Run: 2026-09-23-01M37HJH1JNP9YPX9N23G4BQGR

Run Details

Duration:
42.3s
Cost:
$0.022048
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 chased the blood along the kerb. Detective Harlow Quinn followed it past a shuttered chemist, across a service road, and into the wheels of a night bus that cut between her and Tomás Herrera. “Police! Stop!” Herrera looked back. His short curls clung to his forehead, and one hand pressed a canvas satchel against his ribs. He crossed in front of the bus with inches to spare. The driver hit the horn. Quinn caught the rail beside the rear doors as the bus passed, then let go and ran. Her shoes struck wet pavement. Ahead, Herrera shoved through the queue outside a chicken shop. A man with a paper bag stumbled against the window and swore after him. Quinn slipped between two parked cars, ignored the burn in her lungs, and checked the time on the worn leather watch strapped to her left wrist. 11:47. She had watched Herrera enter a Camden lock-up eleven minutes ago. Two men had brought out a third on a stretcher beneath a tarpaulin. When she had approached, the man under the sheet had grabbed her wrist. Then the lights in the lock-up had gone out. Herrera had come through the back door carrying that satchel. Quinn had followed him into the rain. He reached the corner and turned into a lane behind the market stalls. Their canvas roofs bellied with trapped water. Wind snapped a loose strip of tarpaulin against a metal frame. “Tomás. I saw the stretcher.” Herrera stopped beneath a strip light that hummed over a loading bay. Water ran down his olive face. His fingers clenched around the satchel strap. “Then get back to your car.” “Who was on it?” “You don’t want him in a hospital.” “I want to hear him say that himself.” Herrera glanced past her shoulder. Quinn shifted her weight, ready for him to bolt, but he kept looking at the mouth of the lane. A pair of headlights passed without turning in. “Put the bag down.” “You can arrest me after he’s breathing.” He ran. Quinn lunged and caught his coat. Cloth tore between her fingers. Herrera swung the satchel at her head. She ducked, drove a shoulder into him, and felt the impact carry them both against a stall shutter. His left forearm struck the corrugated metal. A pale knife scar flashed beneath his soaked sleeve. Something small and hard fell from his coat and skipped over the pavement. Herrera looked at it. Quinn saw his expression change before he kicked away from the shutter. She caught the object beneath her shoe. A disk of bone, thin as a coin, with a hole bored through its centre. A crooked line cut across one face. She picked it up. By then Herrera had cleared the end of the lane. “Police! Move!” Two teenagers flattened themselves against a wall as she passed. One stared at the bone disk in her hand. Herrera crossed Camden High Street against the lights. A taxi braked and slewed in the rain; its driver hammered the horn. Quinn passed behind the boot, close enough to feel the hot exhaust through her trousers, and reached the opposite pavement as Herrera ducked beneath the railway arches. She knew the streets above ground. She knew the council maps, the CCTV blind spots, the passages that ended at brick. None showed a route through the boarded entrance ahead of him. Herrera had been in the file for months. Former paramedic. Lost his licence after treating patients he refused to identify. Quinn had seen him twice outside the Raven’s Nest in Soho, beneath its green neon sign, carrying bags that looked heavy when he went in and light when he came out. Each time she had tried to get closer, someone had stepped between her and the door. Now he reached the boards across an old station entrance. He pulled at a gap near the bottom. One sheet of plywood swung in on hidden hinges. Quinn drew her torch. Behind the gap, tiled steps descended into dark. “Herrera!” He turned, framed by broken posters and black tile. The chain at his throat had slipped outside his shirt. A Saint Christopher medallion shone against wet cotton. “Give me the token and go home.” Quinn held up the bone disk. “What’s downstairs?” Herrera opened his mouth. Footsteps sounded within the station, several sets, coming up. He looked over his shoulder and vanished down the steps. Quinn crossed the pavement and stopped at the entrance. Rain tapped against the plywood at her back. Her radio gave a burst of static when she pressed the transmit button. “Control, this is Quinn. Suspect entered a disused Tube station off Camden High Street, north side of the railway arches. Request backup.” Static answered. She tried again. The speaker made a sound like someone dragging a chair over stone, then fell silent. Down the stairwell, Herrera’s shoes struck tile. Other footsteps met his. A low voice spoke, and something grated shut. Quinn’s thumb rested on the edge of the disk. It had warmed in her palm. Three years earlier, DS Morris had called her from an address that did not exist on any map. His last words had come through broken reception: Don’t let them close it. When Quinn had reached the place, she had found a solid wall and his blood on the pavement beside it. She pushed the memory aside and went down six steps. The smell changed. Rain and diesel gave way to hot metal, old dust, and a sharp medicinal scent. Water ran in threads along the grout. Her torch picked out a row of cream tiles, an enamel sign with half its letters scraped off, and fresh muddy prints that ended at a locked iron gate. Herrera stood on the other side. A woman in a red rain cape blocked the way before him. She wore rings on every finger and held out one bare hand. “I had one.” “You came through without it.” Her voice carried up the stairwell. “That’s your problem.” Herrera shifted the satchel against his chest. “There’s a patient waiting.” “Then pay for another token.” Quinn descended the last steps. The woman saw her and reached beneath her cape. “Keep your hand where I can see it.” Quinn held up her warrant card. Neither moved. Behind them, beyond the gate, a train platform glowed with gas lamps and strings of bare bulbs. Stalls crowded the yellow safety line. A butcher’s hook held bunches of dried roots wrapped in blue thread. Glass jars filled a table across from it; one contained a folded paper bird that beat its wings against the lid. People turned towards the gate. Some wore coats and carried umbrellas. One man stood with his back to Quinn, a scarf wrapped around his neck despite the heat. Beneath the scarf, something shifted against the cloth. The woman’s gaze dropped to the bone disk. “You found his.” “I took it off the street. Open the gate.” “Police have no business here.” “A man came into that lock-up on a stretcher. I need to find out what happened to him.” Herrera gripped the bars. “He needs what I’m carrying.” “Then you can take me to him.” The woman in the cape gave a short laugh. She stopped when a crash sounded from deeper along the platform. Heads turned. Someone shouted for room. Quinn looked past Herrera. At the far end of the station, under a sign that read NORTHBOUND, two figures pushed a stretcher between the stalls. Its wheels rattled over the platform edge strips. A blanket covered the patient from feet to chin. One arm hung over the side, fingers dragging across the tile. That hand caught a stall leg. The stretcher stopped. Herrera pulled at the gate. “Open it.” The woman held out her palm to Quinn. The bone disk sat against the lines of Quinn’s hand. She could stay on the steps, keep Herrera in sight through the bars, and wait for the radio to work. Below, the figures pried the patient’s fingers from the stall leg. His arm dropped and swung as they pushed on. Quinn placed the disk in the woman’s palm. The woman fitted it into a slot beside the lock. Metal clicked through the gate’s frame. She drew it open just wide enough for one person. Quinn stepped through. Herrera moved towards the stretcher, and she followed him into the crowd.
created_at"2026-09-23T16:28:54.717Z" 9/23/2026, 4:28:54 PM
model"openai/gpt-6-sol-20260922"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency8559
moderation_latency(null)
generation_time42261
tokens_prompt1104
tokens_completion2076
native_tokens_prompt984
native_tokens_completion2008
native_tokens_completion_images0
native_tokens_reasoning279
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"
usage0.022048
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.20.0; linux; x64))"
http_referer(null)
request_id"req-1790180934-HBnGalkToZAQDbnrOdKf"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1790180934-s3irUcrPh1ZF5Vc8XXXg"
upstream_id"resp_0f03032b7d3e66ce016ab3fe46d19487d1b2c2b5b76019f74f"
provider_responses
0
endpoint_id"1cfc7d9d-4404-4b8e-9ee4-58ae45c9dcd4"
id"resp_0f03032b7d3e66ce016ab3fe46d19487d1b2c2b5b76019f74f"
is_byokfalse
latency1127
model_permaslug"openai/gpt-6-sol-20260922"
provider_name"OpenAI"
status200
total_cost0.022048
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
totalTags2
adverbTagCount0
adverbTags(empty)
dialogueSentences26
tagDensity0.077
leniency0.154
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1404
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)
89.32% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1404
totalAiIsms3
found
0
word"weight"
count1
1
word"footsteps"
count2
highlights
0"weight"
1"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
narrationSentences128
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount1
narrationSentences128
filterMatches
0"watch"
hedgeMatches
0"tried to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences152
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen31
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1404
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions2
unquotedAttributions0
matches(empty)
62.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions64
wordCount1250
uniqueNames16
maxNameDensity1.76
worstName"Quinn"
maxWindowNameDensity2.5
worstWindowName"Quinn"
discoveredNames
Harlow1
Quinn22
Tomás1
Herrera22
Camden2
High1
Street1
Raven1
Nest1
Soho1
Saint1
Christopher1
Morris1
Don1
Rain3
One4
persons
0"Harlow"
1"Quinn"
2"Tomás"
3"Herrera"
4"Saint"
5"Christopher"
6"Morris"
7"One"
places
0"Camden"
1"High"
2"Street"
3"Raven"
4"Soho"
globalScore0.62
windowScore0.833
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences102
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1404
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences152
matches
0"carrying that satchel"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs73
mean19.23
std17.09
cv0.888
sampleLengths
035
12
253
355
438
59
617
731
85
925
106
114
127
138
1424
158
164
177
182
1952
2013
2116
2243
232
2419
2548
2632
2767
2827
2912
301
3127
327
336
342
3523
369
3721
3822
3920
4019
4115
4251
4310
4454
4530
463
4714
487
494
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences128
matches(empty)
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
emDashCount0
semicolonCount2
flaggedSentences2
totalSentences152
ratio0.013
matches
0"A taxi braked and slewed in the rain; its driver hammered the horn."
1"Glass jars filled a table across from it; one contained a folded paper bird that beat its wings against the lid."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1252
adjectiveStacks0
stackExamples(empty)
adverbCount14
adverbRatio0.011182108626198083
lyAdverbCount0
lyAdverbRatio0
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences152
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences152
mean9.24
std5.78
cv0.626
sampleLengths
07
128
22
33
417
511
65
717
85
910
1014
1126
121
1311
1413
1513
169
1710
187
1913
207
2111
225
2312
246
257
266
274
287
298
305
3119
328
334
347
352
366
375
387
3918
407
419
4213
434
4412
457
4615
477
484
4910
62.03% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats3
diversityRatio0.3841059602649007
totalSentences151
uniqueOpeners58
27.32% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences122
matches
0"Then the lights in the"
ratio0.008
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount29
totalSentences122
matches
0"His short curls clung to"
1"He crossed in front of"
2"Her shoes struck wet pavement."
3"She had watched Herrera enter"
4"He reached the corner and"
5"Their canvas roofs bellied with"
6"His fingers clenched around the"
7"She ducked, drove a shoulder"
8"His left forearm struck the"
9"She caught the object beneath"
10"She picked it up."
11"She knew the streets above"
12"She knew the council maps,"
13"He pulled at a gap"
14"He turned, framed by broken"
15"He looked over his shoulder"
16"Her radio gave a burst"
17"She tried again."
18"It had warmed in her"
19"His last words had come"
ratio0.238
50.16% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount100
totalSentences122
matches
0"Rain chased the blood along"
1"Detective Harlow Quinn followed it"
2"Herrera looked back."
3"His short curls clung to"
4"He crossed in front of"
5"The driver hit the horn."
6"Quinn caught the rail beside"
7"Her shoes struck wet pavement."
8"A man with a paper"
9"Quinn slipped between two parked"
10"She had watched Herrera enter"
11"Herrera had come through the"
12"Quinn had followed him into"
13"He reached the corner and"
14"Their canvas roofs bellied with"
15"Wind snapped a loose strip"
16"Herrera stopped beneath a strip"
17"Water ran down his olive"
18"His fingers clenched around the"
19"Herrera glanced past her shoulder."
ratio0.82
40.98% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount1
totalSentences122
matches
0"Now he reached the boards"
ratio0.008
90.91% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences55
technicalSentenceCount4
matches
0"She knew the council maps, the CCTV blind spots, the passages that ended at brick."
1"Quinn had seen him twice outside the Raven’s Nest in Soho, beneath its green neon sign, carrying bags that looked heavy when he went in and light when he came o…"
2"Her torch picked out a row of cream tiles, an enamel sign with half its letters scraped off, and fresh muddy prints that ended at a locked iron gate."
3"At the far end of the station, under a sign that read NORTHBOUND, two figures pushed a stretcher between the stalls."
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
totalTags0
90.7575%