water dripped from the ceiling onto their bare shoulders. Herrera took three steps onto the platform and caught himself against a pillar. Quinn reached him before he slid down it. Blood covered her hand when she gripped his jacket. “You’re going to sit.” “Not here.” He pointed along the platform. “Blue awning. Woman with a silver tooth. Ask her for black salt and a sterile needle.” “For you?” “For the man in Soho. I’ve got what I need for this.” He pulled a packet of gauze from his pocket and pressed it into Quinn’s bloody hand. She looked down at it, then at the open wound beneath his sleeve. “Hold your arm out.” The figure in oilskin remained at the gate, watching them. Quinn tore the packet open with her teeth. Herrera braced his hand against the pillar while she pressed the gauze to his forearm. His jaw locked. “Tell me what happened at the Nest.” “He opened a bottle he bought down here. I told him not to break the seal.” “What was in it?” Herrera watched a thin trail of blood slip past Quinn’s thumb. “I didn’t see it. He dropped the bottle when it bit him.” From somewhere under the platform came the rumble of a train, though the tracks in front of Quinn lay rusted and broken. She kept pressure on the gauze. Herrera pointed again, past a row of hanging lamps to a patch of blue canvas. “Silver tooth,” he repeated. “You’ll see her behind the scales.” Quinn adjusted her grip and walked with him along the platform.
created_at"2026-09-23T16:36:23.145Z" 9/23/2026, 4:36:23 PM
model"openai/gpt-6-sol-20260922"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency2479
moderation_latency(null)
generation_time42005
tokens_prompt1104
tokens_completion2641
native_tokens_prompt984
native_tokens_completion2173
native_tokens_completion_images0
native_tokens_reasoning0
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"
usage0.023698
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.20.0; linux; x64))"
http_referer(null)
request_id"req-1790181383-jLwVoGzwNJzMfsh9Lykf"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1790181383-LjAR4xzDtlP6NcYQI7ej"
upstream_id"resp_0ca7d56b0cb91c7b016ab4000741b087d19595a74d9ced9687"
provider_responses
0
endpoint_id"1cfc7d9d-4404-4b8e-9ee4-58ae45c9dcd4"
id"resp_0ca7d56b0cb91c7b016ab4000741b087d19595a74d9ced9687"
is_byokfalse
latency841
model_permaslug"openai/gpt-6-sol-20260922"
provider_name"OpenAI"
status200
total_cost0.023698
cache_discount(null)
upstream_inference_cost0
provider_name"OpenAI"
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
totalTags9
adverbTagCount0
adverbTags(empty)
dialogueSentences50
tagDensity0.18
leniency0.36
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1753
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)
94.30% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1753
totalAiIsms2
found
0
word"charged"
count1
1
word"flickered"
count1
highlights
0"charged"
1"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
narrationSentences149
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount1
narrationSentences149
filterMatches(empty)
hedgeMatches
0"began to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences190
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
totalWords1753
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions13
unquotedAttributions0
matches(empty)
16.67% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions77
wordCount1434
uniqueNames14
maxNameDensity2.37
worstName"Quinn"
maxWindowNameDensity4.5
worstWindowName"Quinn"
discoveredNames
Camden2
Town1
Detective1
Harlow1
Quinn34
Saint1
Christopher1
Herrera27
High1
Street1
Raven1
Nest2
Morris1
Blood3
persons
0"Harlow"
1"Quinn"
2"Saint"
3"Christopher"
4"Herrera"
5"Nest"
6"Morris"
7"Blood"
places
0"Camden"
1"Town"
2"High"
3"Street"
4"Raven"
globalScore0.315
windowScore0.167
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences108
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1753
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences190
matches
0"knew that scar"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs86
mean20.38
std20.21
cv0.991
sampleLengths
048
13
232
352
43
546
663
719
815
912
1018
118
125
1317
1424
1519
1666
1711
184
1929
204
2127
228
2311
247
2524
265
2710
284
295
3027
3110
322
3339
3444
355
365
3744
3810
397
4052
4146
4214
438
443
4511
462
4731
4810
4970
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences149
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs248
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount2
flaggedSentences2
totalSentences190
ratio0.011
matches
0"One hand caught Herrera’s elbow; the other took the white disc."
1"Behind her, rain struck the pavement; ahead, the stairs dropped past the reach of the work lamp."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1436
adjectiveStacks0
stackExamples(empty)
adverbCount22
adverbRatio0.01532033426183844
lyAdverbCount1
lyAdverbRatio0.0006963788300835655
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences190
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences190
mean9.23
std5.7
cv0.617
sampleLengths
021
15
26
316
43
53
68
719
82
99
1013
1119
1211
133
1410
155
1612
176
1813
1920
2012
212
223
2326
2414
255
269
276
2812
293
3015
318
325
336
3411
3524
365
376
385
393
406
4120
4210
4317
4413
454
467
474
482
494
61.23% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats7
diversityRatio0.3894736842105263
totalSentences190
uniqueOpeners74
23.98% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences139
matches
0"Somewhere below, machinery clanked into"
ratio0.007
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount32
totalSentences139
matches
0"Its rider went over the"
1"His hand went to the"
2"She caught a glimpse of"
3"Her shoes struck wet pavement"
4"She knew that scar from"
5"He had left without it."
6"He knocked into a woman’s"
7"She stopped short, let it"
8"She caught her name, then"
9"She tried again."
10"He cut through a side"
11"He leaned against the boards,"
12"He raised one."
13"His other hand stayed pressed"
14"She twisted free and forced"
15"She had come in without"
16"He looked up at Quinn."
17"It showed a pattern of"
18"Its knuckles bent where knuckles"
19"She had kept the recording"
ratio0.23
71.51% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount108
totalSentences139
matches
0"The man came out of"
1"The bicycle hit the kerb."
2"Its rider went over the"
3"Herrera looked back."
4"Rain flattened his dark curls"
5"His hand went to the"
6"Quinn stepped around the fallen"
7"A bus swung between them,"
8"She caught a glimpse of"
9"Quinn drove through the gap."
10"Her shoes struck wet pavement"
11"A taxi braked hard enough"
12"Quinn took a narrower crossing"
13"She knew that scar from"
14"Tonight he had come out"
15"He had left without it."
16"Quinn held up her warrant"
17"Herrera’s face changed."
18"He knocked into a woman’s"
19"A motorbike cut across Quinn’s"
ratio0.777
71.94% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount2
totalSentences139
matches
0"By the time the man"
1"By the time the fire"
ratio0.014
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences66
technicalSentenceCount0
matches(empty)
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags9
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags8
fancyCount1
fancyTags
0"he repeated (repeat)"
dialogueSentences50
tagDensity0.16
leniency0.32
rawRatio0.125
effectiveRatio0.04
91.3208%