Run: 2026-02-17-01KHNFB38C7PPAG9KDEKDDKRFB

Run Details

Duration:
23.3s
Cost:
$0.001168
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 came down in sheets, turning the Soho streets into a shimmering black mirror. Detective Harlow Quinn sprinted through the neon reflections, her breath coming in sharp bursts. Ahead of her, Tomás Herrera darted around a corner, his dark coat flapping like the wings of a startled crow. "Met Police! Stop!" Quinn's voice cut through the drumming rain, but Herrera didn't even glance back. Her shoes splashed through a puddle as she rounded the corner, the green glow of The Raven's Nest sign casting an eerie pallor over the scene. The bar's door swung shut behind Herrera with a muffled thud. Quinn didn't hesitate—she barreled through after him, the sudden warmth of the interior hitting her like a wall. The bar was all dim lighting and whispered conversations that died the moment she entered. Old maps on the walls seemed to watch her as she scanned the room. No Herrera. A bartender with tattooed knuckles polished a glass with deliberate slowness. "Where'd he go?" Quinn flashed her badge, rainwater dripping from her salt-and-pepper hair onto the worn leather of her watch. The bartender nodded toward a bookshelf in the back. Quinn's sharp jaw tightened. She'd been in enough dive bars to know what that meant. The hidden door gave way with a push, revealing a narrow staircase descending into darkness. The air smelled of damp stone and something metallic—blood or rust, she couldn't tell which. Her hand hovered near her sidearm as she took the steps one at a time, the wood creaking under her weight. The underground space opened up into something far larger than she expected. The abandoned Tube station stretched out before her, makeshift stalls lining the curved tile walls. The Veil Market. The name came to her in a whisper, though she couldn't remember where she'd heard it before. Herrera was already halfway across the platform, weaving between hooded figures haggling over vials of glowing liquid and what looked disturbingly like human teeth strung on cords. Quinn's military-trained eyes catalogued the exits—too few, too far. A vendor with yellowed nails blocked her path, holding up a bone token between grimy fingers. "Entry fee, love." Quinn brushed past him. "Police business." The market's murmur shifted. Eyes gleamed from shadowed hoods. She felt the weight of their stares like physical pressure between her shoulder blades. Herrera glanced back once, his Saint Christopher medallion glinting under the flickering gas lamps, then disappeared through an archway marked with symbols that made Quinn's vision blur if she looked too long. She hesitated. Three years ago, Morris had chased a suspect into an alley that shouldn't have existed. They'd never found his body. Rainwater dripped from her sleeve onto the platform as she made her decision. The medallion's gleam had looked like a challenge. Like an invitation. Quinn stepped forward—into the archway, into the dark.
created_at"2026-02-17T09:37:06.973835+00:00" 2/17/2026, 9:37:06 AM
model"deepseek/deepseek-chat-v3"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency1503
moderation_latency(null)
generation_time23304
tokens_prompt914
tokens_completion761
native_tokens_prompt826
native_tokens_completion644
native_tokens_completion_images0
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"
usage0.0011676
router(null)
provider_responses
0
id"7f4d8d1f728a0abf10ab12e0884f99b5"
status200
is_byokfalse
latency1503
endpoint_id"cefb5a1e-9fea-4496-9a64-4e1b11a7cc8c"
provider_name"Novita"
model_permaslug"deepseek/deepseek-chat-v3"
api_type"completions"
id"gen-1771321003-EE2nytRttfI2jXkoBXPI"
upstream_id"7f4d8d1f728a0abf10ab12e0884f99b5"
total_cost0.0011676
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)
dialogueSentences4
tagDensity0.5
leniency1
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount481
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)
37.63% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount481
totalAiIsms6
found
0
word"warmth"
count1
1
word"scanned"
count1
2
word"weight"
count2
3
word"whisper"
count1
4
word"glinting"
count1
highlights
0"warmth"
1"scanned"
2"weight"
3"whisper"
4"glinting"
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
narrationSentences37
matches(empty)
65.64% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount1
narrationSentences37
filterMatches
0"watch"
hedgeMatches
0"seemed to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences39
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen32
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords474
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions1
unquotedAttributions0
matches(empty)
52.81% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions26
wordCount463
uniqueNames13
maxNameDensity1.94
worstName"Quinn"
maxWindowNameDensity2.5
worstWindowName"Quinn"
discoveredNames
Soho1
Harlow1
Quinn9
Tomás1
Herrera6
Raven1
Nest1
Tube1
Veil1
Market1
Saint1
Christopher1
Morris1
persons
0"Harlow"
1"Quinn"
2"Tomás"
3"Herrera"
4"Raven"
5"Saint"
6"Christopher"
7"Morris"
places
0"Soho"
globalScore0.528
windowScore0.833
60.71% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences28
glossingSentenceCount1
matches
0"looked like a challenge"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount474
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences39
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs16
mean29.63
std14.92
cv0.503
sampleLengths
049
116
255
342
420
524
651
747
836
919
106
1123
1232
1322
1424
158
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences37
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs79
matches(empty)
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount4
semicolonCount0
flaggedSentences4
totalSentences39
ratio0.103
matches
0"Quinn didn't hesitate—she barreled through after him, the sudden warmth of the interior hitting her like a wall."
1"The air smelled of damp stone and something metallic—blood or rust, she couldn't tell which."
2"Quinn's military-trained eyes catalogued the exits—too few, too far."
3"Quinn stepped forward—into the archway, into the dark."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount470
adjectiveStacks0
stackExamples(empty)
adverbCount11
adverbRatio0.023404255319148935
lyAdverbCount1
lyAdverbRatio0.002127659574468085
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences39
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences39
mean12.15
std7.29
cv0.6
sampleLengths
015
114
220
316
426
511
618
715
814
92
1011
1120
129
134
1411
1515
1615
1721
1812
1915
203
2117
2227
239
2416
253
264
272
284
295
3014
3132
322
3315
345
3513
368
373
388
70.94% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats4
diversityRatio0.48717948717948717
totalSentences39
uniqueOpeners19
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences35
matches(empty)
ratio0
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount5
totalSentences35
matches
0"Her shoes splashed through a"
1"She'd been in enough dive"
2"Her hand hovered near her"
3"She felt the weight of"
4"They'd never found his body."
ratio0.143
17.14% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount31
totalSentences35
matches
0"The rain came down in"
1"Detective Harlow Quinn sprinted through"
2"Quinn's voice cut through the"
3"Her shoes splashed through a"
4"The bar's door swung shut"
5"Quinn didn't hesitate—she barreled through"
6"The bar was all dim"
7"A bartender with tattooed knuckles"
8"Quinn flashed her badge, rainwater"
9"The bartender nodded toward a"
10"Quinn's sharp jaw tightened."
11"She'd been in enough dive"
12"The hidden door gave way"
13"The air smelled of damp"
14"Her hand hovered near her"
15"The underground space opened up"
16"The abandoned Tube station stretched"
17"The Veil Market."
18"The name came to her"
19"Herrera was already halfway across"
ratio0.886
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences35
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences23
technicalSentenceCount1
matches
0"Herrera glanced back once, his Saint Christopher medallion glinting under the flickering gas lamps, then disappeared through an archway marked with symbols that…"
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags2
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags0
80.1624%