Run: 2026-02-24-01KJ8TM2K0K28J13PJRYT07NCC

Run Details

Duration:
43.0s
Cost:
$0.032244
Provider:
Amazon Bedrock

Model input

System
You are an expert fiction writer. Always keep the following rules in mind: - Write in past tense and use General English spelling, grammar, and colloquialisms/slang. - Write in active voice - Always follow the "show, don't tell" principle. - Avoid adverbs and cliches and overused/commonly used phrases. Aim for fresh and original descriptions. - Convey events and story through dialogue. - Mix short, punchy sentences with long, descriptive ones. Drop fill words to add variety. - Skip "he/she said said" dialogue tags and convey people's actions or face expressions through their speech - Avoid mushy dialog and descriptions, have dialogue always continue the action, never stall or add unnecessary fluff. Vary the descriptions to not repeat yourself. - Put dialogue on its own paragraph to separate scene and action. - Reduce indicators of uncertainty like "trying" or "maybe" When writing text: - NEVER conclude the scene on your own, follow the beat instructions very closely. - NEVER end with foreshadowing. - NEVER write further than what I prompt you with. - AVOID imagining possible endings, NEVER deviate from the instructions. - STOP EARLY if the continuation contains what was required in the instructions. You do not need to fill out the full amount of words possible.
User/Human
Take into account the following glossary of characters/locations/items/lore... when writing your response: <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> Continue the story using the following instructions: <instructions> 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. </instructions>

Model response

AI
The rain came sideways off the Thames, the kind that found the gap between collar and neck and stayed there. Quinn moved through it without flinching, her eyes fixed on the figure forty metres ahead, weaving between the last of the Camden market stragglers and the shutter-down shopfronts. Tomás Herrera. Twenty-nine years old. Former NHS paramedic. Three prior interviews, zero charges, and a talent for being adjacent to things Quinn couldn't yet prove. She'd been watching him for six days, ever since his name surfaced in the margins of a missing persons case — two victims, no bodies, and a witness who described a man matching Herrera's description loading what she called "medical equipment" into a Transit van outside an address in Kentish Town. The witness had recanted within forty-eight hours. They always did. Tonight Quinn had followed him from a bar in Soho — the Raven's Nest, green neon cutting through the fog like a wound, the kind of place that made the back of her neck prickle in a way she'd learned not to dismiss — and now he was moving with purpose, no longer the casual amble of a man who thought himself unobserved. He knew she was there. She adjusted her pace, let a couple with a shared umbrella drift between them as living cover. The rain drummed against every hard surface, a white noise that swallowed footsteps and turned the street into something close to underwater. Herrera turned left at the canal bridge, moving away from the lit main road, and Quinn followed at the corner, pressing herself against the wet brickwork for a half-second before rounding it. He was running. She ran. Her boots hit the pavement hard, water spraying up her shins. He was fast — longer stride than she'd anticipated — but she'd run suspects across worse terrain than this and she kept the gap at thirty metres, threading between a pair of cyclists who shouted something she ignored entirely. He cut through an alley, knocked a recycling bin sideways behind him. She hurdled it without breaking stride, the worn leather of her watch strap snagging briefly on the rim. "Herrera." Her voice carried over the rain, not a shout, something colder. "Metropolitan Police. Stop." He didn't stop. The alley spat them both out onto a residential street, lamp-lit and empty, and he veered right without hesitating — not the move of a man navigating blind, but someone following a route he'd memorised. Quinn marked that. He wasn't running away from her. He was running somewhere specific. She keyed her radio with her left hand, barely breaking rhythm. "DS Quinn, in foot pursuit of suspect Herrera, Tomás, heading north from Camden Lock vicinity, request—" Static answered. Not interference. Complete silence. She checked the display: full signal, then nothing, then full signal again in the span of two seconds. She'd experienced that before, once, three years ago, the night Morris had gone into a building she hadn't followed him into, and she pushed the memory flat the way you pushed a loose floorboard down so nobody heard it. Herrera reached a chain-link fence, found a gap already bent back at the base, and pulled himself through in one practised motion. Quinn was through before the fence stopped rattling, scraping her shoulder on the way, not caring. On the other side: a service road, unlit, running alongside what looked like a derelict entrance to an old Tube station. The signage had been stripped, the tiling around the arch was cracked and black with decades of damp, and the wooden boarding across the entrance had been recently disturbed — pale raw wood showing at the edges where screws had been removed and reseated without care. Herrera was at the boards, a hand fitting into a gap she hadn't seen from here. "Don't." Quinn had her warrant card in one hand and her baton half-extended in the other. "Step away from the entrance. Hands where I can see them." He turned. For the first time since the chase began, he fully faced her, and something in his expression wasn't fear. The Saint Christopher medallion at his throat caught a guttering light from somewhere, briefly gold against his rain-darkened shirt. "You don't want to follow me in there." His accent was still strong even after years in London, and his voice carried no hostility. It was a practical statement, the same flat tone a paramedic used when assessing rapid blood loss. "I mean that genuinely, Detective." "My job's not wanting." She moved closer. Ten metres. Eight. "Step away from the boards." "There are people inside who will not react well to police presence. I'm not threatening you. I'm telling you what happens in that situation." "People." She kept moving. "What people?" He glanced at the gap in the boards and back to her. The rain had plastered his dark curls flat against his forehead, and along his forearm — the left one, where the sleeve had ridden up during the run — the scar she'd noted in his file sat like a white seam against his olive skin. "People who are sick. People who need help that the NHS isn't built to provide. People who aren't—" He stopped. "Finish that." The radio crackled, went dead again, in exactly the same rhythm as before. "I can't explain it to you out here." He put a hand flat against the boards, not moving yet, not exactly. "If you call for backup before you've seen it, you'll bring people into something they'll misread, and it will go badly for everyone. If you come in alone, I can get you through it without incident, but you'll have to trust me. Or—" "Or?" "You let me go tonight, and I walk back out of there in two hours, and I sit down with you and I tell you everything you can actually use." Quinn stopped at five metres. The rain hit her shoulders and ran down the back of her hands, cold as tap water. Behind the boards, from somewhere deeper than the entrance arch, came something she couldn't immediately categorise — not sound exactly, more like pressure. A low, collective presence, the subterranean suggestion of a crowd. Her teeth were not quite together. She looked at Tomás Herrera, at the gap in the boards, at the dark beyond.
created_at"2026-02-24T21:59:26.056Z" 2/24/2026, 9:59:26 PM
model"anthropic/claude-4.6-sonnet-20260217"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency646
moderation_latency190
generation_time42769
tokens_prompt1140
tokens_completion1886
native_tokens_prompt1143
native_tokens_completion1921
native_tokens_completion_images(null)
native_tokens_reasoning297
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"
usage0.032244
router(null)
provider_responses
0
endpoint_id"83d802cb-44f2-45a0-a9d5-cb9bf79777c8"
id"e9970ed2-0ff9-49f2-8bff-067f27d2c09e"
is_byokfalse
latency645
model_permaslug"anthropic/claude-4.6-sonnet-20260217"
provider_name"Amazon Bedrock"
status200
api_type"completions"
id"gen-1771970366-ht3C4hpOcDGmNSAc77FI"
upstream_id"e9970ed2-0ff9-49f2-8bff-067f27d2c09e"
total_cost0.032244
cache_discount(null)
upstream_inference_cost0
provider_name"Amazon Bedrock"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags8
adverbTagCount0
adverbTags(empty)
dialogueSentences19
tagDensity0.421
leniency0.842
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1047
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)
85.67% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1047
totalAiIsms3
found
0
word"footsteps"
count1
1
word"navigating"
count1
2
word"silence"
count1
highlights
0"footsteps"
1"navigating"
2"silence"
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
narrationSentences58
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences58
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences69
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen61
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1057
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions9
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions30
wordCount872
uniqueNames17
maxNameDensity0.92
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Herrera"
discoveredNames
Thames1
Camden1
Herrera6
Quinn8
Transit1
Kentish1
Town1
Soho1
Raven1
Nest1
Morris1
Tube1
Saint1
Christopher1
London1
Ten1
Tomás2
persons
0"Herrera"
1"Quinn"
2"Raven"
3"Morris"
4"Saint"
5"Christopher"
6"Tomás"
places
0"Thames"
1"Transit"
2"Kentish"
3"Town"
4"Soho"
5"London"
globalScore1
windowScore1
18.42% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences38
glossingSentenceCount2
matches
0"something close to underwater"
1"looked like a derelict entrance to an old"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches1
per1kWords0.946
wordCount1057
matches
0"not the move of a man navigating blind, but someone following a route he'd memorised"
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences69
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs31
mean34.1
std28.2
cv0.827
sampleLengths
048
125
261
363
45
571
63
72
880
915
103
1149
1211
1316
1463
15105
1616
1727
1840
1946
2015
2124
226
2377
242
2513
2664
271
2830
2961
3015
99.21% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences58
matches
0"been stripped"
41.27% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount3
totalVerbs126
matches
0"was running"
1"wasn't running"
2"was running"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount10
semicolonCount0
flaggedSentences7
totalSentences69
ratio0.101
matches
0"She'd been watching him for six days, ever since his name surfaced in the margins of a missing persons case — two victims, no bodies, and a witness who described a man matching Herrera's description loading what she called \"medical equipment\" into a Transit van outside an address in Kentish Town."
1"Tonight Quinn had followed him from a bar in Soho — the Raven's Nest, green neon cutting through the fog like a wound, the kind of place that made the back of her neck prickle in a way she'd learned not to dismiss — and now he was moving with purpose, no longer the casual amble of a man who thought himself unobserved."
2"He was fast — longer stride than she'd anticipated — but she'd run suspects across worse terrain than this and she kept the gap at thirty metres, threading between a pair of cyclists who shouted something she ignored entirely."
3"The alley spat them both out onto a residential street, lamp-lit and empty, and he veered right without hesitating — not the move of a man navigating blind, but someone following a route he'd memorised."
4"The signage had been stripped, the tiling around the arch was cracked and black with decades of damp, and the wooden boarding across the entrance had been recently disturbed — pale raw wood showing at the edges where screws had been removed and reseated without care."
5"The rain had plastered his dark curls flat against his forehead, and along his forearm — the left one, where the sleeve had ridden up during the run — the scar she'd noted in his file sat like a white seam against his olive skin."
6"Behind the boards, from somewhere deeper than the entrance arch, came something she couldn't immediately categorise — not sound exactly, more like pressure."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount870
adjectiveStacks0
stackExamples(empty)
adverbCount34
adverbRatio0.03908045977011494
lyAdverbCount10
lyAdverbRatio0.011494252873563218
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences69
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences69
mean15.32
std13.69
cv0.893
sampleLengths
020
128
22
33
43
517
651
77
83
963
105
1117
1222
1332
143
152
1611
1739
1812
1918
2012
213
223
2335
243
256
265
2711
2816
292
302
312
3218
3339
3422
3516
3621
3746
3816
3916
4011
412
4219
4319
4424
4517
465
477
482
491
80.19% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats3
diversityRatio0.5072463768115942
totalSentences69
uniqueOpeners35
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences49
matches(empty)
ratio0
24.08% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount24
totalSentences49
matches
0"She'd been watching him for"
1"They always did."
2"He knew she was there."
3"She adjusted her pace, let"
4"He was running."
5"Her boots hit the pavement"
6"He was fast — longer"
7"He cut through an alley,"
8"She hurdled it without breaking"
9"Her voice carried over the"
10"He didn't stop."
11"He wasn't running away from"
12"He was running somewhere specific."
13"She keyed her radio with"
14"She checked the display: full"
15"She'd experienced that before, once,"
16"His accent was still strong"
17"It was a practical statement,"
18"She moved closer"
19"She kept moving"
ratio0.49
21.22% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount43
totalSentences49
matches
0"The rain came sideways off"
1"Quinn moved through it without"
2"She'd been watching him for"
3"The witness had recanted within"
4"They always did."
5"Tonight Quinn had followed him"
6"He knew she was there."
7"She adjusted her pace, let"
8"The rain drummed against every"
9"Herrera turned left at the"
10"He was running."
11"Her boots hit the pavement"
12"He was fast — longer"
13"He cut through an alley,"
14"She hurdled it without breaking"
15"Her voice carried over the"
16"He didn't stop."
17"The alley spat them both"
18"Quinn marked that."
19"He wasn't running away from"
ratio0.878
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences49
matches(empty)
ratio0
71.43% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences30
technicalSentenceCount3
matches
0"Tonight Quinn had followed him from a bar in Soho — the Raven's Nest, green neon cutting through the fog like a wound, the kind of place that made the back of h…"
1"The rain drummed against every hard surface, a white noise that swallowed footsteps and turned the street into something close to underwater."
2"He was fast — longer stride than she'd anticipated — but she'd run suspects across worse terrain than this and she kept the gap at thirty metres, threading betw…"
62.50% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags8
uselessAdditionCount1
matches
0"He put, not moving yet, not exactly"
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags0
76.8002%