Run: 2026-04-24-01KPZV32HP511VJAJMSHKXGZ02

Run Details

Duration:
28.9s
Cost:
$0.055195
Provider:
Amazon Bedrock

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 came down in sheets, hammering the Soho pavement until the gutters ran black. Quinn was already three streets behind when she caught the flash of olive skin under a streetlamp — a man ducking into a doorway on Greek Street, one hand pressed to his ribs, the other clutching something small enough to hide in a fist. Tomás Herrera. She'd recognize him anywhere now. Eighteen months of photographs, phone records, grainy CCTV. The paramedic who wasn't a paramedic anymore. The man who'd been seen leaving the Raven's Nest the night a witness turned up in the Thames with no water in his lungs. "Herrera!" she shouted, and he ran. Of course he ran. She cut her stride long, her boots slamming puddles into geysers, her coat heavy as chainmail where the rain had gotten beneath the collar. Forty-one years old and she still ran like she had at twenty-three, the way the Met had taught her, the way the army had taught her before that — elbows tight, chin down, eyes fixed a half-second ahead of where her feet were going. Her watch strap chafed at her left wrist. She barely noticed. Herrera was fast. Fast and favoring his right side — that old knife scar, maybe, or something fresher she hadn't seen yet. He vaulted a bollard without breaking rhythm and hooked left into an alley she didn't know, one of those narrow Soho cuts that ended at a wall or a locked door or a courtyard full of wheelie bins. She pulled her radio up and thumbed it. "DI Quinn, in foot pursuit, male IC2, dark jacket, heading south from Greek Street toward — " She squinted through the rain. " — Shaftesbury. Request backup." Dispatch crackled something she couldn't make out. She let the radio drop and kept running. The alley spat them out onto Shaftesbury Avenue, where the theatre crowds were thinning under umbrellas and taxis hissed past in a slurry of red light. Herrera threaded between a couple holding a Playbill over their heads, ducked a bus, and was across the road before Quinn had cleared the kerb. She followed, raising a hand at a black cab that braked hard enough to throw water over her boots. The driver leaned on the horn. She didn't look back. North now. He was heading north. Past the station, up into the warren above Tottenham Court Road. She lost him once — a bad second where the rain thickened and the crowds parted around some street performer and her heart climbed up into her throat — and then she caught him again, a pale flash of Saint Christopher catching streetlamp as he turned. He'd pulled the medallion out of his shirt. Praying, maybe. Or gripping it. Running men grabbed at whatever anchor they had. He was heading somewhere specific. That was the part that sharpened her. In eighteen years she'd chased enough frightened men to know the difference between a panicked scatter and a planned retreat. Herrera was threading routes. He knew these streets, these mews, these shortcuts the way she knew the corridors back at Charing Cross nick. Every turn he took shaved another corner off her line. By the time they'd crossed Euston Road she was twenty yards behind him and losing ground. Camden. He was making for Camden. The rain eased for a moment, then came back harder, and the streetlights turned to halos. She felt the old ache in her left knee — that case three years ago, the case, the one where Morris had gone through a door and not come back. The door had been in an ordinary building on an ordinary night and when she'd followed him through there had been nothing on the other side. No room. No Morris. Just a smell like wet iron and a single leather glove on the floor. She had learned, that night, that there were doors in this city she did not understand. Herrera slowed. He'd ducked down an iron-railed stairwell behind a shuttered kebab shop — one of those sealed-up Tube entrances the Underground had boarded over decades ago and the council had forgotten about. The boards, she saw as she came up on it, were not boards. They had been, once. Now they were a door, and the door was open a handspan, and warm yellow light was leaking out onto the wet concrete. Quinn stopped at the top of the stairwell. Herrera was at the bottom. He'd turned. He was looking up at her, rain running off the points of his hair, his chest heaving, the medallion out and shining against his jacket. His face — she'd only ever seen it in photographs. In person he looked younger. Tired. Scared in a way that wasn't the scared of a man about to be arrested. "Don't," he said. His accent was Spanish under the London on top. "Detective. Don't come down." "Herrera, hands where I can see them." "You don't know what this is." "I know what you are. Hands." He laughed — one short broken sound, almost a sob. "You don't. You really don't." He held up his closed fist and opened it, and whatever was in his palm was pale and curved and looked, from where she stood, like a piece of bone. "This is the only way I get through that door. Once I'm through, you won't find me. Go home. Please. Go home." He turned and stepped through. The door swung wider behind him, and Quinn saw — for an instant, before it began to close — a staircase spiraling down farther than any Tube station had any right to. Lantern-light. The suggestion of voices. A smell she knew. Wet iron. The smell from the night Morris had vanished. She stood at the top of the stairwell with the rain drumming on her shoulders and her radio squawking in her coat and her hand on the grip of a baton that, she suspected, would be very little use down there. Three years of not understanding. Three years of files she couldn't close and grief she couldn't name. The door was still open. Just. Quinn wiped the rain from her eyes, drew a breath that tasted of the city and something older, and started down the stairs.
created_at"2026-04-24T13:32:56.772Z" 4/24/2026, 1:32:56 PM
model"anthropic/claude-4.7-opus-20260416"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency844
moderation_latency(null)
generation_time28752
tokens_prompt914
tokens_completion1508
native_tokens_prompt1269
native_tokens_completion1954
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)
num_fetches(null)
web_search_engine(null)
origin"https://ncbench.com/"
is_byokfalse
finish_reason"stop"
native_finish_reason"end_turn"
usage0.055195
router(null)
provider_responses
0
endpoint_id"0fa65117-00d9-4835-808d-9b68041d9ce4"
id"a4f9ceaf-9a92-4357-a3a8-58094a05d20d"
is_byokfalse
latency843
model_permaslug"anthropic/claude-4.7-opus-20260416"
provider_name"Amazon Bedrock"
status200
user_agent"langchainjs-openai/1.0.0 ((node/v24.14.1; linux; x64))"
http_referer(null)
request_id"req-1777037576-o1ICKtZEUMtYwzHcwPal"
session_id(null)
api_type"completions"
id"gen-1777037576-KghkYbqe2rZLKaEh5zlU"
upstream_id"a4f9ceaf-9a92-4357-a3a8-58094a05d20d"
total_cost0.055195
cache_discount(null)
upstream_inference_cost0
provider_name"Amazon Bedrock"
response_cache_source_id(null)

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags4
adverbTagCount0
adverbTags(empty)
dialogueSentences10
tagDensity0.4
leniency0.8
rawRatio0
effectiveRatio0
90.34% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1035
totalAiIsmAdverbs2
found
0
adverb"really"
count1
1
adverb"very"
count1
highlights
0"really"
1"very"
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)
100.00% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1035
totalAiIsms0
found(empty)
highlights(empty)
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
narrationSentences79
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount1
narrationSentences79
filterMatches
0"watch"
hedgeMatches
0"began to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences85
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen46
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1050
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions2
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions42
wordCount976
uniqueNames27
maxNameDensity0.61
worstName"Herrera"
maxWindowNameDensity1
worstWindowName"Herrera"
discoveredNames
Soho2
Greek1
Street1
Herrera6
Raven1
Nest1
Thames1
Met1
Dispatch1
Shaftesbury1
Avenue1
Playbill1
Quinn5
Tottenham1
Court1
Road2
Saint1
Christopher1
Charing1
Cross1
Euston1
Camden2
Morris3
Tube2
Underground1
Spanish1
London1
persons
0"Herrera"
1"Raven"
2"Met"
3"Dispatch"
4"Quinn"
5"Saint"
6"Christopher"
7"Cross"
8"Morris"
9"Underground"
places
0"Soho"
1"Greek"
2"Street"
3"Thames"
4"Shaftesbury"
5"Avenue"
6"Tottenham"
7"Court"
8"Road"
9"Charing"
10"Euston"
11"London"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences46
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1050
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences85
matches
0"learned, that night"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs30
mean35
std29.76
cv0.85
sampleLengths
059
146
26
34
479
568
627
715
880
984
1012
1169
126
1390
1416
152
1671
178
1863
1916
207
216
226
2367
245
2551
2641
2717
286
2923
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences79
matches
0"been seen"
0.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount8
totalVerbs162
matches
0"were going"
1"were thinning"
2"was heading"
3"was heading"
4"was threading"
5"was making"
6"was leaking"
7"was looking"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount11
semicolonCount0
flaggedSentences9
totalSentences85
ratio0.106
matches
0"Quinn was already three streets behind when she caught the flash of olive skin under a streetlamp — a man ducking into a doorway on Greek Street, one hand pressed to his ribs, the other clutching something small enough to hide in a fist."
1"Forty-one years old and she still ran like she had at twenty-three, the way the Met had taught her, the way the army had taught her before that — elbows tight, chin down, eyes fixed a half-second ahead of where her feet were going."
2"Fast and favoring his right side — that old knife scar, maybe, or something fresher she hadn't seen yet."
3"She lost him once — a bad second where the rain thickened and the crowds parted around some street performer and her heart climbed up into her throat — and then she caught him again, a pale flash of Saint Christopher catching streetlamp as he turned."
4"She felt the old ache in her left knee — that case three years ago, the case, the one where Morris had gone through a door and not come back."
5"He'd ducked down an iron-railed stairwell behind a shuttered kebab shop — one of those sealed-up Tube entrances the Underground had boarded over decades ago and the council had forgotten about."
6"His face — she'd only ever seen it in photographs."
7"He laughed — one short broken sound, almost a sob."
8"The door swung wider behind him, and Quinn saw — for an instant, before it began to close — a staircase spiraling down farther than any Tube station had any right to."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount971
adjectiveStacks0
stackExamples(empty)
adverbCount29
adverbRatio0.029866117404737384
lyAdverbCount2
lyAdverbRatio0.0020597322348094747
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences85
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences85
mean12.35
std11.39
cv0.922
sampleLengths
015
144
22
35
48
57
624
76
84
924
1044
118
123
133
1419
1538
168
1722
185
197
208
2126
2225
2319
246
254
262
274
2811
2946
308
312
323
338
345
357
3620
374
3819
3910
4016
411
425
4316
4430
4526
462
472
4814
4916
73.73% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats4
diversityRatio0.47058823529411764
totalSentences85
uniqueOpeners40
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences66
matches
0"Of course he ran."
1"Just a smell like wet"
ratio0.03
50.30% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount28
totalSentences66
matches
0"She'd recognize him anywhere now."
1"she shouted, and he ran"
2"She cut her stride long,"
3"Her watch strap chafed at"
4"She barely noticed."
5"He vaulted a bollard without"
6"She pulled her radio up"
7"She squinted through the rain"
8"She let the radio drop"
9"She followed, raising a hand"
10"She didn't look back."
11"He was heading north."
12"She lost him once —"
13"He'd pulled the medallion out"
14"He was heading somewhere specific."
15"He knew these streets, these"
16"He was making for Camden."
17"She felt the old ache"
18"She had learned, that night,"
19"He'd ducked down an iron-railed"
ratio0.424
66.06% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount52
totalSentences66
matches
0"The rain came down in"
1"Quinn was already three streets"
2"She'd recognize him anywhere now."
3"The paramedic who wasn't a"
4"The man who'd been seen"
5"she shouted, and he ran"
6"She cut her stride long,"
7"Her watch strap chafed at"
8"She barely noticed."
9"Herrera was fast."
10"He vaulted a bollard without"
11"She pulled her radio up"
12"She squinted through the rain"
13"Dispatch crackled something she couldn't"
14"She let the radio drop"
15"The alley spat them out"
16"Herrera threaded between a couple"
17"She followed, raising a hand"
18"The driver leaned on the"
19"She didn't look back."
ratio0.788
100.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount2
totalSentences66
matches
0"By the time they'd crossed"
1"Now they were a door,"
ratio0.03
95.24% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences30
technicalSentenceCount2
matches
0"He was looking up at her, rain running off the points of his hair, his chest heaving, the medallion out and shining against his jacket."
1"She stood at the top of the stairwell with the rain drumming on her shoulders and her radio squawking in her coat and her hand on the grip of a baton that, she …"
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags4
uselessAdditionCount0
matches(empty)
50.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags2
fancyCount1
fancyTags
0"she shouted (shout)"
dialogueSentences10
tagDensity0.2
leniency0.4
rawRatio0.5
effectiveRatio0.2
87.5222%