Run: 2026-02-19-01KHTRHANSSZZN894AJ1DSY5QK

Run Details

Duration:
21.9s
Cost:
$0.001402
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 Soho pavement into a black mirror that shattered under Detective Harlow Quinn’s boots. Her breath came in sharp bursts, the cold air stinging her lungs as she pushed past a group of startled club-goers huddled under a neon-lit awning. Ahead, the suspect—a wiry figure in a dark hood—darted around the corner, his silhouette swallowed by the alley’s gloom. She didn’t slow. Eighteen years on the force had honed her instincts to a razor’s edge, and tonight, they screamed that this was the break she needed. The Morris case had gone cold, but the threads were there, tangled in the shadows. And this man—this runner—knew something. The alley narrowed, walls slick with condensation and graffiti. The stench of rotting garbage mixed with the metallic bite of rain. Quinn’s fingers brushed the grip of her sidearm, but she didn’t draw. Not yet. A flash of movement. The suspect vaulted over a rusted fire escape ladder, landing with a splash in the flooded alley beyond. Quinn followed, her boots skidding on wet brick as she hauled herself up. The impact jolted through her knees when she dropped down on the other side, but she was already moving, her pulse hammering in her throat. The runner disappeared through a service door, its peeling green paint barely visible under the flickering glow of a dying streetlamp. Quinn shouldered through after him, the door groaning on unoiled hinges. Darkness. The air inside was thick with mildew and something sharper—ozone, maybe, or the acrid tang of old magic. Her hand found the wall, fingers tracing damp concrete as she advanced. A faint green light pulsed ahead, casting jagged shadows. Then, voices. She edged closer, pressing against a support beam as the narrow corridor opened into a cavernous space. The abandoned Tube station stretched before her, its vaulted ceiling lost in shadow. Makeshift stalls lined the platform, their wares illuminated by flickering lanterns and the occasional flash of unnatural light. The Veil Market. Quinn had heard whispers—a place where the city’s underbelly traded in things that didn’t belong in daylight. Her runner was already weaving through the crowd, his hood still up, shoulders hunched. A hand clamped onto her arm. She spun, reaching for her weapon before she registered the face—olive skin, warm brown eyes, a scar running down his left forearm. Tomás Herrera. “You don’t want to be here, detective.” His voice was low, urgent. The Saint Christopher medallion at his throat caught the green light as he leaned in. “This isn’t your kind of hunt.” Quinn jerked her arm free. “That man’s connected to a case. I’m not losing him.” Tomás’ jaw tightened. “Then you’re walking into a grave. The Market doesn’t take kindly to cops—especially not the ones asking the wrong questions.” She didn’t answer. Ahead, the runner ducked behind a stall draped in black velvet, vanishing into the deeper recesses of the platform. Tomás exhaled through his nose. “Fine. But if you’re going in, you’re not doing it blind.” He pulled a bone token from his pocket—yellowed, carved with runes. “Stick close. And for God’s sake, don’t touch anything.” Quinn hesitated. The weight of her badge pressed against her ribs. Then she took the token and stepped into the crowd.
created_at"2026-02-19T10:53:36.83Z" 2/19/2026, 10:53:36 AM
model"deepseek/deepseek-chat-v3"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency1167
moderation_latency(null)
generation_time21598
tokens_prompt1140
tokens_completion895
native_tokens_prompt1002
native_tokens_completion770
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"
usage0.0014018
router(null)
provider_responses
0
endpoint_id"cefb5a1e-9fea-4496-9a64-4e1b11a7cc8c"
id"05c1fa078103e76e1ba9ffb7c847f4f3"
is_byokfalse
latency1167
model_permaslug"deepseek/deepseek-chat-v3"
provider_name"Novita"
status200
api_type"completions"
id"gen-1771498416-Q3fepoAgAFcd63Gi41P0"
upstream_id"05c1fa078103e76e1ba9ffb7c847f4f3"
total_cost0.0014018
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
totalTags1
adverbTagCount0
adverbTags(empty)
dialogueSentences6
tagDensity0.167
leniency0.333
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount555
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)
27.93% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount555
totalAiIsms8
found
0
word"shattered"
count1
1
word"gloom"
count1
2
word"pulse"
count1
3
word"tracing"
count1
4
word"pulsed"
count1
5
word"grave"
count1
6
word"velvet"
count1
7
word"weight"
count1
highlights
0"shattered"
1"gloom"
2"pulse"
3"tracing"
4"pulsed"
5"grave"
6"velvet"
7"weight"
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
narrationSentences41
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences41
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences46
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
totalWords544
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions4
unquotedAttributions0
matches(empty)
76.78% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions17
wordCount478
uniqueNames11
maxNameDensity1.46
worstName"Quinn"
maxWindowNameDensity2
worstWindowName"Quinn"
discoveredNames
Soho1
Detective1
Harlow1
Quinn7
Morris1
Tube1
Veil1
Market1
Herrera1
Saint1
Christopher1
persons
0"Harlow"
1"Quinn"
2"Morris"
3"Herrera"
4"Saint"
5"Christopher"
places
0"Soho"
globalScore0.768
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences28
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount544
matches(empty)
94.20% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences46
matches
0"screamed that this"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs19
mean28.63
std18.02
cv0.63
sampleLengths
066
147
235
360
432
540
62
748
83
931
106
1124
1233
1315
1423
1522
1636
1711
1810
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences41
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs84
matches(empty)
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount8
semicolonCount0
flaggedSentences6
totalSentences46
ratio0.13
matches
0"Ahead, the suspect—a wiry figure in a dark hood—darted around the corner, his silhouette swallowed by the alley’s gloom."
1"And this man—this runner—knew something."
2"The air inside was thick with mildew and something sharper—ozone, maybe, or the acrid tang of old magic."
3"Quinn had heard whispers—a place where the city’s underbelly traded in things that didn’t belong in daylight."
4"She spun, reaching for her weapon before she registered the face—olive skin, warm brown eyes, a scar running down his left forearm."
5"“Fine. But if you’re going in, you’re not doing it blind.” He pulled a bone token from his pocket—yellowed, carved with runes."
94.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount463
adjectiveStacks1
stackExamples
0"faint green light pulsed"
adverbCount9
adverbRatio0.019438444924406047
lyAdverbCount4
lyAdverbRatio0.008639308855291577
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences46
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences46
mean11.83
std7.2
cv0.609
sampleLengths
021
126
219
33
424
515
65
79
812
912
102
114
1218
1313
1425
1521
1611
171
1818
1912
209
212
2217
2313
2418
253
2617
2714
286
2922
302
3112
3215
336
345
3510
363
3720
383
3919
405
4122
429
432
449
4510
60.87% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats2
diversityRatio0.391304347826087
totalSentences46
uniqueOpeners18
92.59% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences36
matches
0"Then she took the token"
ratio0.028
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount9
totalSentences36
matches
0"Her breath came in sharp"
1"She didn’t slow."
2"Her hand found the wall,"
3"She edged closer, pressing against"
4"Her runner was already weaving"
5"She spun, reaching for her"
6"His voice was low, urgent"
7"She didn’t answer."
8"He pulled a bone token"
ratio0.25
43.33% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount30
totalSentences36
matches
0"The rain came down in"
1"Her breath came in sharp"
2"She didn’t slow."
3"The Morris case had gone"
4"The alley narrowed, walls slick"
5"The stench of rotting garbage"
6"Quinn’s fingers brushed the grip"
7"A flash of movement."
8"The suspect vaulted over a"
9"Quinn followed, her boots skidding"
10"The impact jolted through her"
11"The runner disappeared through a"
12"Quinn shouldered through after him,"
13"The air inside was thick"
14"Her hand found the wall,"
15"A faint green light pulsed"
16"She edged closer, pressing against"
17"The abandoned Tube station stretched"
18"The Veil Market."
19"Quinn had heard whispers—a place"
ratio0.833
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences36
matches(empty)
ratio0
80.75% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences23
technicalSentenceCount2
matches
0"The rain came down in sheets, turning the Soho pavement into a black mirror that shattered under Detective Harlow Quinn’s boots."
1"The impact jolted through her knees when she dropped down on the other side, but she was already moving, her pulse hammering in her throat."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags1
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags0
85.6817%