Run: 2026-09-23-01M36NZ8RW5M54Z4VNS89DNW5R

Run Details

Duration:
31.8s
Cost:
$0.001137
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 drove Detective Harlow Quinn off the pavement and into the road. A taxi clipped the puddle beside her. Water slapped her coat to the knee. She kept her eyes on the man ahead, a dark shape cutting between headlights, one hand clamped to the strap of a canvas bag. “Police! Stop!” He glanced back. A streetlamp caught his face: pale, narrow, familiar from the grainy photograph on Quinn’s desk. Martin Kells. Three months of missed interviews and false addresses, and now he was running with a bag that knocked against his hip like it held tools. Kells turned into an alley. Quinn followed. The lane stank of wet brick and old beer. A delivery van blocked half the way through. Kells vaulted its bonnet, slid, caught himself on the windscreen wiper and kept moving. Quinn went over the side, boots skidding on the slick pavement. Her left wrist struck the van’s mirror. The leather strap of her watch bit into her skin. She kept pace. At the end of the alley, Kells burst onto a busier street. A night bus groaned past, blotting him from view. Quinn cut behind it, palm raised against its exhaust, and saw his coat vanish through the crowd beyond. “Move.” People turned. A couple with takeaway cartons stepped apart. Quinn pushed through, shoulder first, without losing sight of him. Kells ran past the green neon sign above the Raven’s Nest. Its glow marked the rain in thin, sharp strokes. Inside the bar, old maps and black-and-white photographs crowded the walls. Through the glass, Quinn caught a glimpse of the bartender lifting his head as she passed. She had interviewed him twice. He’d claimed not to know Kells. That claim had lasted until tonight. Kells ducked into a narrow passage beside the bar. Quinn reached the entrance in time to see him shove through a metal door marked PRIVATE. “Quinn! Wait up!” A uniformed constable came pounding from the far end of the street, his cap pulled low. Quinn gave him a flat-handed stop. “Keep the street clear. Call it in, then hold this door.” “Do you want me inside?” “No. Stay here.” The constable looked at the door, then at her. “You’re going in alone?” “I’m not letting him disappear.” Quinn drew her baton and eased the door open. The stairs beyond dropped steeply into darkness. A bulb at the landing flickered over damp stone. Kells’s footsteps rang below. Quinn descended with one hand on the rail, baton low in the other. The air cooled as she went. At the bottom, the steps ended at a disused platform. Old tiles bore the ghost of a station name, scraped down to fragments. A row of dead lamps stretched into the dark. Somewhere beyond them, metal wheels squealed against a track. She stopped. No trains ran beneath this part of the city. Not anymore. Kells crossed the platform toward a tunnel entrance sealed by a gate. He had stopped running. He stood with his back to her, shoulders rising and falling, and held something small in his bare hand. “Martin Kells.” He turned. Rainwater shone on his hair and ran down the side of his face. “You brought the police,” he replied. “I am the police.” “That’s what I mean.” Quinn stepped onto the platform. Her boots sent a thin echo down the tunnel. “Put the bag down. Keep your hands where I can see them.” Kells looked past her, toward the stairs. “Your friend isn’t coming down.” “He’s not my friend.” “Then you won’t mind if I go.” Quinn lifted the baton. “Bag. Ground.” Kells let it fall. The canvas struck the tiles with a wet, heavy thud. He held up the object in his palm: a short piece of pale bone, drilled through one end and bound with copper wire. Quinn’s gaze moved from the token to his face. “What is that?” “A way through.” “You’re going to need more than a bit of bone.” Kells pressed the token against the gate. The metal shivered. A latch snapped open on the other side. Quinn did not move. The gate swung inward without a hand touching it. Beyond lay a narrow passage, its walls lined with glazed tiles. Warm light pooled at the far end, and a murmur rose from it: dozens of voices layered together, too clear to belong beneath a sealed station. Kells stepped through. Quinn crossed the distance in three strides and caught his coat. He twisted out of her grip with a sharp jerk, leaving the fabric in her fist. His shoulder struck the wall. The bag lay between them. “Don’t,” he warned. Quinn pressed him against the tiles. “Hands behind your back.” His eyes fixed on the baton. “You don’t know where you are.” “I know you’re under arrest.” “You’ll get yourself killed for a man who stole a box.” “What was in it?” Kells laughed once. The sound carried down the passage and came back altered, stretched thin. “Ask the people who paid me.” The gate began to swing shut. Quinn shoved him back, caught the edge with her boot, and reached for his wrist. He slipped away, ducked through the gap, and ran towards the warm light. She grabbed the fallen token. It was cold enough to sting her palm. Then the gate slammed against her boot. Pain shot through her toes. She wedged the baton between the bars and forced it open. The passage smelled of soot, wet wool and something coppery. Voices sharpened as she followed them. At the end, the tiles opened onto a broad platform crowded with stalls. Quinn stopped at the threshold. The market spread through the old station, beneath arches furred with mineral stains. Strands of amber bulbs hung from iron beams. Vendors occupied folding tables, battered suitcases and counters built from railway sleepers. Glass jars held things that twitched when the light passed over them. A woman in a paper mask traded silver coins for a bundle wrapped in black cloth. Two men argued over a bottle that glowed at its neck. Above the platform, a station clock had no hands. A pair of guards stood beside the entrance. One wore a long oilskin coat, the other a waistcoat over a bare chest. Neither carried a visible weapon. Both watched the bone token in Quinn’s fist. Kells vanished into the moving crowd. She could retreat. The constable waited above. She could call for backup, secure the entrance and put a perimeter around the station. But Kells would have time to lose himself among the stalls—or use that passage to reach another exit. Her phone showed no signal. The market’s noise swallowed the scrape of her boots. One of the guards held out his hand. “Token.” “I’m following a suspect.” “Token.” Quinn turned her shoulder to him and scanned the crowd. A flash of Kells’s dark coat appeared between two stalls, then disappeared behind a rack of hanging keys. The guard’s hand remained out. “Police,” Quinn told him. The man glanced at her badge. His face did not change. “Then you should know better than to come without a pass.” Quinn’s thumb pressed into the bone. The drilled hole had a dark ring inside it. Not dirt. Something had dried there. Behind her, the gate rattled in its frame. The sound came from the passage, though the platform beyond it held no wind. She looked back once. The stairs waited in the dark. A call to the constable could bring uniforms down. It could also bring them into a place where ordinary rules had already failed at the gate. Kells shoved through a gap between two stalls. A vendor cursed as he knocked a tray of glass vials to the floor. Their contents flashed blue across the tiles. Quinn closed her fingers around the token. “Move,” she told the guard. The guard lowered his hand. “You’ll pay the entry fee.” “Put it on my tab.” He stepped aside. Quinn pushed into the market. The crowd closed around her, shoulders brushing her coat, voices clipping her from both sides. She kept one hand on her badge and the other on her baton, tracking the dark coat as it slipped deeper between the stalls.
created_at"2026-09-23T08:26:32.099Z" 9/23/2026, 8:26:32 AM
model"openai/gpt-6-luna-20260922"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency8600
moderation_latency(null)
generation_time31815
tokens_prompt1104
tokens_completion2063
native_tokens_prompt984
native_tokens_completion2077
native_tokens_completion_images0
native_tokens_reasoning345
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"
usage0.0011369
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.20.0; linux; x64))"
http_referer(null)
request_id"req-1790151992-eK8gn0269OYJdEWyBvpQ"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1790151992-HmZ6M0o6zfcayV5Y026m"
upstream_id"resp_0053b23df6090fbd016ab38d3833b087d187dd2e127b80fdd6"
provider_responses
0
endpoint_id"05e94e02-b9c2-4bbb-ba55-4082ee9ad687"
id"resp_0053b23df6090fbd016ab38d3833b087d187dd2e127b80fdd6"
is_byokfalse
latency536
model_permaslug"openai/gpt-6-luna-20260922"
provider_name"OpenAI"
status200
total_cost0.0011369
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
totalTags4
adverbTagCount0
adverbTags(empty)
dialogueSentences35
tagDensity0.114
leniency0.229
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1371
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)
78.12% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1371
totalAiIsms6
found
0
word"familiar"
count1
1
word"pounding"
count1
2
word"flickered"
count1
3
word"footsteps"
count1
4
word"echo"
count1
5
word"scanned"
count1
highlights
0"familiar"
1"pounding"
2"flickered"
3"footsteps"
4"echo"
5"scanned"
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
narrationSentences136
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount1
narrationSentences136
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)
analyzedSentences167
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
totalWords1370
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions7
unquotedAttributions0
matches(empty)
46.69% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions47
wordCount1210
uniqueNames6
maxNameDensity2.07
worstName"Quinn"
maxWindowNameDensity3.5
worstWindowName"Quinn"
discoveredNames
Detective1
Harlow1
Quinn25
Kells18
Raven1
Nest1
persons
0"Harlow"
1"Quinn"
2"Kells"
3"Raven"
places(empty)
globalScore0.467
windowScore0.5
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences96
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1370
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences167
matches
0"use that passage"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs88
mean15.57
std16.16
cv1.038
sampleLengths
012
138
22
345
45
52
659
73
839
91
1019
1158
126
1325
143
1522
1611
175
183
1913
205
219
2220
2360
242
2511
2635
272
2815
296
304
314
3226
337
345
354
367
376
3837
399
403
413
4210
437
4411
454
4646
473
4837
493
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences136
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs210
matches
0"was running"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount1
semicolonCount0
flaggedSentences1
totalSentences167
ratio0.006
matches
0"But Kells would have time to lose himself among the stalls—or use that passage to reach another exit."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1214
adjectiveStacks0
stackExamples(empty)
adverbCount19
adverbRatio0.015650741350906095
lyAdverbCount1
lyAdverbRatio0.0008237232289950577
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences167
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences167
mean8.2
std5.03
cv0.613
sampleLengths
012
17
27
324
42
53
615
72
825
95
102
119
128
1314
1411
157
1610
173
1812
199
2018
211
222
237
2410
2511
269
2711
2816
295
306
316
329
3316
343
3516
366
3711
385
393
409
414
425
439
447
459
464
4713
486
4910
53.69% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats2
diversityRatio0.32934131736526945
totalSentences167
uniqueOpeners55
52.49% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences127
matches
0"Somewhere beyond them, metal wheels"
1"Then the gate slammed against"
ratio0.016
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount28
totalSentences127
matches
0"She kept her eyes on"
1"He glanced back."
2"Her left wrist struck the"
3"She kept pace."
4"Its glow marked the rain"
5"She had interviewed him twice."
6"He’d claimed not to know"
7"He had stopped running."
8"He stood with his back"
9"Her boots sent a thin"
10"He held up the object"
11"He twisted out of her"
12"His shoulder struck the wall."
13"His eyes fixed on the"
14"He slipped away, ducked through"
15"She grabbed the fallen token."
16"It was cold enough to"
17"She wedged the baton between"
18"She could retreat."
19"She could call for backup,"
ratio0.22
30.87% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount109
totalSentences127
matches
0"Rain drove Detective Harlow Quinn"
1"A taxi clipped the puddle"
2"Water slapped her coat to"
3"She kept her eyes on"
4"He glanced back."
5"A streetlamp caught his face:"
6"Kells turned into an alley."
7"The lane stank of wet"
8"A delivery van blocked half"
9"Kells vaulted its bonnet, slid,"
10"Quinn went over the side,"
11"Her left wrist struck the"
12"The leather strap of her"
13"She kept pace."
14"A night bus groaned past,"
15"Quinn cut behind it, palm"
16"A couple with takeaway cartons"
17"Quinn pushed through, shoulder first,"
18"Kells ran past the green"
19"Its glow marked the rain"
ratio0.858
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences127
matches(empty)
ratio0
98.21% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences48
technicalSentenceCount3
matches
0"Three months of missed interviews and false addresses, and now he was running with a bag that knocked against his hip like it held tools."
1"He stood with his back to her, shoulders rising and falling, and held something small in his bare hand."
2"The crowd closed around her, shoulders brushing her coat, voices clipping her from both sides."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags4
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags4
fancyCount1
fancyTags
0"he warned (warn)"
dialogueSentences35
tagDensity0.114
leniency0.229
rawRatio0.25
effectiveRatio0.057
88.6693%