Run: 2026-09-10-01M25DSY528NZZ2YZG2J6GT273

Run Details

Duration:
21.2s
Cost:
$0.001862
Provider:
DeepSeek

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 started at Charing Cross and followed them north. Quinn had picked him up outside the Raven’s Nest at half past ten — Tomás Herrera, black jacket, hood drawn against the weather, walking with the kind of purpose that told her he wasn’t going home. She’d given him a block, then two, keeping the green neon of the Nest’s sign burning in the puddles between them. By Camden he’d made her. He glanced back at the corner of Delancey Street, and something shifted in the set of his shoulders. Then he ran. She ran too. Eighteen years on the job and the body remembered before the mind caught up — heel-toe rhythm, breath measured through the nose, the shortened stride you took on wet cobbles because a bad turn would put you on your back. He cut left into Inverness Street, took out a stack of crates outside a shuttered fruit stall, and didn’t look back. Quinn hurdled the wreckage. A crate lid spun into the gutter and the rain ate it. “Police,” she shouted, mostly for the benefit of the two drinkers stumbling out of a pub doorway. “Move.” They moved. He had a runner’s build and he used it. Short, economical strides. No wasted motion. She marked the scar along his left forearm when his sleeve rode up — the knife scar from the file, the one that matched the statement she’d pulled off a Hackney assault charge four years back. Her lungs burned. Her coat weighed ten pounds more than it had when she’d left the car. He turned again at the canal, ducked under a chain, and vanished between two hoardings. She reached the gap and stopped. Beyond it: nothing but a wall of corrugated steel, a dead-end yard, a skip full of builder’s rubble. She pulled her torch and swept the beam. Water drummed on the metal. A rat went about its business along the wall. Then she saw it. A service hatch, knee-high, rusted at the hinges, standing three inches open. The beam picked out fibres on the rim. Black polyester. Fresh. She looked at the hatch. Then at the yard behind her. Then at her watch — 23:07, the leather strap soft and heavy from the rain. She got on her knees and pulled the hatch wide. The smell hit her first. Wet earth, cold iron, and beneath it something faintly sweet, like incense gone stale. Her torch fell down a shaft of brick and came back off the walls of a stairwell. Steps descended into black. Somewhere far below, water dripped into water, and the sound had a room behind it — a big room. The stairwell breathed. She felt it on her face. Faint air moving up, warmed by whatever lay at the bottom. A dead Tube station. She knew the map — Camden had a dozen of them, sealed in the fifties, bricked up and forgotten, and the Met had a file of complaints about the one under Buck Street going back years. Trespass. Vandalism. The occasional report of a man emerging from a locked door at three in the morning. Quinn rested her hand on the rim of the hatch and listened. Nothing but the rain on the steel above her. She went down. Twelve steps to the first landing. Tiles the colour of old teeth, green and cream, and a station name in enamel letters so worn she couldn’t make it out. Graffiti climbed the walls in layers, each generation of taggers painting over the last. A shopping trolley lay on its side, stripped to the frame. Her torch swept past a poster advertising a bank that had gone bust in 2008. The air got warmer the deeper she went. Not warm. Close. The kind of air you found in a cellar where something kept the damp off. At the bottom of the second flight, the stairs stopped. Not sealed — stopped. A brick wall ran floor to ceiling with a steel door set into it, and the door stood open a hand’s width. Light spilled through the gap. Not torchlight. Lamp light, yellow and steady. Quinn put her shoulder to the door and pushed it wide. She came out onto a platform. The tracks were gone. Someone had floored it over with timber, and on the timber stood a market. Stalls ran the length of the platform in two rows, lit by gas lamps hung from the tiled ceiling. She saw a woman in a waxed coat selling stoppered bottles that glowed under their own light. She saw a man with wings folded neat against his back, silver feathers, negotiating over a crate of something that moved. She saw a table of clocks that all showed different times, and none of them were wrong. Voices carried. Coins rang on wood. Somewhere a woman sang in a language Quinn didn’t know. Nobody looked at her. No — that was the thing. Everybody looked at her. Every head on the platform turned, one after another, down the rows, a wave of faces catching the light, and then every head turned away. Business resumed. Coins rang. She was being given the courtesy of being ignored. That was worse than being noticed. Herrera was fifty feet ahead, walking fast along the far edge of the platform, hood back now. He stopped at a stall where a man in a grey suit sat behind a folding table. They spoke. The man in grey pointed down the tunnel. Herrera nodded and walked on. Quinn put her hand on her radio. The set clicked and gave her static, then nothing. No signal. No way to call it in. She looked at the tunnel mouth. At Herrera’s back, receding. A woman stepped into her path. Short, older, a bone token on a leather cord around her neck — the same token Quinn had seen on every stallholder in the place, hanging against coats and shirts like a dress code. The woman held out her hand. Palm up. Empty. “You’re new,” the woman said. Her voice was cheerful. That was the trouble with it. “Metropolitan Police,” Quinn said. “The man who just passed you is a person of interest in an ongoing investigation. You’re going to let me by.” “Am I.” “That’s the polite version.” The woman looked at Quinn’s empty hand. Then at her face. Then at the platform behind her, where two men in waxed coats had stopped their conversation and turned to watch, and a third had set down a crate and was rolling his shoulders like a man loosening up for a shift. “You can’t come in here without a token,” the woman said. “That’s not me being difficult. That’s the Market. You walk past me, something takes you. Doesn’t matter what you are. Detective. Soldier. It takes you.” “Where do I get one.” The woman smiled and said nothing. Down the tunnel, Herrera’s silhouette thinned against the lamplight and then it was gone, swallowed by the dark where the platform ended. Quinn took her hand off her radio. She looked at the tunnel. She looked at the token on the leather cord, swinging very slightly, though there was no draught on the platform. Rain fell somewhere behind her, far above, on the streets of Camden, and here she stood at the edge of a floor that shouldn’t exist, in a city she’d sworn eighteen years ago to police, with no signal, no backup, no warrant, and no token. She took a step forward anyway.
created_at"2026-09-10T10:28:52.519Z" 9/10/2026, 10:28:52 AM
model"deepseek/deepseek-v4.1-flash-20260910"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency510
moderation_latency(null)
generation_time21176
tokens_prompt1104
tokens_completion3421
native_tokens_prompt1029
native_tokens_completion3066
native_tokens_completion_images(null)
native_tokens_reasoning1470
native_tokens_cached895
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)
usage0.001862385
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.20.0; linux; x64))"
http_referer(null)
request_id"req-1789036132-0YjFJD1hQSOSasg6LxQh"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1789036132-ko1MfoTmZrO7u3YD5SDU"
upstream_id"3cf32710-92f6-4d0c-8786-faba5e59d844"
provider_responses
0
endpoint_id"fd104bc8-535a-4d31-a847-a8e402e86a02"
id"3cf32710-92f6-4d0c-8786-faba5e59d844"
is_byokfalse
latency339
model_permaslug"deepseek/deepseek-v4.1-flash-20260910"
provider_name"DeepSeek"
status200
total_cost0.001862385
cache_discount0.000131565
upstream_inference_cost0
provider_name"DeepSeek"
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
totalTags4
adverbTagCount0
adverbTags(empty)
dialogueSentences10
tagDensity0.4
leniency0.8
rawRatio0
effectiveRatio0
91.97% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1245
totalAiIsmAdverbs2
found
0
adverb"very"
count1
1
adverb"slightly"
count1
highlights
0"very"
1"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)
95.98% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1245
totalAiIsms1
found
0
word"measured"
count1
highlights
0"measured"
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
narrationSentences115
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences115
filterMatches
0"watch "
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences121
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen45
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1254
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions7
unquotedAttributions0
matches(empty)
83.33% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions33
wordCount1183
uniqueNames15
maxNameDensity0.85
worstName"Quinn"
maxWindowNameDensity2.5
worstWindowName"Quinn"
discoveredNames
Charing1
Cross1
Raven1
Nest2
Tomás1
Herrera5
Camden3
Delancey1
Street3
Inverness1
Hackney1
Tube1
Met1
Buck1
Quinn10
persons
0"Nest"
1"Tomás"
2"Herrera"
3"Camden"
4"Street"
5"Met"
6"Buck"
7"Quinn"
places
0"Charing"
1"Raven"
2"Delancey"
3"Inverness"
globalScore1
windowScore0.833
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences70
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1254
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences121
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs48
mean26.13
std19.83
cv0.759
sampleLengths
010
157
226
343
421
534
62
768
815
96
1040
1116
1211
1326
1410
1559
1620
1758
1821
193
2069
2126
2236
2312
2411
256
2618
2774
2816
294
3039
3115
3249
3324
3410
3549
3615
3725
382
394
4052
4136
425
436
4422
4532
4645
476
93.06% Passive voice overuse
Target: ≤2% passive sentences
passiveCount4
totalSentences115
matches
0"were gone"
1"being given"
2"being ignored"
3"being noticed"
4"was gone"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount2
totalVerbs188
matches
0"wasn’t going"
1"was rolling"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount9
semicolonCount0
flaggedSentences9
totalSentences121
ratio0.074
matches
0"Quinn had picked him up outside the Raven’s Nest at half past ten — Tomás Herrera, black jacket, hood drawn against the weather, walking with the kind of purpose that told her he wasn’t going home."
1"Eighteen years on the job and the body remembered before the mind caught up — heel-toe rhythm, breath measured through the nose, the shortened stride you took on wet cobbles because a bad turn would put you on your back."
2"She marked the scar along his left forearm when his sleeve rode up — the knife scar from the file, the one that matched the statement she’d pulled off a Hackney assault charge four years back."
3"Then at her watch — 23:07, the leather strap soft and heavy from the rain."
4"Somewhere far below, water dripped into water, and the sound had a room behind it — a big room."
5"She knew the map — Camden had a dozen of them, sealed in the fifties, bricked up and forgotten, and the Met had a file of complaints about the one under Buck Street going back years."
6"Not sealed — stopped."
7"No — that was the thing."
8"Short, older, a bone token on a leather cord around her neck — the same token Quinn had seen on every stallholder in the place, hanging against coats and shirts like a dress code."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1177
adjectiveStacks0
stackExamples(empty)
adverbCount28
adverbRatio0.0237892948173322
lyAdverbCount3
lyAdverbRatio0.002548853016142736
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences121
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences121
mean10.36
std9.33
cv0.9
sampleLengths
010
136
221
35
418
53
63
740
821
94
1012
1117
121
132
149
153
163
1736
183
1914
2015
216
2218
238
245
259
264
2712
288
292
301
315
326
3315
3410
355
3614
3717
384
3919
403
416
4211
434
4436
451
461
4716
4812
499
64.46% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats7
diversityRatio0.4214876033057851
totalSentences121
uniqueOpeners51
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount8
totalSentences99
matches
0"Then he ran."
1"Then she saw it."
2"Then at the yard behind"
3"Then at her watch —"
4"Somewhere far below, water dripped"
5"Somewhere a woman sang in"
6"Then at her face."
7"Then at the platform behind"
ratio0.081
98.79% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount30
totalSentences99
matches
0"She’d given him a block,"
1"He glanced back at the"
2"She ran too."
3"He cut left into Inverness"
4"she shouted, mostly for the"
5"He had a runner’s build"
6"She marked the scar along"
7"Her lungs burned."
8"Her coat weighed ten pounds"
9"He turned again at the"
10"She reached the gap and"
11"She pulled her torch and"
12"She looked at the hatch."
13"She got on her knees"
14"Her torch fell down a"
15"She felt it on her"
16"She knew the map —"
17"She went down."
18"Her torch swept past a"
19"She came out onto a"
ratio0.303
76.16% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount76
totalSentences99
matches
0"The rain started at Charing"
1"Quinn had picked him up"
2"She’d given him a block,"
3"He glanced back at the"
4"She ran too."
5"He cut left into Inverness"
6"Quinn hurdled the wreckage."
7"A crate lid spun into"
8"she shouted, mostly for the"
9"He had a runner’s build"
10"She marked the scar along"
11"Her lungs burned."
12"Her coat weighed ten pounds"
13"He turned again at the"
14"She reached the gap and"
15"She pulled her torch and"
16"Water drummed on the metal."
17"A rat went about its"
18"A service hatch, knee-high, rusted"
19"The beam picked out fibres"
ratio0.768
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences99
matches(empty)
ratio0
59.80% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences43
technicalSentenceCount5
matches
0"Quinn had picked him up outside the Raven’s Nest at half past ten — Tomás Herrera, black jacket, hood drawn against the weather, walking with the kind of purpos…"
1"She’d given him a block, then two, keeping the green neon of the Nest’s sign burning in the puddles between them."
2"She saw a woman in a waxed coat selling stoppered bottles that glowed under their own light."
3"She saw a man with wings folded neat against his back, silver feathers, negotiating over a crate of something that moved."
4"Rain fell somewhere behind her, far above, on the streets of Camden, and here she stood at the edge of a floor that shouldn’t exist, in a city she’d sworn eight…"
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
totalTags4
fancyCount1
fancyTags
0"she shouted (shout)"
dialogueSentences10
tagDensity0.4
leniency0.8
rawRatio0.25
effectiveRatio0.2
87.1186%