Run: 2026-09-23-01M36P14JXHRDD0TJD1XPTV3FY

Run Details

Duration:
34.7s
Cost:
$0.001203
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 had turned Camden High Street into a black ribbon. Harlow Quinn ran along its edge, one hand braced against the slick brick wall, the other reaching for the radio at her shoulder. “Control, suspect heading north past the station. Male, dark coat, carrying a canvas bag. Keep units off the pavement. He’s cutting through the market.” Her radio spat static. Ahead, the man shouldered between a pair of late-night revellers and knocked one into a bus shelter. He did not look back. His coat snapped behind him as he sprinted past shuttered shopfronts, shoes slapping through puddles. Quinn lengthened her stride. Her worn leather watch caught beneath her cuff; she tugged it free without slowing. The second hand had stopped at 11:17. It had been stuck there for months. She had not bothered to replace it. “Police! Stop!” The suspect vaulted a low metal barrier. Quinn hit it with both hands, swung a leg across, and landed hard on the other side. Pain shot through her knee. She took three quick steps to shake it loose and kept moving. A cyclist swerved around her, bell shrilling. Quinn cut across the street behind him. A taxi braked and its tyres hissed over the wet road. The driver leaned on the horn. “Watch where you’re going!” “Keep moving,” Quinn barked. The suspect ducked into a narrow lane between a kebab shop and a boarded-up tailor. Quinn followed. The lane pinched tight, hemmed in by brick walls and overflowing bins. Rain rattled on the metal lids. The suspect’s bag struck a bin as he squeezed past. Something clattered onto the stones. Quinn spotted a pale object beneath the wash of a security lamp. She snatched it up without stopping. It felt cold and ridged in her palm, a small disc of bone carved with a knot pattern. No key ring, no hole drilled through it. A token. At the far end of the lane, the suspect burst through a gate and crossed a courtyard strewn with broken paving slabs. Quinn caught sight of his face when he glanced over his shoulder: narrow, bloodless, a fresh cut above one eyebrow. “Venn!” His gaze flicked to the token in her hand. He faced forward and ran harder. Quinn followed him through the courtyard and out onto a residential street. Terraced houses leaned shoulder to shoulder beneath the rain. A television glowed behind one window. Somewhere, a dog barked at the pounding feet. The suspect veered down a flight of steps between two houses. Quinn took them in a rush, palm sliding along the iron rail. At the bottom, the alley opened onto a service road beside a darkened pub. He reached the road first and swung around a delivery van. Quinn cut inside the turn. Her shoulder struck the van’s flank. Pain flared and vanished beneath the scrape of her coat. She came out close enough to see water beading on the man’s collar. He hit a chain-link fence at the end of the road. The gate hung open by one hinge, its padlock split. Beyond it lay a disused station entrance: tiled stairs descending beneath a sign stripped of its letters. A council notice warned of asbestos, structural instability and a fine for trespass. Someone had crossed out the fine with black marker and drawn an eye beneath it. Venn slipped through the gate. Quinn stopped for half a breath. Her lungs burned. Rain ran from her cropped hair into her eyes. She wiped it away with the back of her hand and looked down the stairs. A dim orange glow pulsed below, though the station had been closed for years. The stairwell smelled of wet stone and hot metal. Voices rose from it—too many for an empty platform, too close for people gathered on the street above. Her radio crackled. “Quinn, status?” She thumbed the button. “At the old station entrance. Suspect went below.” “Hold position. Uniforms are two minutes out.” The stairwell answered with a sudden crash. A shout followed, then the fast scrape of shoes on tile. Quinn lowered the radio. Two minutes meant two minutes in the rain while Venn vanished into whatever waited below. She had the bone token in one hand and her sidearm in the other. The token’s ridges pressed into her palm. At the mouth of the stairs, someone had painted a message across the wall: NO TOKEN, NO TRADE. The paint looked new. Beneath it sat a shallow dish filled with scraps of bone and grey ash. Quinn could hear the suspect’s footfalls retreating. Then came another sound, a low murmur that gathered voices together without becoming words. She had seen the same symbol carved into the flesh of a man they found in Bermondsey. She had stood in a morgue and watched the lights flicker out over DS Morris’s covered body. She had heard the coroner call the cause of death inconclusive and had read the word inconclusive until the letters stopped making sense. Her thumb pressed against the token’s carved knot. “Quinn?” Control called. “Confirm you’re holding.” She stared into the stairwell. The orange glow shifted, and shadows moved against the tiled walls. One of them looked up. “Negative,” she answered. “I’m going in.” She descended. The stairs turned once, then split around a pillar. Quinn kept one hand on the rail and her weapon angled towards the lower landing. Old white tiles reflected the orange light in fractured strips. Rainwater ran down the steps after her, tracing a thin silver line. At the bottom, a passage opened onto the abandoned platform. Venn stood beside a rusted ticket barrier, pinned between a woman in a green velvet coat and a man whose mouth had been sewn shut with copper wire. The canvas bag hung from his shoulder. He raised both hands. “Police,” Quinn called. “Step away from them.” The woman in green turned. Her eyes settled on Quinn’s badge, then on the bone token. “Inspector?” the woman asked. “Detective.” “Then you’re lost.” “Not yet.” Venn lunged for the gap between them. Quinn brought up her pistol. He checked his stride and struck the barrier with his hip. The bag swung open. A bundle of papers spilled onto the platform, skidding through a film of dust. “Hands on the rail,” Quinn ordered. “Now.” The man with the stitched mouth gave a dry click of his tongue. Venn stared at the papers, then at Quinn’s gun. “You don’t know what you’ve walked into,” he said. “Then tell me while you’re face down.” The woman in green stepped into Quinn’s line of fire. She held out one pale hand, palm up. “Token.” Quinn kept her pistol trained on Venn. “You want this?” “You brought it. The door accepted you.” Behind the woman, a row of market stalls stretched down the platform. Lanterns hung from iron hooks. A vendor arranged glass vials on a velvet cloth while two customers examined a set of silver teeth. Beyond them, figures moved between tarpaulins and old railway pillars. One stall displayed jars packed with things that twitched against the glass. Another offered folded maps, each inked with streets Quinn did not recognise. The market filled the station without making a sound that matched its size. Its voices gathered around her from every direction. Venn’s foot shifted towards the fallen papers. “Don’t,” Quinn warned. He froze. The woman in green kept her hand extended. “Without the token, you don’t cross the stalls.” Quinn glanced back towards the stairwell. It offered a straight route to the street, to uniforms and rain and a case file with names she could subpoena. In front of her, Venn stood beside the scattered papers, and the passage beyond the stalls swallowed the platform’s light. Her radio crackled again. No words came through. Quinn slipped the bone token into her pocket and moved past the barrier. “Keep your hands where I can see them, Venn.” He raised them to the rusted rail. Quinn stepped into the market.
created_at"2026-09-23T08:27:33.395Z" 9/23/2026, 8:27:33 AM
model"openai/gpt-6-luna-20260922"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency8766
moderation_latency(null)
generation_time34635
tokens_prompt1104
tokens_completion2018
native_tokens_prompt984
native_tokens_completion2209
native_tokens_completion_images0
native_tokens_reasoning508
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"
usage0.0012029
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.20.0; linux; x64))"
http_referer(null)
request_id"req-1790152053-rQ5MdRTSJRJtE0jY2RF1"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1790152053-IFcUXHhvLtZQHdz5EjsF"
upstream_id"resp_0bd1fcb41a23c229016ab38d7582f087d189e4275fea94641b"
provider_responses
0
endpoint_id"05e94e02-b9c2-4bbb-ba55-4082ee9ad687"
id"resp_0bd1fcb41a23c229016ab38d7582f087d189e4275fea94641b"
is_byokfalse
latency754
model_permaslug"openai/gpt-6-luna-20260922"
provider_name"OpenAI"
status200
total_cost0.0012029
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
totalTags8
adverbTagCount0
adverbTags(empty)
dialogueSentences28
tagDensity0.286
leniency0.571
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1332
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)
66.22% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1332
totalAiIsms9
found
0
word"eyebrow"
count1
1
word"flicked"
count1
2
word"pounding"
count1
3
word"pulsed"
count1
4
word"flicker"
count1
5
word"fractured"
count1
6
word"tracing"
count1
7
word"velvet"
count2
highlights
0"eyebrow"
1"flicked"
2"pounding"
3"pulsed"
4"flicker"
5"fractured"
6"tracing"
7"velvet"
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
narrationSentences127
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount0
narrationSentences127
filterMatches
0"watch"
1"notice"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences147
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen28
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1331
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions7
unquotedAttributions0
matches(empty)
33.33% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions43
wordCount1213
uniqueNames8
maxNameDensity2.23
worstName"Quinn"
maxWindowNameDensity4
worstWindowName"Quinn"
discoveredNames
Camden1
High1
Street1
Quinn27
Venn8
Bermondsey1
Morris1
Rain3
persons
0"Quinn"
1"Venn"
2"Morris"
3"Rain"
places
0"Camden"
1"High"
2"Street"
3"Bermondsey"
globalScore0.387
windowScore0.333
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
wordCount1331
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences147
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs63
mean21.13
std18.44
cv0.873
sampleLengths
033
124
241
339
42
541
631
74
84
950
1046
1142
121
1315
1435
1548
1634
1766
185
1933
2041
213
222
2312
247
2518
2640
2736
2821
2957
308
316
3221
336
342
3546
3649
377
3816
394
401
413
422
4341
447
4522
469
477
4818
491
96.97% Passive voice overuse
Target: ≤2% passive sentences
passiveCount3
totalSentences127
matches
0"been stuck"
1"been closed"
2"been sewn"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs205
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount1
semicolonCount1
flaggedSentences2
totalSentences147
ratio0.014
matches
0"Her worn leather watch caught beneath her cuff; she tugged it free without slowing."
1"Voices rose from it—too many for an empty platform, too close for people gathered on the street above."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1217
adjectiveStacks0
stackExamples(empty)
adverbCount19
adverbRatio0.015612161051766639
lyAdverbCount0
lyAdverbRatio0
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences147
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences147
mean9.05
std5.46
cv0.603
sampleLengths
010
123
224
34
417
55
615
74
814
97
107
117
122
137
1417
155
1612
177
187
1911
206
214
224
2315
242
2512
266
2710
285
2912
306
3118
328
332
3422
3520
361
379
386
3912
409
416
428
4311
4412
4514
4611
475
486
4910
54.65% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats3
diversityRatio0.3401360544217687
totalSentences147
uniqueOpeners50
57.47% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences116
matches
0"Somewhere, a dog barked at"
1"Then came another sound, a"
ratio0.017
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount33
totalSentences116
matches
0"Her radio spat static."
1"He did not look back."
2"His coat snapped behind him"
3"Her worn leather watch caught"
4"It had been stuck there"
5"She had not bothered to"
6"She took three quick steps"
7"She snatched it up without"
8"It felt cold and ridged"
9"His gaze flicked to the"
10"He faced forward and ran"
11"He reached the road first"
12"Her shoulder struck the van’s"
13"She came out close enough"
14"He hit a chain-link fence"
15"Her lungs burned."
16"She wiped it away with"
17"Her radio crackled."
18"She thumbed the button."
19"She had the bone token"
ratio0.284
41.90% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount97
totalSentences116
matches
0"Rain had turned Camden High"
1"Harlow Quinn ran along its"
2"Her radio spat static."
3"He did not look back."
4"His coat snapped behind him"
5"Quinn lengthened her stride."
6"Her worn leather watch caught"
7"The second hand had stopped"
8"It had been stuck there"
9"She had not bothered to"
10"The suspect vaulted a low"
11"Quinn hit it with both"
12"Pain shot through her knee."
13"She took three quick steps"
14"A cyclist swerved around her,"
15"Quinn cut across the street"
16"A taxi braked and its"
17"The driver leaned on the"
18"The suspect ducked into a"
19"The lane pinched tight, hemmed"
ratio0.836
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences116
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences59
technicalSentenceCount0
matches(empty)
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags8
uselessAdditionCount0
matches(empty)
42.86% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags8
fancyCount3
fancyTags
0"Quinn barked (bark)"
1"Quinn ordered (order)"
2"Quinn warned (warn)"
dialogueSentences28
tagDensity0.286
leniency0.571
rawRatio0.375
effectiveRatio0.214
86.4466%