Run: 2026-09-23-01M36P1E720SDC8SA342QZG2BS

Run Details

Duration:
35.9s
Cost:
$0.001306
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 green neon over the Raven’s Nest broke apart in the rain, each letter trembling in the puddles beneath it. Harlow Quinn stood across the road with her collar turned up and her hands empty. She had watched the man inside for forty minutes. He had taken no drink, spoken to nobody for longer than a minute, and kept one gloved hand on the narrow case under his coat. Now he came out alone. Quinn pushed off the brick wall. The man glanced towards the crossing, then cut into the traffic. A bus blared at him. He slipped between a taxi and a delivery van, coat snapping behind him. Quinn ran. “Police! Stop!” He looked back once. The streetlights caught a pale face, a narrow nose, a line of blood beneath one ear. Then he ducked into an alley between a late-night grocer and a shuttered tailor. Quinn reached the alley as he cleared the far end. Rainwater ran down the sloped paving and over her shoes. She took the turn at speed, shoulder clipping brick, and caught sight of him at the next junction. “Police!” she called again. “Don’t make this worse.” He put his head down. A cyclist swore as the man cut across the pavement. Quinn followed, one hand out to keep a couple from stepping into her path. Her worn leather watch knocked against her wrist. The second hand kept moving. The man had a good lead and knew how to use it. He crossed streets on the red, doubled back through a narrow passage, vaulted a stack of milk crates. Quinn took the corner wide, found the gap he had used, and drove through it without breaking stride. At the mouth of the passage, her shoe skidded on slick stone. She caught herself against a drainpipe. The pipe shook in its brackets. Ahead, the man collided with a woman carrying a paper bag. Apples rolled into the gutter. He didn’t stop. Quinn stopped long enough to catch the woman’s elbow. “You hurt?” The woman stared after him, then at Quinn’s police badge. “He took my purse.” Quinn saw the strap dragging from the man’s fist. She let go and ran on. He had crossed into Camden now, where the late bars emptied their smoke and noise into the wet streets. A queue outside a club parted when he forced through. Quinn shouldered after him, flashing her badge to the doorman as he reached to block her. “Police. Move.” The doorman’s hand dropped. The suspect disappeared into the crowd. Quinn swept the faces, caught the dark coat near the kerb, and pushed through. She took an elbow to the ribs and a curse to the back, then emerged into the rain with the man thirty metres ahead. He turned down a street lined with closed shops and old brick fronts. The case beneath his coat bumped against his hip. Quinn kept him in view. Her breath scraped her throat. Her jaw tightened as she lengthened her stride. The street dipped towards a row of boarded-up buildings. At the end, the man pulled hard on a metal gate set into a wall of soot-stained brick. The gate gave way, and he vanished through it. Quinn reached the entrance and caught it before it swung shut. Beyond lay a stairwell. The air coming up smelled of wet iron, burnt herbs, and something sharp enough to sting the back of her nose. A bare bulb glowed above the first landing. Below that, the steps bent out of sight. She listened. Footsteps descended, then stopped. Quinn pressed her palm to the brick beside the gate. The rain ticked on the pavement behind her. Her radio hissed at her shoulder. “Quinn, status?” Her sergeant’s voice came through the speaker. “Suspect entered a service stairwell off Camden High Street. I’m going down.” “Back-up’s eight minutes out. Hold position.” “He’s carrying a case and a stolen purse. I’m not holding position.” She clicked off the radio before the reply. At the foot of the first flight, a door stood open on a corridor tiled in cracked green squares. Someone had painted over an old roundel. A few letters showed through the grey: CAM—. The suspect’s shoes slapped against the tiles farther in. Quinn descended. The stair rail left rust on her palm. At the bottom, she found a passage with old station posters, their faces faded to blank ovals. A ventilation fan turned overhead with a dry clack at each rotation. She reached the door and stopped. On its far side, voices murmured beneath the steady patter of water. Not commuters. Not drunks spilling out of a club. A low, busy babble with the scrape of tables and the clink of glass. Quinn drew her warrant card and pushed through. The abandoned platform had been remade into a market. Canvas awnings crowded the tiled walls. Lamps burned blue and amber over stalls heaped with brass instruments, jars of dark powder, folded maps, and objects Quinn couldn’t name. Buyers and sellers moved shoulder to shoulder beneath the old station signs. Some wore office clothes beneath raincoats. Others had scales of pale metal along their throats, or eyes that caught the light in colours no contact lens could produce. No one looked surprised to see her. A few looked annoyed. The suspect ran across the platform, scattering a tray of glass beads. He shoved through a gap between two stalls. Quinn started after him. A hand caught her sleeve. “Bone token,” the stallholder said. He was a narrow man with a silver ring through his lower lip. His gaze dropped to her badge. “No token, no crossing.” “Police. Let go.” “You can arrest me upstairs.” He pointed to a small arch cut into the brick at the far end of the platform. Two men stood on either side of it, both broad in the shoulders, both watching Quinn with their hands folded. Between them, the suspect had produced a pale disc and pressed it into a shallow socket in the arch. The stone face of the passage shivered. One of the men stepped aside. The suspect vanished through. Quinn twisted her sleeve free. “Where do I get one?” The stallholder smiled without warmth. “From someone who doesn’t need it.” A shout rose behind her. A woman in a red hat was accusing a vendor of switching labels on a packet of powder. Nobody reached for a phone. Nobody turned towards the station entrance. Quinn touched the radio at her shoulder. Its hiss had sharpened into a thin whine. The signal was gone. She scanned the floor. The suspect’s stolen purse lay near the scattered beads. Beside it, something pale glimmered in a runnel of water, wedged against the platform edge. Quinn crouched. A bone disc, no larger than a coin, rested beneath the lip of the platform. It had a notch through its centre and a mark burned into one side. She picked it up with two fingers. “Drop that,” the stallholder warned. “Then tell me where he went.” “You heard the rule.” Quinn rose and crossed to the arch. The two guards did not move. Up close, the air around the passage felt colder than the station. One guard’s eyes travelled from her face to the bone token. “That man went through,” Quinn said. “I’m following.” “Then you’ve got the token.” The guard’s hand opened, palm up. Quinn looked over her shoulder. The stairwell waited behind her, two flights and a locked gate away from the street. Her sergeant would have back-up on the way. If she went through, she’d be alone without a working radio in a place where nobody cared about her badge. The suspect had a head start. The stolen purse remained on the platform. She could hold the guards, call for help from the entrance, and lose him on the other side of the arch. Her fingers closed around the bone. “Are you coming through or not?” the guard asked. Quinn tucked her warrant card beneath her coat and set the token into the socket. The stone gave a short, grinding click. The passage opened by the width of her shoulders. “Keep the gate clear.” She ducked beneath the arch and stepped into the market.
created_at"2026-09-23T08:27:43.208Z" 9/23/2026, 8:27:43 AM
model"openai/gpt-6-luna-20260922"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency11461
moderation_latency(null)
generation_time35848
tokens_prompt1104
tokens_completion2029
native_tokens_prompt984
native_tokens_completion2415
native_tokens_completion_images0
native_tokens_reasoning695
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"
usage0.0013059
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.20.0; linux; x64))"
http_referer(null)
request_id"req-1790152063-kr0EuDBj44HotxpbZIhn"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1790152063-ZXdjAeuXm9AQnPkZUNLv"
upstream_id"resp_08bbfd5697ca6aa4016ab38d7f4e7487d1b08baf0530d82c7d"
provider_responses
0
endpoint_id"05e94e02-b9c2-4bbb-ba55-4082ee9ad687"
id"resp_08bbfd5697ca6aa4016ab38d7f4e7487d1b08baf0530d82c7d"
is_byokfalse
latency860
model_permaslug"openai/gpt-6-luna-20260922"
provider_name"OpenAI"
status200
total_cost0.0013059
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
adverbTagCount1
adverbTags
0"she called again [again]"
dialogueSentences24
tagDensity0.25
leniency0.5
rawRatio0.167
effectiveRatio0.083
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1375
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)
89.09% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1375
totalAiIsms3
found
0
word"footsteps"
count1
1
word"warmth"
count1
2
word"scanned"
count1
highlights
0"footsteps"
1"warmth"
2"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
emotionTells1
narrationSentences133
matches
0"looked surprised"
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences133
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences151
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen25
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1375
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions6
unquotedAttributions0
matches(empty)
47.64% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions29
wordCount1270
uniqueNames4
maxNameDensity2.05
worstName"Quinn"
maxWindowNameDensity3
worstWindowName"Quinn"
discoveredNames
Raven1
Nest1
Quinn26
Camden1
persons
0"Raven"
1"Nest"
2"Quinn"
places
0"Camden"
globalScore0.476
windowScore0.667
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences100
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1375
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences151
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs76
mean18.09
std16.33
cv0.903
sampleLengths
020
149
25
36
429
52
62
734
838
98
105
1137
1248
1324
1419
159
162
1710
184
1915
2045
212
2210
2360
2418
2536
2611
2741
282
294
3024
319
3212
336
3412
358
3634
379
3839
396
4035
418
429
4368
4411
4520
464
475
4828
493
99.99% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences133
matches
0"been remade"
1"was gone"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs208
matches
0"was accusing"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount1
semicolonCount0
flaggedSentences1
totalSentences151
ratio0.007
matches
0"A few letters showed through the grey: CAM—."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1274
adjectiveStacks0
stackExamples(empty)
adverbCount15
adverbRatio0.011773940345368918
lyAdverbCount1
lyAdverbRatio0.0007849293563579278
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences151
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences151
mean9.11
std5.31
cv0.583
sampleLengths
020
115
29
325
45
56
611
75
813
92
102
114
1216
1314
1410
1510
1618
174
184
195
2010
2114
228
235
2412
2518
2618
2712
286
296
3011
315
323
339
342
3510
364
379
386
3919
4010
4116
422
434
446
4514
4624
4713
489
495
47.68% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats8
diversityRatio0.31788079470198677
totalSentences151
uniqueOpeners48
26.25% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences127
matches
0"Then he ducked into an"
ratio0.008
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount33
totalSentences127
matches
0"She had watched the man"
1"He had taken no drink,"
2"He slipped between a taxi"
3"He looked back once."
4"She took the turn at"
5"she called again"
6"He put his head down."
7"Her worn leather watch knocked"
8"He crossed streets on the"
9"She caught herself against a"
10"He didn’t stop."
11"She let go and ran"
12"He had crossed into Camden"
13"She took an elbow to"
14"He turned down a street"
15"Her breath scraped her throat."
16"Her jaw tightened as she"
17"Her radio hissed at her"
18"Her sergeant’s voice came through"
19"She clicked off the radio"
ratio0.26
42.68% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount106
totalSentences127
matches
0"The green neon over the"
1"Harlow Quinn stood across the"
2"She had watched the man"
3"He had taken no drink,"
4"Quinn pushed off the brick"
5"The man glanced towards the"
6"A bus blared at him."
7"He slipped between a taxi"
8"He looked back once."
9"The streetlights caught a pale"
10"Quinn reached the alley as"
11"Rainwater ran down the sloped"
12"She took the turn at"
13"she called again"
14"He put his head down."
15"A cyclist swore as the"
16"Quinn followed, one hand out"
17"Her worn leather watch knocked"
18"The second hand kept moving."
19"The man had a good"
ratio0.835
78.74% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount2
totalSentences127
matches
0"Now he came out alone."
1"If she went through, she’d"
ratio0.016
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences53
technicalSentenceCount0
matches(empty)
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags6
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags5
fancyCount1
fancyTags
0"the stallholder warned (warn)"
dialogueSentences24
tagDensity0.208
leniency0.417
rawRatio0.2
effectiveRatio0.083
91.0687%