Run: 2026-02-17-01KHMPDCT6Y57PGH8YEZ30XE3N

Run Details

Duration:
19.5s
Cost:
$0.001238
Provider:
Novita

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
The rain fell in sheets, turning the neon reflections on the pavement into liquid smears of color. Detective Harlow Quinn sprinted down the slick cobblestones of Soho, her breath coming in sharp bursts. Ahead, the suspect—a wiry figure in a dark hood—darted around a corner, nearly colliding with a group of tourists huddled under an awning. She adjusted her grip on her sidearm, not drawing it yet, but ready. The chase had started at The Raven’s Nest, where a tip had led her to a man selling black-market alchemical reagents. The moment he’d clocked her as police, he’d bolted. "Met Police! Stop!" she barked, but the figure didn’t even glance back. Her boots splashed through a puddle as she rounded the corner, catching a glimpse of the suspect slipping into an alleyway. The narrow passage was barely lit, the brick walls slick with moisture. She slowed just enough to avoid skidding, her sharp jaw set in determination. Then the alley opened into a wider courtyard, and the suspect was gone. Quinn cursed under her breath, scanning the shadows. A flicker of movement—a door swinging shut near a rusted fire escape. She lunged for it, wrenching it open to reveal a dim stairwell descending into darkness. The air smelled of damp concrete and something faintly metallic. Her wristwatch, the leather strap worn from years of wear, ticked softly as she hesitated. Protocol said to call for backup. But backup wouldn’t get here in time, and she couldn’t lose this lead. Not when it might be tied to Morris. She descended. The stairs led to an abandoned Tube station, its tiled walls cracked and covered in graffiti. The platform was empty except for a single figure standing beneath a flickering fluorescent light—Tomás Herrera, arms crossed, his Saint Christopher medallion glinting against his olive skin. "You shouldn’t be here, Detective," he said, his voice calm but edged with warning. Quinn’s hand hovered near her hip. "Where’d he go, Herrera?" Tomás exhaled through his nose, nodding toward an archway half-hidden behind peeling posters. "Through there. But you don’t have what you need to follow." She narrowed her eyes. "What’s that supposed to mean?" He lifted his left arm slightly, the scar along his forearm catching the light. "This isn’t your world. You go in there without a bone token, and they’ll know you’re not one of them." The Veil Market. She’d heard whispers—a place where the things that shouldn’t exist traded in shadows. Morris had muttered about it once, delirious with fever after a case gone wrong. Quinn’s pulse thudded in her throat. "Then get me one." Tomás studied her for a long moment, then reached into his jacket and tossed her a small, carved bone disk. "Don’t say I didn’t warn you." She caught it, the surface cold against her palm. The archway loomed ahead, the air beyond thick with the scent of incense and something older, darker. For Morris, she thought, and stepped through.
created_at"2026-02-17T02:21:23.928311+00:00" 2/17/2026, 2:21:23 AM
model"deepseek/deepseek-chat-v3"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency974
moderation_latency(null)
generation_time19334
tokens_prompt914
tokens_completion792
native_tokens_prompt826
native_tokens_completion698
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"
usage0.0012378
router(null)
provider_responses
0
id"60b2c4de82c97fc041dd02b64f933fb3"
status200
is_byokfalse
latency974
endpoint_id"cefb5a1e-9fea-4496-9a64-4e1b11a7cc8c"
provider_name"Novita"
model_permaslug"deepseek/deepseek-chat-v3"
api_type"completions"
id"gen-1771294864-zMobugx8tCCsELfl8ocb"
upstream_id"60b2c4de82c97fc041dd02b64f933fb3"
total_cost0.0012378
cache_discount(null)
upstream_inference_cost0
provider_name"Novita"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags2
adverbTagCount0
adverbTags(empty)
dialogueSentences8
tagDensity0.25
leniency0.5
rawRatio0
effectiveRatio0
79.88% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount497
totalAiIsmAdverbs2
found
0
adverb"softly"
count1
1
adverb"slightly"
count1
highlights
0"softly"
1"slightly"
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)
49.70% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount497
totalAiIsms5
found
0
word"scanning"
count1
1
word"flicker"
count1
2
word"glinting"
count1
3
word"pulse"
count1
4
word"loomed"
count1
highlights
0"scanning"
1"flicker"
2"glinting"
3"pulse"
4"loomed"
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
narrationSentences35
matches
0"t in determination"
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences35
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences41
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
totalWords492
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
totalMentions20
wordCount434
uniqueNames13
maxNameDensity0.92
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Morris"
discoveredNames
Harlow1
Quinn4
Soho1
Raven1
Nest1
Morris3
Tube1
Tomás3
Herrera1
Saint1
Christopher1
Veil1
Market1
persons
0"Harlow"
1"Quinn"
2"Morris"
3"Tomás"
4"Herrera"
5"Saint"
6"Christopher"
places
0"Soho"
1"Raven"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences32
glossingSentenceCount0
matches(empty)
0.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches1
per1kWords2.033
wordCount492
matches
0"not drawing it yet, but ready"
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences41
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs19
mean25.89
std15.94
cv0.615
sampleLengths
056
143
212
346
413
545
642
72
843
914
1010
1124
129
1334
1430
1510
1626
1726
187
95.24% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences35
matches
0"was gone"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs78
matches(empty)
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount5
semicolonCount0
flaggedSentences4
totalSentences41
ratio0.098
matches
0"Ahead, the suspect—a wiry figure in a dark hood—darted around a corner, nearly colliding with a group of tourists huddled under an awning."
1"A flicker of movement—a door swinging shut near a rusted fire escape."
2"The platform was empty except for a single figure standing beneath a flickering fluorescent light—Tomás Herrera, arms crossed, his Saint Christopher medallion glinting against his olive skin."
3"She’d heard whispers—a place where the things that shouldn’t exist traded in shadows."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount441
adjectiveStacks0
stackExamples(empty)
adverbCount10
adverbRatio0.022675736961451247
lyAdverbCount5
lyAdverbRatio0.011337868480725623
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences41
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences41
mean12
std5.84
cv0.487
sampleLengths
017
116
223
313
421
59
612
721
812
913
1013
118
1212
1315
1410
1515
166
1713
188
192
2016
2127
2214
236
244
2513
2611
274
285
2914
3020
313
3213
3314
346
354
3620
376
389
3917
407
84.55% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats2
diversityRatio0.5365853658536586
totalSentences41
uniqueOpeners22
98.04% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences34
matches
0"Then the alley opened into"
ratio0.029
90.59% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount11
totalSentences34
matches
0"She adjusted her grip on"
1"she barked, but the figure"
2"Her boots splashed through a"
3"She slowed just enough to"
4"She lunged for it, wrenching"
5"Her wristwatch, the leather strap"
6"he said, his voice calm"
7"She narrowed her eyes."
8"He lifted his left arm"
9"She’d heard whispers—a place where"
10"She caught it, the surface"
ratio0.324
33.53% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount29
totalSentences34
matches
0"The rain fell in sheets,"
1"Detective Harlow Quinn sprinted down"
2"She adjusted her grip on"
3"The chase had started at"
4"The moment he’d clocked her"
5"she barked, but the figure"
6"Her boots splashed through a"
7"The narrow passage was barely"
8"She slowed just enough to"
9"Quinn cursed under her breath,"
10"A flicker of movement—a door"
11"She lunged for it, wrenching"
12"The air smelled of damp"
13"Her wristwatch, the leather strap"
14"Protocol said to call for"
15"The stairs led to an"
16"The platform was empty except"
17"he said, his voice calm"
18"Quinn’s hand hovered near her"
19"Tomás exhaled through his nose,"
ratio0.853
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences34
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences22
technicalSentenceCount0
matches(empty)
0.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags2
uselessAdditionCount2
matches
0"she barked, but the figure didn’t even glance back"
1"he said, his voice calm but edged with warning"
25.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags2
fancyCount1
fancyTags
0"she barked (bark)"
dialogueSentences8
tagDensity0.25
leniency0.5
rawRatio0.5
effectiveRatio0.25
78.5508%