Run: 2026-02-17-01KHNFMANMWYNHVECVT1JDKNYH

Run Details

Duration:
13.2s
Cost:
$0.000246
Provider:
Clarifai

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 lashed thepavement like a thousand tiny fists, turning the London street into a mirror of wet asphalt and blurred neon. Detective Harlow Quinn’s breath misted in the chill air as she pushed her salt-and-pepper cropped hair from her sharp jawline, the worn leather watch on her left wrist ticking a frantic rhythm against her wrist. Her boots splashed through a puddle, the sound swallowed by the relentless drumming of the downpour. Ahead, a figure darted between the yellow glow of a streetlamp, vanishing around the corner onto a narrow alley choked with overflowing dumpsters and the sour tang of decay. Quinn’s military precision kicked in. She didn’t run, but moved with a controlled urgency, her long coat flapping open to reveal the dark blue uniform beneath. The alley was a labyrinth of shadows, the only light coming from the occasional flickering sign for a closed kebab shop. She followed the faint, frantic scuff of footsteps, her senses honed to a razor’s edge. The figure was fast, but Quinn’s years of tracking criminals through the Metropolitan’s most dangerous precincts had taught her patience. She waited, crouched low behind a rusted dumpster, her eyes scanning the gloom. The figure emerged, slipping through a rusted fire escape ladder and onto a fire escape. Quinn followed, her boots silent on the metal rungs. They climbed, the city lights below a distant, blurred constellation. At the top, the figure vanished into the darkness of a rooftop. Quinn paused, her breath catching in her throat. The view was breathtaking – the sprawling city lights of London, a sea of humanity below. But the figure was gone, swallowed by the night. A low, guttural growl echoed from the shadows behind her. Quinn spun, her hand instinctively drifting towards her holster. A figure emerged from the darkness, silhouetted against the city lights. It wasn’t the suspect. It was Tomás Herrera, his short, curly dark hair plastered to his olive skin, a Saint Christopher medallion glinting weakly in the rain. A jagged scar, pale against his tanned forearm, traced a path from wrist to elbow. He looked terrified, his eyes wide with a primal fear. “Harlan?” he whispered, his voice thick with rain and strain. “You shouldn’t be here.” “Where is he?” Quinn demanded, her voice low and dangerous, her gaze flicking back towards the rooftop edge. “The one who ran.” Herrera shook his head, backing away. “I don’t know. He just… disappeared. Like smoke.” He gestured vaguely towards the rooftop. “This isn’t the place for you, Detective. Not tonight.” Quinn’s gaze hardened. The rain soaked through her coat, chilling her to the bone, but the fire in her eyes remained. Three years ago, her partner, DS Morris, had vanished under circumstances she still didn’t understand, circumstances that had supernatural origins she didn’t yet comprehend. This chase, this feeling of being hunted, it echoed that loss. She couldn’t turn away now. “He’s connected to this,” she said, her voice tight. “To whatever this is.” She gestured towards the rooftop, then back towards the alley. “Where does he go?” Herrera’s eyes darted nervously. “There’s a place. Below. The Veil Market. It moves. Every full moon. You need a bone token to get in. It’s… not safe, Detective. Not for you.” Quinn’s brow furrowed. The Veil Market. She’d heard whispers, rumors of a hidden supernatural black market beneath the city, dealing in enchanted goods and forbidden knowledge. But entering it? It was uncharted territory, dangerous even for her. Yet, the suspect had vanished into it. The bone token requirement meant it was heavily guarded, exclusive. Someone powerful was involved. “Where?” she pressed, ignoring the chill and the warning in Herrera’s eyes. “Follow me,” Herrera said, his voice barely a whisper. He turned and melted back into the shadows of the alley, his footsteps silent on the wet pavement. Quinn hesitated for only a second, the weight of her watch on her wrist a constant reminder of the ticking clock, the unresolved past, and the dangerous path she was about to tread. The rain continued to fall, washing the city streets clean, but the shadows held secrets, and Quinn was about to step into one of them. She drew her coat tighter and followed Herrera into the darkness, the neon sign of The Raven’s Nest blinking a distant, indifferent green above them, unaware of the storm brewing beneath its neon glow.
created_at"2026-02-17T09:41:59.552938+00:00" 2/17/2026, 9:41:59 AM
model"arcee-ai/trinity-mini-20251201"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency115
moderation_latency(null)
generation_time13113
tokens_prompt914
tokens_completion1749
native_tokens_prompt787
native_tokens_completion1403
native_tokens_completion_images(null)
native_tokens_reasoning585
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"
usage0.000245865
router(null)
provider_responses
0
id"0bbde49dfc824a9191ffca55c912185e"
status200
is_byokfalse
latency115
endpoint_id"73d82c49-90ae-4223-a860-83ed0810821f"
provider_name"Clarifai"
model_permaslug"arcee-ai/trinity-mini-20251201"
api_type"completions"
id"gen-1771321305-c9YtWg3AOLwVdNt1rNFX"
upstream_id"0bbde49dfc824a9191ffca55c912185e"
total_cost0.000245865
cache_discount(null)
upstream_inference_cost0
provider_name"Clarifai"

Evaluation details

Result Evaluator Details Meta Data
78.57% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags7
adverbTagCount1
adverbTags
0"He gestured vaguely [vaguely]"
dialogueSentences12
tagDensity0.583
leniency1
rawRatio0.143
effectiveRatio0.143
93.17% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount732
totalAiIsmAdverbs1
found
0
adverb"nervously"
count1
highlights
0"nervously"
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)
0.00% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount732
totalAiIsms15
found
0
word"chill"
count2
1
word"footsteps"
count2
2
word"scanning"
count1
3
word"gloom"
count1
4
word"echoed"
count2
5
word"glinting"
count1
6
word"traced"
count1
7
word"chilling"
count1
8
word"furrowed"
count1
9
word"whisper"
count1
10
word"weight"
count1
11
word"reminder"
count1
highlights
0"chill"
1"footsteps"
2"scanning"
3"gloom"
4"echoed"
5"glinting"
6"traced"
7"chilling"
8"furrowed"
9"whisper"
10"weight"
11"reminder"
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
narrationSentences50
matches
0"looked terrified"
85.71% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount0
narrationSentences50
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences55
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen37
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords731
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions5
unquotedAttributions0
matches(empty)
66.67% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions29
wordCount660
uniqueNames13
maxNameDensity1.67
worstName"Quinn"
maxWindowNameDensity2.5
worstWindowName"Quinn"
discoveredNames
London2
Harlow1
Quinn11
Metropolitan1
Tomás1
Herrera6
Saint1
Christopher1
Morris1
Veil1
Market1
Raven1
Nest1
persons
0"Harlow"
1"Quinn"
2"Tomás"
3"Herrera"
4"Saint"
5"Christopher"
6"Morris"
7"Raven"
8"Nest"
places
0"London"
1"Metropolitan"
globalScore0.667
windowScore0.833
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences40
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount731
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences55
matches
0"echoed that loss"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs13
mean56.23
std34.96
cv0.622
sampleLengths
0102
195
279
382
414
522
629
761
827
931
1058
1112
12119
91.23% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences50
matches
0"was gone"
1"being hunted"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs107
matches
0"was breathtaking –"
90.91% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount1
semicolonCount0
flaggedSentences1
totalSentences55
ratio0.018
matches
0"The view was breathtaking – the sprawling city lights of London, a sea of humanity below."
88.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount661
adjectiveStacks2
stackExamples
0"short, curly dark hair"
1"distant, indifferent green above them,"
adverbCount17
adverbRatio0.025718608169440244
lyAdverbCount9
lyAdverbRatio0.01361573373676248
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences55
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences55
mean13.29
std8.37
cv0.63
sampleLengths
022
135
216
329
45
521
621
715
820
913
1015
119
1210
1312
148
1516
169
1710
189
1911
204
2123
2215
2310
2410
254
2618
274
286
2914
309
313
3218
3324
3411
355
369
3714
384
394
4027
413
423
4320
443
458
467
4710
484
4912
70.91% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats1
diversityRatio0.43636363636363634
totalSentences55
uniqueOpeners24
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences50
matches(empty)
ratio0
76.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount18
totalSentences50
matches
0"Her boots splashed through a"
1"She didn’t run, but moved"
2"She followed the faint, frantic"
3"She waited, crouched low behind"
4"They climbed, the city lights"
5"It wasn’t the suspect."
6"It was Tomás Herrera, his"
7"He looked terrified, his eyes"
8"he whispered, his voice thick"
9"He gestured vaguely towards the"
10"She couldn’t turn away now."
11"she said, her voice tight"
12"She gestured towards the rooftop,"
13"She’d heard whispers, rumors of"
14"It was uncharted territory, dangerous"
15"she pressed, ignoring the chill"
16"He turned and melted back"
17"She drew her coat tighter"
ratio0.36
20.00% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount44
totalSentences50
matches
0"The rain lashed thepavement like"
1"Detective Harlow Quinn’s breath misted"
2"Her boots splashed through a"
3"Quinn’s military precision kicked in."
4"She didn’t run, but moved"
5"The alley was a labyrinth"
6"She followed the faint, frantic"
7"The figure was fast, but"
8"She waited, crouched low behind"
9"The figure emerged, slipping through"
10"Quinn followed, her boots silent"
11"They climbed, the city lights"
12"Quinn paused, her breath catching"
13"The view was breathtaking –"
14"A low, guttural growl echoed"
15"Quinn spun, her hand instinctively"
16"A figure emerged from the"
17"It wasn’t the suspect."
18"It was Tomás Herrera, his"
19"A jagged scar, pale against"
ratio0.88
100.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount1
totalSentences50
matches
0"Yet, the suspect had vanished"
ratio0.02
89.95% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences27
technicalSentenceCount2
matches
0"Three years ago, her partner, DS Morris, had vanished under circumstances she still didn’t understand, circumstances that had supernatural origins she didn’t ye…"
1"She drew her coat tighter and followed Herrera into the darkness, the neon sign of The Raven’s Nest blinking a distant, indifferent green above them, unaware of…"
0.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags7
uselessAdditionCount3
matches
0"he whispered, his voice thick with rain and strain"
1"she said, her voice tight"
2"Herrera said, his voice barely a whisper"
0.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags5
fancyCount3
fancyTags
0"he whispered (whisper)"
1"Quinn demanded (demand)"
2"she pressed (press)"
dialogueSentences12
tagDensity0.417
leniency0.833
rawRatio0.6
effectiveRatio0.5
78.3705%