Run: 2026-09-03-01M1KRFAK57DX6SV6TNH1DYN8D

Run Details

Duration:
19.2s
Cost:
$0.002563
Provider:
DeepInfra

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
Rain came down hard enough to turn Soho into a smeared photograph. Detective Harlow Quinn kept her collar up and her stride even, military precision in the way she moved through the crowd spilling out of The Raven's Nest. The distinctive green neon sign above the entrance buzzed and stuttered against the wet pavement, throwing sickly light across the faces of the night regulars. The bar was dim inside, walls covered with old maps and black-and-white photographs, and Quinn had spent three hours watching the door from across the street, watching who came in and who left with their shoulders too tight. Tomás Herrera came out last. She knew him by the file photos from the hospital board review, by the police sketch artist's notes, by the way he held his left arm close to his body as if the scar running along his forearm still ached. Short curly dark brown hair plastered to his forehead, olive skin darkened by the neon, Saint Christopher medallion catching light at his throat. He was 29, former paramedic, licensed until he started treating patients the NHS wouldn't acknowledge. Now he provided off-the-books medical care for the clique. Quinn had been building a case against the clique for eighteen months. She didn't know what they were, only that they moved money through fronts, that injuries healed too fast, that witnesses changed their stories. She suspected criminal activity. She was not wrong, she was just late. Herrera didn't look back. He slipped into the rain like a man who knew the streets. Quinn fell in half a dozen paces behind him, hands in her coat pockets, worn leather watch on her left wrist ticking against her palm. The watch was old, a gift from DS Morris before the night he went missing three years ago on a case that had ended with a body that wasn't there and a report that read like a lie. She never took it off. He moved fast for a man who'd been on his feet all night. Down Wardour Street, past shuttered shops and delivery bikes skidding on slick asphalt. Quinn matched him without running, letting the crowd provide cover, cutting through a kebab shop doorway when he cut left, emerging just as he ducked under a low awning. He was leading her somewhere. The rain was cold enough to bite. It ran in rivulets down her closely cropped salt-and-pepper hair, pooled at the cuffs of her coat. Her sharp jaw was set. Brown eyes tracked him through reflections in shop windows. She didn't want a collar tonight. She wanted to see where he went. He turned north, away from the West End lights toward Camden. That made no sense for a man who lived in Shoreditch. The streetlamps thinned. The music from the bars thinned. Herrera's pace never slackened. At the edge of Camden Market the streets grew narrower, grittier. Graffiti ran up brick like fever. Herrera stopped at a service alley behind a closed record store and vanished through a metal door propped open with a brick. Quinn slowed. She could call it in, wait for backup, get a warrant for the premises. Protocol. Protocol had cost Morris. She crossed the alley before she could change her mind. The door led to concrete steps descending into darkness. Air that smelled of damp earth and old metal rose to meet her. No sign. No light except the spill from the alley mouth above. She paused at the top step, listening. Footsteps echoed below, quick, then gone. Quinn went down. The stairwell was narrow, service access, probably built for maintenance crews who never came anymore. Water dripped from pipes overhead. Halfway down she passed a rusted sign, TUBULAR, and understood. The abandoned Tube station beneath Camden. The Veil Market. She had read about it in a seized notebook, a rumor in a debrief from a constable who'd taken a bribe and never talked again. A hidden supernatural black market that sold enchanted goods, banned alchemical substances, and information. It moved locations every full moon. Entry requirement: bone token. She had no token. The passage opened into a vaulted platform, brick arches black with age. A low amber glow pulsed from somewhere further down the tunnel, and the air was thick with bodies. Not commuters. Traders. Figures in coats and masks, some with things moving beneath their skin, others with eyes that caught light wrong. The market was alive. Herrera was ahead of her, twenty meters down the platform, moving toward a stall where a woman in a long coat weighed something in a silver scale. He didn't run. He was buying, or paying. Quinn's radio was dead down here. No signal. She could turn back, surface, and come back with a team and lights and legal authority. That was the right move. The safe move. The platform trembled as a train never came. Somewhere a vendor shouted in a language Quinn didn't know. The sound of rain above was muffled to a thud. She thought of Morris's last call, garbled, saying it wasn't human. She thought of the files on Herrera, the patients who lived after they should have died, the medallion he wore like a promise. She thought of eighteen years of decorated service and how it hadn't taught her what to do when the ground dropped out from under the law. Herrera turned his head. He saw her at the top of the stairs. Warm brown eyes widened, not with surprise but with resignation. He lifted a hand, just slightly, as if to say wait. Then he turned back to the stall and handed over a small pouch. The woman gave him something in return, a flat white object that looked like bone. Token. He slipped it into his palm and moved on, deeper into the market, toward a shadowed archway where the amber light didn't reach. He didn't look back again. Quinn stood at the threshold between the world she understood and the one that had taken her partner. Rain hammered the street above. Below, the market breathed. She could let him go. She could log the sighting and build the case the proper way. She pulled her coat tighter, felt the leather watch press into her wrist, and stepped down onto the platform. The cold bit deeper. The crowd parted without looking at her. She followed Herrera's silhouette into the dark, one step at a time, into unfamiliar and potentially dangerous territory, because the only way to learn what had happened to Morris was to go where he had gone.
created_at"2026-09-03T13:48:59.372Z" 9/3/2026, 1:48:59 PM
model"meta/muse-glimmer-30b-20260810"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency127
moderation_latency(null)
generation_time19118
tokens_prompt888
tokens_completion2472
native_tokens_prompt830
native_tokens_completion2067
native_tokens_completion_images(null)
native_tokens_reasoning889
native_tokens_cached640
num_media_prompt(null)
num_input_audio_prompt(null)
num_media_completion0
num_search_results(null)
num_fetches(null)
web_search_engine(null)
origin"https://ncbench.com/"
is_byokfalse
finish_reason"stop"
native_finish_reason"stop"
service_tier(null)
usage0.002563
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.19.0; linux; x64))"
http_referer(null)
request_id"req-1788443339-ZvEXocE1TlxrBImFeU7M"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1788443339-YDmx5onAAscs9w3o8xCh"
upstream_id"chatcmpl-RwMVLUzAO7rf1KHxjFZW8wu1"
provider_responses
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endpoint_id"a9912acb-568e-4147-8ed3-5d20aea22135"
id"chatcmpl-RwMVLUzAO7rf1KHxjFZW8wu1"
is_byokfalse
latency59
model_permaslug"meta/muse-glimmer-30b-20260810"
provider_name"DeepInfra"
status200
total_cost0.002563
cache_discount0.0001664
upstream_inference_cost0
provider_name"DeepInfra"
response_cache_source_id(null)
data_region"global"
workspace_id"97e315e5-d303-487d-83c1-83180e8a13d4"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags0
95.43% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1095
totalAiIsmAdverbs1
found
0
adverb"slightly"
count1
highlights
0"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)
81.74% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1095
totalAiIsms4
found
0
word"footsteps"
count1
1
word"echoed"
count1
2
word"pulsed"
count1
3
word"trembled"
count1
highlights
0"footsteps"
1"echoed"
2"pulsed"
3"trembled"
66.67% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches2
maxInWindow2
found
0
label"eyes widened/narrowed"
count1
1
label"air was thick with"
count1
highlights
0"eyes widened"
1"the air was thick with"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells1
narrationSentences96
matches
0"t with surprise"
98.21% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount1
narrationSentences96
filterMatches
0"watch"
hedgeMatches
0"happened to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences96
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen40
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1095
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions3
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions40
wordCount1095
uniqueNames18
maxNameDensity0.91
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Quinn"
discoveredNames
Soho1
Harlow1
Quinn10
Raven1
Nest1
Herrera8
Saint1
Christopher1
Morris4
Wardour1
Street1
West1
End1
Camden3
Shoreditch1
Market2
Tube1
Veil1
persons
0"Harlow"
1"Quinn"
2"Raven"
3"Herrera"
4"Saint"
5"Christopher"
6"Morris"
places
0"Soho"
1"Wardour"
2"Street"
3"West"
4"End"
5"Camden"
6"Shoreditch"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences64
glossingSentenceCount1
matches
0"looked like bone"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches1
per1kWords0.913
wordCount1095
matches
0"not with surprise but with resignation"
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences96
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs27
mean40.56
std28.19
cv0.695
sampleLengths
012
190
25
387
447
584
660
751
835
956
104
1151
126
133
1488
154
1656
1735
1832
1928
2060
2162
221
2328
2427
2517
2666
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences96
matches
0"was muffled"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount2
totalVerbs194
matches
0"was leading"
1"was buying"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences96
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1101
adjectiveStacks0
stackExamples(empty)
adverbCount32
adverbRatio0.029064486830154404
lyAdverbCount8
lyAdverbRatio0.007266121707538601
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences96
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences96
mean11.41
std8.96
cv0.785
sampleLengths
012
127
225
338
45
540
623
715
89
912
1023
114
128
134
1412
1525
1638
175
1813
1913
2029
215
227
2317
245
259
266
277
2811
2911
303
316
324
3311
346
3522
362
3714
381
394
4010
419
4213
432
4410
457
466
473
4815
495
40.63% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats16
diversityRatio0.34375
totalSentences96
uniqueOpeners33
74.91% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences89
matches
0"Somewhere a vendor shouted in"
1"Then he turned back to"
ratio0.022
67.19% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount34
totalSentences89
matches
0"She knew him by the"
1"He was 29, former paramedic,"
2"She didn't know what they"
3"She suspected criminal activity."
4"She was not wrong, she"
5"He slipped into the rain"
6"She never took it off."
7"He moved fast for a"
8"He was leading her somewhere."
9"It ran in rivulets down"
10"Her sharp jaw was set."
11"She didn't want a collar"
12"She wanted to see where"
13"He turned north, away from"
14"She could call it in,"
15"She crossed the alley before"
16"She paused at the top"
17"She had read about it"
18"It moved locations every full"
19"She had no token."
ratio0.382
21.80% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount78
totalSentences89
matches
0"Rain came down hard enough"
1"Detective Harlow Quinn kept her"
2"The distinctive green neon sign"
3"The bar was dim inside,"
4"Tomás Herrera came out last."
5"She knew him by the"
6"He was 29, former paramedic,"
7"Quinn had been building a"
8"She didn't know what they"
9"She suspected criminal activity."
10"She was not wrong, she"
11"Herrera didn't look back."
12"He slipped into the rain"
13"Quinn fell in half a"
14"The watch was old, a"
15"She never took it off."
16"He moved fast for a"
17"Quinn matched him without running,"
18"He was leading her somewhere."
19"The rain was cold enough"
ratio0.876
56.18% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount1
totalSentences89
matches
0"Now he provided off-the-books medical"
ratio0.011
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences48
technicalSentenceCount2
matches
0"She knew him by the file photos from the hospital board review, by the police sketch artist's notes, by the way he held his left arm close to his body as if the…"
1"A hidden supernatural black market that sold enchanted goods, banned alchemical substances, and information."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags0
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags0
90.0917%