Run: 2026-02-19-01KHTYQFTCY2NJD8BM5AWCQKSX

Run Details

Duration:
1m 2s
Cost:
$0.001140
Provider:
Novita

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 rain came down in sheets, turning the neon reflections on the pavement into liquid smears of color. Detective Harlow Quinn sprinted after the figure darting between the crowds on Dean Street, her boots splashing through puddles that soaked through her trousers. The suspect—hood up, shoulders hunched—ducked into an alleyway. She followed without hesitation. "Met Police! Stop!" Her voice barely carried over the drumming rain. The figure vaulted over a stack of crates, landing with a wet slap on the other side. Quinn didn’t slow. She grabbed the edge of a dumpster, swung herself over, and landed hard enough to send a jolt up her knees. Ahead, the suspect shoved open a rusted service door and disappeared inside. She drew her sidearm, flicked off the safety, and pushed through the door. The dim interior smelled of damp concrete and mildew. Water dripped from pipes overhead, echoing in the narrow corridor. A flickering bulb cast long shadows. Footsteps pounded ahead. She moved fast, pistol raised, sweeping each corner before advancing. The corridor ended at a staircase descending into darkness. A cold draft rose from below, carrying the scent of incense and something metallic. Quinn hesitated. Protocol said call for backup. But backup wouldn’t get here in time, and she wasn’t losing another lead. She descended. The stairs opened into a cavernous space—an abandoned Tube station, its tiled walls cracked and stained. Market stalls lined the platforms, lit by lanterns that cast flickering orange light. The air hummed with low voices, the clink of glass, the rustle of cloth. The suspect was already weaving through the crowd. Quinn holstered her gun but kept her hand near it. The market-goers didn’t look like ordinary Londoners. A woman with too-sharp teeth examined a vial of swirling liquid. A man with ink-black eyes haggled over what looked like a human finger bone. She moved forward, scanning the stalls. "You’re not supposed to be here." The voice came from her left. A man leaned against a stall piled with dried herbs, his arms crossed. Olive skin, dark curls, a scar running down his forearm. A Saint Christopher medallion glinted at his throat. Tomás Herrera. She knew him from the files—former paramedic, suspected of supplying underground clinics. "Metropolitan Police," she said, low. "I’m pursuing a suspect." Tomás snorted. "No badge down here, detective. And no laws." "I don’t care about your market. Just the man who ran in." He studied her, then jerked his chin toward a stall at the far end. "Green tent. But you won’t like what you find." She didn’t thank him. The crowd parted reluctantly as she moved. Whispers followed her. The green tent was half-hidden behind a curtain of beads. She pushed through. Inside, the air was thick with smoke. The suspect stood at the back, hood still up, facing a figure seated in shadow. "Police," Quinn said. "Turn around. Hands where I can see them." The seated figure laughed—a wet, rattling sound. The suspect turned slowly, lowering his hood. Quinn’s breath caught. It wasn’t a man. The face was stretched too long, the mouth split ear to ear. Its eyes reflected the lantern light like a cat’s. "Detective," it hissed. "You shouldn’t have followed." Her hand went to her gun. The thing lunged.
created_at"2026-02-19T12:41:50.163Z" 2/19/2026, 12:41:50 PM
model"deepseek/deepseek-chat-v3-0324"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency858
moderation_latency(null)
generation_time62102
tokens_prompt1140
tokens_completion900
native_tokens_prompt1002
native_tokens_completion776
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"
usage0.00113966
router(null)
provider_responses
0
endpoint_id"3a816eb8-7a0b-4a9a-b497-e3a1efb349fc"
id"c104bd88585fa88f747cb3669d99810d"
is_byokfalse
latency858
model_permaslug"deepseek/deepseek-chat-v3-0324"
provider_name"Novita"
status200
api_type"completions"
id"gen-1771504910-cCQA1B7mQuz3KCDc52e0"
upstream_id"c104bd88585fa88f747cb3669d99810d"
total_cost0.00113966
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
totalTags3
adverbTagCount0
adverbTags(empty)
dialogueSentences11
tagDensity0.273
leniency0.545
rawRatio0
effectiveRatio0
81.85% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount551
totalAiIsmAdverbs2
found
0
adverb"reluctantly"
count1
1
adverb"slowly"
count1
highlights
0"reluctantly"
1"slowly"
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)
63.70% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount551
totalAiIsms4
found
0
word"flicked"
count1
1
word"echoing"
count1
2
word"footsteps"
count1
3
word"scanning"
count1
highlights
0"flicked"
1"echoing"
2"footsteps"
3"scanning"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches1
maxInWindow1
found
0
label"air was thick with"
count1
highlights
0"the air was thick with"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences56
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences56
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences64
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen24
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords542
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions3
unquotedAttributions0
matches(empty)
88.02% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions16
wordCount484
uniqueNames10
maxNameDensity1.24
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Quinn"
discoveredNames
Harlow1
Quinn6
Dean1
Street1
Tube1
Londoners1
Saint1
Christopher1
Herrera1
Tomás2
persons
0"Harlow"
1"Quinn"
2"Saint"
3"Christopher"
4"Herrera"
5"Tomás"
places
0"Dean"
1"Street"
globalScore0.88
windowScore1
85.90% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences39
glossingSentenceCount1
matches
0"looked like a human finger bone"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount542
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences64
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs30
mean18.07
std16.35
cv0.905
sampleLengths
054
13
261
338
436
520
62
743
88
942
106
116
1237
132
1412
159
1610
1712
1823
194
2023
2122
2211
2314
243
254
2621
277
286
293
99.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences56
matches
0"was stretched"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs92
matches
0"wasn’t losing"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount5
semicolonCount0
flaggedSentences4
totalSentences64
ratio0.063
matches
0"The suspect—hood up, shoulders hunched—ducked into an alleyway."
1"The stairs opened into a cavernous space—an abandoned Tube station, its tiled walls cracked and stained."
2"She knew him from the files—former paramedic, suspected of supplying underground clinics."
3"The seated figure laughed—a wet, rattling sound."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount493
adjectiveStacks0
stackExamples(empty)
adverbCount11
adverbRatio0.02231237322515213
lyAdverbCount3
lyAdverbRatio0.006085192697768763
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences64
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences64
mean8.47
std4.9
cv0.579
sampleLengths
018
124
28
34
43
58
617
73
821
912
1013
119
1210
136
143
1510
169
1714
182
195
2013
212
2216
2313
2414
258
2610
277
2811
2914
306
316
326
3313
3410
358
362
3712
385
394
402
418
4212
4314
449
454
467
473
4810
493
65.63% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats3
diversityRatio0.421875
totalSentences64
uniqueOpeners27
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences50
matches(empty)
ratio0
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount14
totalSentences50
matches
0"She followed without hesitation."
1"Her voice barely carried over"
2"She grabbed the edge of"
3"She drew her sidearm, flicked"
4"She moved fast, pistol raised,"
5"She moved forward, scanning the"
6"She knew him from the"
7"she said, low"
8"He studied her, then jerked"
9"She didn’t thank him."
10"She pushed through."
11"It wasn’t a man."
12"Its eyes reflected the lantern"
13"Her hand went to her"
ratio0.28
0.00% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount46
totalSentences50
matches
0"The rain came down in"
1"Detective Harlow Quinn sprinted after"
2"The suspect—hood up, shoulders hunched—ducked"
3"She followed without hesitation."
4"Her voice barely carried over"
5"The figure vaulted over a"
6"Quinn didn’t slow."
7"She grabbed the edge of"
8"She drew her sidearm, flicked"
9"The dim interior smelled of"
10"Water dripped from pipes overhead,"
11"A flickering bulb cast long"
12"Footsteps pounded ahead."
13"She moved fast, pistol raised,"
14"The corridor ended at a"
15"A cold draft rose from"
16"Protocol said call for backup."
17"The stairs opened into a"
18"The air hummed with low"
19"The suspect was already weaving"
ratio0.92
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences50
matches(empty)
ratio0
83.33% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences24
technicalSentenceCount2
matches
0"Detective Harlow Quinn sprinted after the figure darting between the crowds on Dean Street, her boots splashing through puddles that soaked through her trousers…"
1"Market stalls lined the platforms, lit by lanterns that cast flickering orange light."
0.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags3
uselessAdditionCount1
matches
0"she said, low"
59.09% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags3
fancyCount1
fancyTags
0"it hissed (hiss)"
dialogueSentences11
tagDensity0.273
leniency0.545
rawRatio0.333
effectiveRatio0.182
77.5505%