Run: 2026-04-24-01KQ0GBV2KRY0TTM1BY6698DYB

Run Details

Duration:
27.9s
Cost:
$0.057070
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 like it had a grudge. It rattled on the iron grates along Camden High Street, slicked the pavement to a black mirror, and drummed against the shoulders of Detective Harlow Quinn's coat as she ran. Herrera had twenty meters on her. He moved like someone who had been chased before — head down, elbows tight, no wasted glances behind. A runner's economy. She'd clocked the scar on his forearm back at the Raven's Nest, when he'd reached across the bar to palm a folded envelope from a man she had been watching for six weeks. The envelope had gone into his jacket. He had looked up, and for one clean instant his warm brown eyes had found hers in the mirror behind the bottles, and he had known. Then he had bolted, and the green neon of Silas' sign had smeared past her as she followed. That had been eight blocks ago. Quinn kept her breathing metered, three strides in, three strides out, the way the service had taught her when she was twenty-three and still thought she understood the world. Her radio was clipped to the inside of her lapel. She didn't call it in. Not yet. The moment she put Herrera's name over the air, half the Met would want to know why she was two boroughs outside her patch, chasing a man whose file officially didn't exist. He cut left down Kentish Town Road, and she cut with him. A cyclist swerved, shouted. A bus heaved past, sending a cold sheet of gutter water over her knees. She barely felt it. Her eyes were on the back of Herrera's head — short curly hair plastered flat, the gleam of something on a chain around his neck swinging free of his shirt as he ran. A medallion. Saint Christopher. Patron saint of travelers, of people running for their lives. He made a mistake at the crossing by Inverness Street. He glanced back. That was what she needed. The glance told her he was tired, that the envelope mattered enough to keep, that he hadn't ditched it in any doorway yet. She closed three meters in the time it took him to turn his head. "Herrera!" she shouted. "Metropolitan Police! On the ground!" He didn't go down. He ducked between two parked vans and vanished. Quinn swore and followed. The gap between the vans stank of diesel and piss. Beyond it she found a narrow service road, a skip piled high with sodden cardboard, and — there — a wrought-iron railing hanging loose from its post, peeled back like a page. Behind the railing, a flight of concrete steps fell down into the dark. She knew this stretch of Camden the way any copper with eighteen years' service knew their city. There was no Tube station here. There hadn't been one in sixty years. The line had been closed since the war. She drew her torch. She drew her sidearm, a small comfort she was not officially supposed to be carrying. At the top of the steps she paused, just long enough to hear — beneath the hiss of the rain — the slap of his shoes on stone, dropping away and away. Her watch ticked against her wrist: the old leather strap she hadn't replaced since DS Morris had given it to her on her promotion. Morris, who had chased a man down a staircase not unlike this one, three years and four months ago, and never come back up. She went down. The stairs took two turns and ended in a tiled vestibule gone yellow with age. Posters hung from the walls in rags: Ovaltine, war bonds, a girl smiling over a cup of tea from 1943. The air was warm. That was the first thing that wasn't right. You went underground in London, you got the cold of the tunnels, the ghost-draft of trains in other lines pulling the air through. This was warm, and it smelled of woodsmoke, of cardamom, of something metallic she couldn't place. Ahead, an archway. Inside the arch, a curtain of what looked like black beads — except as the torchlight brushed them they shifted and clicked, and she realized they were small bones, polished, strung on wire. Finger bones. Something smaller. Birds, maybe. She did not want to know. On the floor at her feet, a Saint Christopher medallion lay in a puddle of rain that had run down from her coat. The chain was snapped. He'd dropped it. Or left it. From beyond the curtain came the low churn of voices. Dozens of voices. A market's murmur, the soft bargain-noise of people who did not want to be heard. A woman laughed, high and bright. Something — she could not tell what — barked once, and was hushed. Quinn's hand tightened on the grip of her pistol. She crouched, picked up the medallion with two gloved fingers, and turned it in the torchlight. Warm to the touch. Warmer than it should have been after lying in cold water. She thought of Morris. She thought of the report she had written and rewritten and never filed, the one that used words like inconsistent and anomalous because she could not bring herself to write the words she meant. She thought of the way the duty sergeant had looked at her, afterward, and how he had stopped looking at her at all. She thought: this is the kind of door he went through. The sensible thing was to back up the stairs, call it in, cordon the entrance, wait for tactical. The sensible thing was not to push through a bone curtain into a warm underground no one had told her existed. The sensible thing was what had kept her alive for eighteen years. Herrera had the envelope. Herrera had answers. Herrera was, right now, thirty seconds ahead of her and bleeding velocity into a crowd. Quinn pocketed the medallion. She clicked her torch off — whatever light was on the other side of the curtain would be enough, and she did not want to announce herself — and she let her eyes adjust to the thin red glow seeping through the bones. She breathed in, three counts. Out, three counts. Then Detective Harlow Quinn pushed aside the curtain of small bones, and stepped into the Veil Market, and the door of her old, reasonable life closed quietly behind her.
created_at"2026-04-24T19:44:44.12Z" 4/24/2026, 7:44:44 PM
model"anthropic/claude-4.7-opus-20260416"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency2060
moderation_latency(null)
generation_time27872
tokens_prompt914
tokens_completion1574
native_tokens_prompt1269
native_tokens_completion2029
native_tokens_completion_images(null)
native_tokens_reasoning54
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"
usage0.05707
router(null)
provider_responses
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endpoint_id"0fa65117-00d9-4835-808d-9b68041d9ce4"
id"fb921726-d393-4392-81ef-6a2214e1af01"
is_byokfalse
latency1385
model_permaslug"anthropic/claude-4.7-opus-20260416"
provider_name"Amazon Bedrock"
status200
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http_referer(null)
request_id"req-1777059884-QckIgHTr0a4idgAYzEXN"
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api_type"completions"
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total_cost0.05707
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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
totalTags3
adverbTagCount0
adverbTags(empty)
dialogueSentences2
tagDensity1
leniency1
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1061
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)
95.29% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1061
totalAiIsms1
found
0
word"churn"
count1
highlights
0"churn"
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
narrationSentences85
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences85
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences86
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen41
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1072
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
wordCount1052
uniqueNames22
maxNameDensity0.57
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Herrera"
discoveredNames
Camden2
High1
Street2
Detective2
Harlow2
Quinn6
Raven1
Nest1
Herrera6
Met1
Kentish1
Town1
Road1
Christopher2
Inverness1
Tube1
Morris3
Ovaltine1
London1
Saint2
Veil1
Market1
persons
0"Harlow"
1"Quinn"
2"Herrera"
3"Met"
4"Christopher"
5"Morris"
6"Ovaltine"
7"Saint"
places
0"Camden"
1"High"
2"Street"
3"Raven"
4"Kentish"
5"Town"
6"Road"
7"Inverness"
8"London"
9"Veil"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences55
glossingSentenceCount1
matches
0"looked like black beads — except as the t"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1072
matches(empty)
89.15% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount2
totalSentences86
matches
0"tired, that the keep, that he"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs29
mean36.97
std25.46
cv0.689
sampleLengths
039
16
287
318
46
578
681
713
842
98
1012
1159
1238
1351
1448
153
1686
1748
1827
196
2047
2140
2261
2311
2451
2522
2647
278
2829
80.50% Passive voice overuse
Target: ≤2% passive sentences
passiveCount6
totalSentences85
matches
0"been chased"
1"was clipped"
2"was tired"
3"been closed"
4"was snapped"
5"was hushed"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs176
matches(empty)
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount11
semicolonCount0
flaggedSentences7
totalSentences86
ratio0.081
matches
0"He moved like someone who had been chased before — head down, elbows tight, no wasted glances behind."
1"Her eyes were on the back of Herrera's head — short curly hair plastered flat, the gleam of something on a chain around his neck swinging free of his shirt as he ran."
2"Beyond it she found a narrow service road, a skip piled high with sodden cardboard, and — there — a wrought-iron railing hanging loose from its post, peeled back like a page."
3"At the top of the steps she paused, just long enough to hear — beneath the hiss of the rain — the slap of his shoes on stone, dropping away and away."
4"Inside the arch, a curtain of what looked like black beads — except as the torchlight brushed them they shifted and clicked, and she realized they were small bones, polished, strung on wire."
5"Something — she could not tell what — barked once, and was hushed."
6"She clicked her torch off — whatever light was on the other side of the curtain would be enough, and she did not want to announce herself — and she let her eyes adjust to the thin red glow seeping through the bones."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount143
adjectiveStacks0
stackExamples(empty)
adverbCount2
adverbRatio0.013986013986013986
lyAdverbCount0
lyAdverbRatio0
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences86
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences86
mean12.47
std10.12
cv0.812
sampleLengths
09
130
26
318
43
533
67
726
818
96
1029
1110
125
132
1432
1512
164
1714
184
1933
202
212
2210
2310
243
255
2623
2714
283
295
304
318
324
3310
3432
3513
3617
376
387
398
404
4115
4232
4324
4424
453
4615
4720
484
498
49.61% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats14
diversityRatio0.3953488372093023
totalSentences86
uniqueOpeners34
85.47% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences78
matches
0"Then he had bolted, and"
1"Then Detective Harlow Quinn pushed"
ratio0.026
71.28% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount29
totalSentences78
matches
0"It rattled on the iron"
1"He moved like someone who"
2"She'd clocked the scar on"
3"He had looked up, and"
4"Her radio was clipped to"
5"She didn't call it in."
6"He cut left down Kentish"
7"She barely felt it."
8"Her eyes were on the"
9"He made a mistake at"
10"He glanced back."
11"She closed three meters in"
12"He didn't go down."
13"He ducked between two parked"
14"She knew this stretch of"
15"She drew her torch."
16"She drew her sidearm, a"
17"Her watch ticked against her"
18"She went down."
19"You went underground in London,"
ratio0.372
49.74% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount64
totalSentences78
matches
0"The rain came down like"
1"It rattled on the iron"
2"Herrera had twenty meters on"
3"He moved like someone who"
4"A runner's economy."
5"She'd clocked the scar on"
6"The envelope had gone into"
7"He had looked up, and"
8"That had been eight blocks"
9"Quinn kept her breathing metered,"
10"Her radio was clipped to"
11"She didn't call it in."
12"The moment she put Herrera's"
13"He cut left down Kentish"
14"A cyclist swerved, shouted."
15"A bus heaved past, sending"
16"She barely felt it."
17"Her eyes were on the"
18"Patron saint of travelers, of"
19"He made a mistake at"
ratio0.821
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences78
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences41
technicalSentenceCount1
matches
0"On the floor at her feet, a Saint Christopher medallion lay in a puddle of rain that had run down from her coat."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags3
uselessAdditionCount0
matches(empty)
0.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags1
fancyCount1
fancyTags
0"she shouted (shout)"
dialogueSentences2
tagDensity0.5
leniency1
rawRatio1
effectiveRatio1
84.0346%