Run: 2026-09-23-01M37HK7DATK9SRYTZ80EC8REE

Run Details

Duration:
56.0s
Cost:
$0.028858
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 chewed the green neon above The Raven’s Nest into pieces. Detective Harlow Quinn pushed through the bar’s door and spotted Tomás Herrera crossing the Soho pavement, one hand clamped around the inside of his coat. “Herrera. Stop.” He glanced back. His Saint Christopher medallion flashed at his open collar. Then he ran. Quinn cleared a row of outdoor tables as he turned into a service lane. A chair struck her shin and spun beneath a parked van. She caught the van’s wing mirror, swung round it and kept moving. Herrera had spent twelve minutes answering questions in the Nest’s hidden back room. He had given her an address that didn’t exist and a phone number missing a digit. When she showed him the photograph of the police evidence envelope found in his medical bag, he had asked for water. By the time she reached the bar, he had shoved through the bookshelf and out into the rain. The envelope bore Morris’s case number. Herrera reached the end of the lane and cut across Wardour Street. A taxi’s horn cracked through the rain. Its bonnet blocked Quinn’s path; she slapped it as she passed and followed Herrera through a break in the traffic. “Police!” she called. “Move.” Two men outside a takeaway flattened themselves against the shopfront. Herrera knocked through the doorway of a corner shop, sending a tower of bottled water across the floor. Quinn took the pavement, gained the far door and caught sight of him through the glass. He saw her and changed course, hurdling the fallen bottles to leave by the entrance he had used. She stopped short. He burst out less than an arm’s length away. Quinn grabbed his coat. Fabric tore beneath her fingers. Herrera twisted, and the scar along his left forearm showed white against his olive skin. He struck her wrist with the edge of his hand. Her grip slipped; he backed into the street and ran north. A small disc dropped from his torn pocket and struck the pavement with a hard click. Quinn scooped it up. Bone, polished thin and pierced near one edge. A black crescent had been cut into its face. “Give that back.” Herrera had stopped beneath the shop awning, water dripping from his curls. His chest rose and fell. “You don’t know what it’s for.” “Come and explain it.” A bus pulled between them. Quinn moved before its rear wheels passed, but Herrera had crossed the road and reached the next corner. She shoved the bone disc into her coat pocket and went after him. By Tottenham Court Road, the cold had worked through her shirt. Herrera stayed ahead of her, slipping between late drinkers and delivery cyclists. He ran with his left arm tucked close to his ribs. Quinn’s own breath rasped against the traffic. He checked over his shoulder outside a shuttered electronics shop. Quinn pointed at him across the distance. “I saw the envelope.” Herrera stumbled, caught himself against a bollard and kept running. “Where did you get Morris’s file?” He turned down a narrow road without answering. Quinn followed, boots skidding on wet paving stones. A cyclist shouted as she crossed in front of his wheel. At a crossing farther north, Herrera caught the edge of a closing pedestrian signal. Quinn ran through the red. Headlights washed across her face. She braced a hand on the bonnet of a braking car, heard the driver hammer his horn and reached the other kerb. Herrera had gained half a street. He could have taken a cab. He passed three with their lights on. Whatever lay at the end of this route mattered more to him than distance. Her radio hissed when she pressed it. She gave her location, heard broken syllables beneath a rush of static, and tried again. “Suspect on foot. Northbound toward Camden. Male, twenty-nine, dark coat. I need a unit ahead of me.” “Repeat your—” The signal cut. Quinn let the radio fall against her coat. At the Nest she had watched Herrera’s face when she mentioned DS Morris. Not at the name; at the date. Three years had passed since Morris vanished from a locked building while Quinn guarded its only door. Herrera had looked down at the envelope before he looked at her. A black cab lurched from a side street ahead. Herrera ran behind it. Quinn reached the junction in time to see him disappear beyond the Euston Road, where buses and cars dragged ribbons of light across the wet tarmac. She searched the opposite pavement. A dark coat moved past a bus shelter. She took the underpass steps two at a time. Sour water collected on the lowest tread. At the far end she emerged beneath a hotel canopy, spotted Herrera reflected in a darkened window and turned left. “You’re not going to keep this up,” she called. “I’ll stop if you stop.” “Set the file down.” Herrera glanced back, his face tight beneath the streetlights. “It isn’t mine.” “Then whose is it?” He ducked through a knot of people waiting outside an all-night café. One of them caught Quinn’s sleeve. “Did he nick something?” “Let go.” The man released her. Herrera reached Camden High Street while Quinn was still clearing the group. The shopfronts grew darker. Rain ran over painted shutters and pooled around bags of rubbish left for morning collection. Herrera stopped at the entrance to a disused passage beside a boarded station building. A steel door stood inside the recess, its lower half brown with rust. Quinn slowed. Herrera put his shoulder to the door and vanished through it. The door swung back, leaving a narrow gap and a strip of weak amber light. She reached it with her baton out. Beyond lay stairs. Old tiles lined the walls, their grout black with damp. Voices rose from below: a burst of laughter, someone arguing over a price, metal ringing against metal. The smell changed as she stepped across the threshold. Wet concrete gave way to hot fat, incense and the sharp bite of antiseptic. Quinn checked her phone. One bar of signal fell to none. She sent a message with the location and Morris’s case number. It sat on the screen, unsent. At the foot of the stairs stood a gate made from sections of old Tube railing. A woman in a waxed apron watched the stairwell from a stool. She held a square of red cloth over one hand and a short-bladed knife in the other. Herrera reached her first. He fumbled in his coat and stopped. “Lost it,” the woman observed. “Let me through. I’ll pay inside.” “You know the price.” “I had one.” Herrera looked up. His eyes found Quinn on the stairs. The woman followed his gaze. “Your friend?” “No.” Quinn descended three steps. Through the railing she saw stalls packed along an old platform, their lamps reflecting in puddles between the rails. A man lifted a jar to inspect what moved against the glass. At the next stall, a customer drew back a sleeve to show the seller a wound with silver edges. The woman rose from her stool. “Token.” Quinn took the bone disc from her pocket. The woman’s eyes fixed on the black crescent. “That’s his,” Herrera told her. “It fell while he was running from me.” Quinn held it between two fingers. “Metropolitan Police. I’m taking him in.” The woman rested her knife on the gate latch. “Upstairs, you can call yourself what you like.” Herrera pressed a hand against the railing. “Harlow, go back.” Quinn’s grip tightened around the token. No signal. No unit at her shoulder. On the platform, people had begun to turn towards the gate. She held out the disc. The woman took it, checked the crescent with her thumb and pushed the latch free. Herrera slipped through at once. Quinn caught the gate before it closed and stepped onto the platform. A stallholder pulled his crates out of her path as she moved after Herrera.
created_at"2026-09-23T16:29:17.617Z" 9/23/2026, 4:29:17 PM
model"openai/gpt-6-sol-20260922"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency23372
moderation_latency(null)
generation_time55931
tokens_prompt1104
tokens_completion2006
native_tokens_prompt984
native_tokens_completion2689
native_tokens_completion_images0
native_tokens_reasoning1034
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"
usage0.028858
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.20.0; linux; x64))"
http_referer(null)
request_id"req-1790180957-ap3b3zIuLhhdxRfDVHhn"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1790180957-yYK3wPxNXmBTpzWvuTfc"
upstream_id"resp_04e85dcb780d78ee016ab3fe5dc5d487d193d9750e92436335"
provider_responses
0
endpoint_id"1cfc7d9d-4404-4b8e-9ee4-58ae45c9dcd4"
id"resp_04e85dcb780d78ee016ab3fe5dc5d487d193d9750e92436335"
is_byokfalse
latency1036
model_permaslug"openai/gpt-6-sol-20260922"
provider_name"OpenAI"
status200
total_cost0.028858
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
totalTags6
adverbTagCount0
adverbTags(empty)
dialogueSentences29
tagDensity0.207
leniency0.414
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1334
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)
96.25% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1334
totalAiIsms1
found
0
word"lurched"
count1
highlights
0"lurched"
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
narrationSentences123
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences123
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences146
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
totalWords1334
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions9
unquotedAttributions0
matches(empty)
42.83% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions75
wordCount1213
uniqueNames20
maxNameDensity2.14
worstName"Herrera"
maxWindowNameDensity3.5
worstWindowName"Herrera"
discoveredNames
Raven1
Nest3
Harlow1
Quinn24
Tomás1
Herrera26
Soho1
Saint1
Christopher1
Morris4
Wardour1
Street2
Two1
Tottenham1
Court1
Road2
Euston1
Camden1
High1
Tube1
persons
0"Raven"
1"Harlow"
2"Quinn"
3"Herrera"
4"Saint"
5"Christopher"
6"Morris"
7"Tube"
places
0"Nest"
1"Tomás"
2"Soho"
3"Wardour"
4"Street"
5"Tottenham"
6"Court"
7"Road"
8"Euston"
9"Camden"
10"High"
globalScore0.428
windowScore0.5
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences93
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1334
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences146
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs66
mean20.21
std17.32
cv0.857
sampleLengths
036
12
215
337
468
56
639
74
862
912
1045
1116
1221
1326
144
1536
1641
1717
184
1910
206
2127
2246
2333
2422
2517
262
2711
2849
2952
3036
319
325
334
3412
354
3618
374
382
3916
4046
4128
427
4353
4428
4545
4611
475
486
494
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences123
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs204
matches
0"was still clearing"
84.15% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount3
flaggedSentences3
totalSentences146
ratio0.021
matches
0"Its bonnet blocked Quinn’s path; she slapped it as she passed and followed Herrera through a break in the traffic."
1"Her grip slipped; he backed into the street and ran north."
2"Not at the name; at the date."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1215
adjectiveStacks0
stackExamples(empty)
adverbCount16
adverbRatio0.01316872427983539
lyAdverbCount1
lyAdverbRatio0.0008230452674897119
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences146
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences146
mean9.14
std5.37
cv0.588
sampleLengths
011
125
22
33
49
53
614
711
812
913
1016
1121
1218
136
1412
157
1620
173
181
1910
2018
2116
2218
233
249
254
265
2715
2810
2911
3016
314
328
339
3415
355
366
374
385
3918
4013
4111
4212
4311
447
4510
467
474
4810
496
56.85% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats2
diversityRatio0.3493150684931507
totalSentences146
uniqueOpeners51
28.01% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences119
matches
0"Then he ran."
ratio0.008
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount30
totalSentences119
matches
0"He glanced back."
1"His Saint Christopher medallion flashed"
2"She caught the van’s wing"
3"He had given her an"
4"Its bonnet blocked Quinn’s path;"
5"He saw her and changed"
6"She stopped short."
7"He burst out less than"
8"He struck her wrist with"
9"Her grip slipped; he backed"
10"His chest rose and fell."
11"She shoved the bone disc"
12"He ran with his left"
13"He checked over his shoulder"
14"He turned down a narrow"
15"She braced a hand on"
16"He could have taken a"
17"He passed three with their"
18"Her radio hissed when she"
19"She gave her location, heard"
ratio0.252
52.44% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount97
totalSentences119
matches
0"Rain chewed the green neon"
1"Detective Harlow Quinn pushed through"
2"He glanced back."
3"His Saint Christopher medallion flashed"
4"Quinn cleared a row of"
5"A chair struck her shin"
6"She caught the van’s wing"
7"Herrera had spent twelve minutes"
8"He had given her an"
9"The envelope bore Morris’s case"
10"Herrera reached the end of"
11"A taxi’s horn cracked through"
12"Its bonnet blocked Quinn’s path;"
13"Herrera knocked through the doorway"
14"Quinn took the pavement, gained"
15"He saw her and changed"
16"She stopped short."
17"He burst out less than"
18"Quinn grabbed his coat."
19"Fabric tore beneath her fingers."
ratio0.815
42.02% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount1
totalSentences119
matches
0"By the time she reached"
ratio0.008
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences57
technicalSentenceCount0
matches(empty)
41.67% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags6
uselessAdditionCount1
matches
0"Herrera had, water dripping from his curls"
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags4
fancyCount1
fancyTags
0"the woman observed (observe)"
dialogueSentences29
tagDensity0.138
leniency0.276
rawRatio0.25
effectiveRatio0.069
88.1403%