Run: 2026-09-03-01M1KCVSD9E6B7WTK1HFC6FFTJ

Run Details

Duration:
1m 54s
Cost:
$0.027954
Provider:
Fireworks

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 had been falling for hours, turning Soho’s neon into smeared paint on black glass. Harlow Quinn stood outside the Raven’s Nest with water crawling down the collar of her coat and her closely cropped salt-and-pepper hair plastered to her skull. The green sign above the bar buzzed like a trapped insect. She had been watching Tomás Herrera for three days, waiting for him to make a mistake. That night he made it. He came out of the bar at 11:47, pausing just long enough to light a cigarette under the awning. The flame caught the Saint Christopher medallion around his neck. He had olive skin, short dark curls flattened by the damp, and his left forearm carried the old knife scar she remembered from his file. Harlow noted it all without moving from the doorway across the street. The worn leather watch on her left wrist ticked against her pulse. He looked right, then left. Then he began walking north toward Camden, his hands in his pockets. She followed. The tail took ninety minutes. He changed buses twice, doubled back through side streets, and once stood on a corner for so long Harlow thought he had spotted her. But he moved again, and she stayed with him, keeping a block back, her sharp jaw set against the cold. The city grew quieter. The rain kept coming. In Camden, he turned off a main road into a narrow lane lined with closed market stalls and wheeled rubbish bins. Harlow knew this area—an old Tube station had been shut here years earlier. She was already reaching for her radio. “Quinn to dispatch,” she said. “I’m in pursuit of Tomás Herrera, moving into the old station off Castle Street. Request backup.” The radio crackled and returned nothing. Tomás scrambled over a section of chain-link fence that had been peeled back like a bandage. Harlow went after him, landing hard on a pavement covered in broken glass and wet leaves. She was forty metres behind. He crossed the dead concourse, past ticket windows bricked over with cinderblock, and threw himself through a propped-open service door. Harlow followed. Inside, the air changed. It smelled of standing water and old metal, of earth that had not breathed in a long time. A staircase led down. She took the steps two at a time, her boots loud and sure. At the bottom, an arched steel door stood ajar. Tomás was already through it, but as she reached the door she saw him glance back. In the green glow of a caged light, his face was strained, not panicked. He held the door for half a second too long—long enough for her to see the pale disc in his other hand, carved from bone—and then he let it swing shut. Harlow threw her shoulder against it. The door was heavier than it should have been. It resisted like a living thing, then gave with a sound like a rib separating. She stumbled through and found herself standing on the platform of an abandoned Tube station. The Veil Market. She didn’t know the name yet, but she understood at once that this was not a normal place. Stalls crowded the platform, tents of waxed canvas and striped cloth hung between black iron pillars. Lanterns threw a light the colour of old ivory. The air smelled of clove and hot wax and electrical fire. Faces turned as she passed. Some of them were wrong in ways her mind refused to hold onto: a woman with too many knuckles counting coins, a man whose shadow did not follow his body. Harlow told herself they were costumes, prosthetics, a cult. Her left hand drifted to the retention strap of her holster. Tomás moved through the crowd like a fish in current. She could see his medallion flickering between shoulders and elbows. She pushed after him, past a stall selling jars packed with liquid and small floating shapes, past a table where old books were chained by their spines. A child offered her a jar of raw luck. She knocked the jar aside. It shattered and the boy hissed through teeth filed into points. “Police!” she shouted. “Move!” No one moved. The market simply adjusted around her and closed again. Tomás ducked behind an ancient ticket booth and turned down a service tunnel that opened black at the edge of the platform. Above the tunnel mouth, someone had painted a sigil in white—a circle with a vertical slash through it. Harlow stopped. She had seen that mark before. Three years ago. In a flat in Bethnal Green where her partner, DS Morris, had died from no wound the coroner could explain. The same sigil had been scratched into the windowsill above his body. Three years of dead ends, and now it was here, on a wall beneath London, glowing faintly in the lantern light. A hand caught her elbow. She spun, breaking the grip. A vendor with silver teeth smiled at her. “You need a token for this part, detective,” he said. “Bone. You don’t have bone.” She shook him off. “That man—where does that tunnel lead?” The vendor’s smile widened, but his eyes slid away. “Follow him, and you won’t come back the same.” Harlow looked toward the tunnel. It was dark and low, the walls damp, the tracks rusted. The market’s noise had dropped to a pressure in her ears. She could still hear Tomás’s footsteps, quick and light, receding into the earth. Beyond that, water dripped. Something else shifted in the dark, wet and patient. She checked her phone. No signal. Her radio gave only static. At the top of the station, the rain and the ordinary city waited, with backup she might never get in time and a search warrant that would take hours. But Tomás Herrera would be gone by then. The market would fold up and vanish. She had seen enough to understand that much. Harlow pressed her palm against the painted sigil. The white paint was cold, but beneath it the stone seemed to hum, a low current that travelled up her wrist and stopped beneath her watch. She thought of Morris. The smell of his blood in that Bethnal Green flat. The way his eyes had been open and empty, not afraid, but surprised. She checked her weapon. She squared her shoulders with the military carriage that had never left her. “No more dead ends,” she said quietly. Then Harlow Quinn stepped into the tunnel after Tomás Herrera, and the dark swallowed her whole.
created_at"2026-09-03T10:26:04.858Z" 9/3/2026, 10:26:04 AM
model"deepseek/deepseek-v4-pro-20260813"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency763
moderation_latency(null)
generation_time113513
tokens_prompt888
tokens_completion7645
native_tokens_prompt906
native_tokens_completion6757
native_tokens_completion_images(null)
native_tokens_reasoning5384
native_tokens_cached0
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)
usage0.02795364
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.19.0; linux; x64))"
http_referer(null)
request_id"req-1788431164-O4lcPW1gx2m4wQhQvd2S"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1788431164-Sl9KCXY9sTIijTZ1CEck"
upstream_id"chatcmpl-0ce6f6c1678044bd9ad64748bdcbacdb"
provider_responses
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endpoint_id"ef03a21f-1e70-4f34-9fd9-c6e823f2fcfd"
id"chatcmpl-0ce6f6c1678044bd9ad64748bdcbacdb"
is_byokfalse
latency763
model_permaslug"deepseek/deepseek-v4-pro-20260813"
provider_name"Fireworks"
status200
total_cost0.02795364
cache_discount(null)
upstream_inference_cost0
provider_name"Fireworks"
response_cache_source_id(null)
data_region"global"
workspace_id"97e315e5-d303-487d-83c1-83180e8a13d4"

Evaluation details

Result Evaluator Details Meta Data
0.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags4
adverbTagCount1
adverbTags
0"she said quietly [quietly]"
dialogueSentences9
tagDensity0.444
leniency0.889
rawRatio0.25
effectiveRatio0.222
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1096
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)
86.31% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1096
totalAiIsms3
found
0
word"pulse"
count1
1
word"shattered"
count1
2
word"footsteps"
count1
highlights
0"pulse"
1"shattered"
2"footsteps"
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
narrationSentences94
matches(empty)
97.26% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount1
narrationSentences94
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)
analyzedSentences99
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen33
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1091
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions4
unquotedAttributions0
matches(empty)
92.20% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions43
wordCount1038
uniqueNames17
maxNameDensity1.16
worstName"Harlow"
maxWindowNameDensity2
worstWindowName"Harlow"
discoveredNames
Soho1
Quinn2
Raven1
Nest1
Tomás8
Herrera3
Saint1
Christopher1
Camden2
Harlow12
Tube2
Veil1
Market1
Bethnal2
Green2
Morris2
London1
persons
0"Quinn"
1"Tomás"
2"Herrera"
3"Saint"
4"Christopher"
5"Harlow"
6"Market"
7"Morris"
places
0"Soho"
1"Raven"
2"Camden"
3"Bethnal"
4"Green"
5"London"
globalScore0.922
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences64
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches1
per1kWords0.917
wordCount1091
matches
0"not afraid, but surprised"
99.33% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount2
totalSentences99
matches
0"seen that mark"
1"understand that much"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs30
mean36.37
std31.19
cv0.858
sampleLengths
074
178
217
32
457
541
621
76
857
92
10109
116
1239
133
14109
1572
164
1712
1842
1962
2018
2115
2210
2318
2453
2563
2661
2717
287
2916
82.87% Passive voice overuse
Target: ≤2% passive sentences
passiveCount6
totalSentences94
matches
0"been shut"
1"been peeled"
2"was strained"
3"were chained"
4"been scratched"
5"been open"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs181
matches
0"was already reaching"
56.28% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount4
semicolonCount0
flaggedSentences3
totalSentences99
ratio0.03
matches
0"Harlow knew this area—an old Tube station had been shut here years earlier."
1"He held the door for half a second too long—long enough for her to see the pale disc in his other hand, carved from bone—and then he let it swing shut."
2"Above the tunnel mouth, someone had painted a sigil in white—a circle with a vertical slash through it."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1046
adjectiveStacks0
stackExamples(empty)
adverbCount35
adverbRatio0.033460803059273424
lyAdverbCount5
lyAdverbRatio0.004780114722753346
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences99
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences99
mean11.02
std7.09
cv0.643
sampleLengths
016
126
211
316
45
519
610
725
812
912
105
1112
122
135
1424
1520
164
174
1821
1913
207
215
2216
236
2416
2516
265
2720
282
294
3018
314
3213
339
3416
3514
3631
376
389
3915
4015
413
4218
4316
449
4511
465
4730
489
4911
46.46% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats4
diversityRatio0.30303030303030304
totalSentences99
uniqueOpeners30
76.63% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences87
matches
0"Then he began walking north"
1"Then Harlow Quinn stepped into"
ratio0.023
77.47% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount31
totalSentences87
matches
0"She had been watching Tomás"
1"He came out of the"
2"He had olive skin, short"
3"He looked right, then left."
4"He changed buses twice, doubled"
5"She was already reaching for"
6"She was forty metres behind."
7"He crossed the dead concourse,"
8"It smelled of standing water"
9"She took the steps two"
10"He held the door for"
11"It resisted like a living"
12"She stumbled through and found"
13"She didn’t know the name"
14"Her left hand drifted to"
15"She could see his medallion"
16"She pushed after him, past"
17"She knocked the jar aside."
18"It shattered and the boy"
19"She had seen that mark"
ratio0.356
46.21% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount72
totalSentences87
matches
0"The rain had been falling"
1"Harlow Quinn stood outside the"
2"The green sign above the"
3"She had been watching Tomás"
4"That night he made it."
5"He came out of the"
6"The flame caught the Saint"
7"He had olive skin, short"
8"Harlow noted it all without"
9"The worn leather watch on"
10"He looked right, then left."
11"The tail took ninety minutes."
12"He changed buses twice, doubled"
13"The city grew quieter."
14"The rain kept coming."
15"Harlow knew this area—an old"
16"She was already reaching for"
17"The radio crackled and returned"
18"Tomás scrambled over a section"
19"Harlow went after him, landing"
ratio0.828
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences87
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences47
technicalSentenceCount2
matches
0"Tomás scrambled over a section of chain-link fence that had been peeled back like a bandage."
1"She squared her shoulders with the military carriage that had never left her."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags4
uselessAdditionCount0
matches(empty)
38.89% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags4
fancyCount1
fancyTags
0"she shouted (shout)"
dialogueSentences9
tagDensity0.444
leniency0.889
rawRatio0.25
effectiveRatio0.222
83.3302%