Run: 2026-02-17-01KHNFQ1S4JSQ9DWBBYSSK2945

Run Details

Duration:
33.4s
Cost:
$0.001054
Provider:
AtlasCloud

Model input

System
You are an expert novelist with a gift for vivid, immersive prose. You write in tight third-person limited point of view with a strong narrative voice. Your prose is clean, precise, and engaging — you favor strong verbs, concrete details, and natural-sounding dialogue. You will be given a story bible (codex) containing canonical details about characters, locations, items, and lore. Use these details to ground your writing in the established world. Stay consistent with the codex. Write only the prose of the scene. Do not include titles, chapter headings, markdown formatting, or any commentary before or after the scene.
User/Human
<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> 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.

Model response

AI
Rain slid down Detective Harlow Quinn’s neck in a cold, insistent trickle. The collar of her coat was soaked through, the worn leather of her watch strap damp against her skin. Ahead, the suspect—a wiry figure in a dark hood—darted around the corner of a crumbling brick building, splashing through a puddle that gleamed with reflected neon. She adjusted her stride, pushing through the ache in her knees as she pounded after him. Soho at night was a study in contrasts. The slick pavement shimmered under green and red signs while the air smelled of wet asphalt and fried food. She caught the blur of the neon sign above The Raven’s Nest as she passed it, its green glow painting the raindrops in her vision a sickly hue. The suspect twisted down an alley, and she followed, her breath burning in her chest. He was fast. Too fast. Her polished boots skidded on a slick drain cover as she rounded another corner, nearly costing her her balance. The alley dead-ended at a rusted metal door hanging ajar. She drew her sidearm. Edging forward, she nudged the door open with her toe—hinges shrieking—and stepped into the skeletal remains of a derelict building. The floor groaned under her weight. A shadow flickered at the far end of the gutted space. She moved toward it, training her weapon ahead in a two-handed grip. “Metropolitan Police,” she called out. Her voice echoed. No response. A low rumble vibrated through the ground—something subterranean. The Underground. Her pulse jumped. They were near the tunnels. The suspect vanished down a set of makeshift stairs, little more than broken concrete slabs descending into darkness. She hesitated. Protocol dictated backup, radioing in, containment. But backup didn’t know about the patterns, the whispers she’d uncovered in case files, the way DS Morris’ name kept surfacing in reports with blacked-out details. She stepped down. The air grew thick, heavy with the scent of damp earth and something metallic—ozone? The walls here were old brick, slick with condensation. Water dripped in an uneven rhythm. The stairs ended at what looked like an abandoned Tube platform, the rails swallowed by shadow. There was no train waiting. Just a scattering of people in clusters, hooded, shrouded, their murmurs bouncing off the curved ceiling. A market. But not the kind you found above ground. She holstered her weapon, kept her hand near it. Booths lined the platform, makeshift stalls draped in dark fabric. Lanterns hung from rusted hooks, casting long, shifting shadows. Someone was selling vials of liquid that gleamed unnaturally under the dim light. Another stall displayed what looked like weapons—thin blades with carved handles, their edges shimmering as if dusted with powdered glass. Her suspect was nowhere. A man near the weapon stall turned his head slightly. Hispanic, late twenties, with sharp features and a Saint Christopher medallion glinting at his throat. His dark eyes met hers for a half-second too long. Recognition? Warning? She moved toward him, casually, as if just browsing. Close up, she noted the scar along his forearm—jagged, old. The air between them stiffened. He tapped his fingers once, twice against the stall’s wooden frame. The vendor—a hunched figure with too many rings—flicked a glance at Quinn and then away, deliberately disinterested. “You’re not supposed to be here,” the man murmured in a low Spanish accent. “Yeah,” Quinn said, keeping her voice flat. “I get that a lot.” She let her gaze drift over the weapons. “You know the guy I was chasing? Hood. Fast.” His jaw tightened almost imperceptibly. “You should leave.” She turned slightly toward him, closing the distance. “Not until I find my suspect.” Something in his expression shifted. Wariness. He exhaled through his nose. “You won’t.” There was finality in his tone. Beyond them, the market’s murmur dipped, just for a moment. Eyes flicked in their direction. The hairs on her neck pricked. The man inclined his head toward the stairs—barely a movement. Go. She wasn’t a rookie. She had eighteen years in the Met and five before that in the army. But this place hummed with something she didn’t understand—something that made her sidearm feel absurdly insufficient. Quinn took a slow step back. The man didn’t watch her leave. She made it to the stairs before she heard the first footstep behind her.
created_at"2026-02-17T09:43:52.251855+00:00" 2/17/2026, 9:43:52 AM
model"deepseek/deepseek-chat-v3-0324"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency3136
moderation_latency(null)
generation_time33293
tokens_prompt914
tokens_completion1177
native_tokens_prompt827
native_tokens_completion995
native_tokens_completion_images(null)
native_tokens_reasoning0
native_tokens_cached0
num_media_prompt(null)
num_input_audio_prompt(null)
num_media_completion0
num_search_results(null)
origin"https://ncbench.com/"
is_byokfalse
finish_reason"stop"
native_finish_reason"stop"
usage0.001054232
router(null)
provider_responses
0
id"cc6a47be37a74c2f8a15618e926162a4"
status200
is_byokfalse
latency3136
endpoint_id"5bb598f5-fd97-4f8b-91e4-ceab3743a03b"
provider_name"AtlasCloud"
model_permaslug"deepseek/deepseek-chat-v3-0324"
api_type"completions"
id"gen-1771321394-0mNEMaAoMSGJedASMHKm"
upstream_id"cc6a47be37a74c2f8a15618e926162a4"
total_cost0.001054232
cache_discount(null)
upstream_inference_cost0
provider_name"AtlasCloud"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags5
adverbTagCount0
adverbTags(empty)
dialogueSentences8
tagDensity0.625
leniency1
rawRatio0
effectiveRatio0
79.59% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount735
totalAiIsmAdverbs3
found
0
adverb"slightly"
count2
1
adverb"deliberately"
count1
highlights
0"slightly"
1"deliberately"
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)
38.78% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount735
totalAiIsms9
found
0
word"shimmered"
count1
1
word"weight"
count1
2
word"flickered"
count1
3
word"echoed"
count1
4
word"vibrated"
count1
5
word"pulse"
count1
6
word"glinting"
count1
7
word"flicked"
count2
highlights
0"shimmered"
1"weight"
2"flickered"
3"echoed"
4"vibrated"
5"pulse"
6"glinting"
7"flicked"
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
narrationSentences73
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount0
narrationSentences73
filterMatches
0"watch"
1"feel"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences77
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen28
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords719
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions4
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions14
wordCount676
uniqueNames11
maxNameDensity0.59
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Quinn"
discoveredNames
Detective1
Harlow1
Quinn4
Raven1
Nest1
Underground1
Tube1
Saint1
Christopher1
Spanish1
Met1
persons
0"Harlow"
1"Quinn"
2"Raven"
3"Saint"
4"Christopher"
places
0"Met"
globalScore1
windowScore1
47.96% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences49
glossingSentenceCount2
matches
0"looked like an abandoned Tube platform, t"
1"looked like weapons—thin blades with carv"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount719
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences77
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs29
mean24.79
std20.03
cv0.808
sampleLengths
073
170
25
359
423
510
618
752
83
945
1031
119
1252
134
1437
1524
1628
1714
1829
198
2014
2113
226
2321
2410
251
2634
2712
2814
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences73
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs123
matches
0"was selling"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount12
semicolonCount0
flaggedSentences9
totalSentences77
ratio0.117
matches
0"Ahead, the suspect—a wiry figure in a dark hood—darted around the corner of a crumbling brick building, splashing through a puddle that gleamed with reflected neon."
1"Edging forward, she nudged the door open with her toe—hinges shrieking—and stepped into the skeletal remains of a derelict building."
2"A low rumble vibrated through the ground—something subterranean."
3"The air grew thick, heavy with the scent of damp earth and something metallic—ozone?"
4"Another stall displayed what looked like weapons—thin blades with carved handles, their edges shimmering as if dusted with powdered glass."
5"Close up, she noted the scar along his forearm—jagged, old."
6"The vendor—a hunched figure with too many rings—flicked a glance at Quinn and then away, deliberately disinterested."
7"The man inclined his head toward the stairs—barely a movement."
8"But this place hummed with something she didn’t understand—something that made her sidearm feel absurdly insufficient."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount701
adjectiveStacks0
stackExamples(empty)
adverbCount24
adverbRatio0.034236804564907276
lyAdverbCount10
lyAdverbRatio0.014265335235378032
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences77
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences77
mean9.34
std6.31
cv0.675
sampleLengths
012
119
226
316
48
519
628
715
83
92
1019
1110
124
1320
146
1511
1612
175
183
192
208
212
223
235
2418
252
266
2726
283
2914
309
316
3216
335
3416
352
368
379
3810
399
4013
4120
424
4310
4415
4510
461
471
489
4910
76.19% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats3
diversityRatio0.4805194805194805
totalSentences77
uniqueOpeners37
52.08% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences64
matches
0"Just a scattering of people"
ratio0.016
76.25% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount23
totalSentences64
matches
0"She adjusted her stride, pushing"
1"She caught the blur of"
2"He was fast."
3"Her polished boots skidded on"
4"She drew her sidearm."
5"She moved toward it, training"
6"she called out"
7"Her voice echoed."
8"Her pulse jumped."
9"They were near the tunnels."
10"She stepped down."
11"She holstered her weapon, kept"
12"Her suspect was nowhere."
13"His dark eyes met hers"
14"She moved toward him, casually,"
15"He tapped his fingers once,"
16"She let her gaze drift"
17"His jaw tightened almost imperceptibly."
18"She turned slightly toward him,"
19"He exhaled through his nose."
ratio0.359
45.94% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount53
totalSentences64
matches
0"Rain slid down Detective Harlow"
1"The collar of her coat"
2"She adjusted her stride, pushing"
3"Soho at night was a"
4"The slick pavement shimmered under"
5"She caught the blur of"
6"The suspect twisted down an"
7"He was fast."
8"Her polished boots skidded on"
9"The alley dead-ended at a"
10"She drew her sidearm."
11"The floor groaned under her"
12"A shadow flickered at the"
13"She moved toward it, training"
14"she called out"
15"Her voice echoed."
16"A low rumble vibrated through"
17"Her pulse jumped."
18"They were near the tunnels."
19"The suspect vanished down a"
ratio0.828
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences64
matches(empty)
ratio0
71.43% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences30
technicalSentenceCount3
matches
0"Ahead, the suspect—a wiry figure in a dark hood—darted around the corner of a crumbling brick building, splashing through a puddle that gleamed with reflected n…"
1"Someone was selling vials of liquid that gleamed unnaturally under the dim light."
2"But this place hummed with something she didn’t understand—something that made her sidearm feel absurdly insufficient."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags5
uselessAdditionCount0
matches(empty)
25.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags2
fancyCount1
fancyTags
0"the man murmured (murmur)"
dialogueSentences8
tagDensity0.25
leniency0.5
rawRatio0.5
effectiveRatio0.25
80.4405%