Run: 2026-04-27-01KQ743KS1CQR9JS06KH1J8FJC

Run Details

Duration:
51.4s
Cost:
$0.006063
Provider:
AtlasCloud

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
Rain hammered the cobblestones like shrapnel. Harlow Quinn sprinted down the slick alley, boots skidding on oil-slicked pavement. Her breath tore through her throat, each inhalation biting cold air. The closely cropped salt-and-pepper hair plastered against her scalp, water streaming down her sharp jawline. Eighteen years on the force taught her to read a chase. She read the suspect’s rhythm now: fast turns, heavy boots, zero regard for traffic. A black coat flapped behind him, a shadow slipping through the Soho arteries. Stop. The command cracked through the downpour. The figure did not break stride. He vaulted a rusted dumpster, metal groaning under his weight. Harlow followed, gloves scraping peeling paint off the steel side. She checked her wrist. The worn leather strap creaked against her pulse. Three years since Morris vanished into a blind spot, and this thread felt just as fragile. She closed the gap. The suspect cut left toward a rusted iron gate wedged between boarded-up shops. Camden’s old railway tunnels lay just beyond the corner. He pried the gate open with a folded crowbar, disappeared into the dark. Harlow rounded the corner, chest heaving. Water pooled in the stairwell, reflecting flickering amber light from somewhere below. She descended. Concrete steps swallowed the city noise. The air grew thick, smelling of damp stone, ozone, and something metallic. She reached a landing. A heavy steel door stood ajar, hinges screaming in the draft. Inside, a cavernous space opened up. Abandoned platforms, cracked tiles, vendors hawking wares under flickering fluorescents and gas lamps. People moved through the shadows, faces obscured, transactions silent. Bone tokens clinked on wooden counters. The Veil Market. The name surfaced from old case files, dismissed as urban myth. Harlow stepped over the threshold. Her boots echoed on wet concrete. You are not invited. The voice rasped from the gloom. Harlow froze. A figure emerged from behind a stacked crate. Olive skin glistening, short dark curls plastered to his forehead. A silver Saint Christopher medallion caught the light. Tomás Herrera. Former paramedic. His left forearm bore a jagged scar. He held out a hand, palm up. A carved bone token rested on his skin. Turn around, Detective. The upper streets are safe. Down here, the air eats witnesses. Harlow did not lower her sidearm. I am looking for a man. Black coat. Silver cufflink. Herrera’s eyes narrowed. Warm brown irises reflected the flickering lamps. He pays in secrets. You pay in badges. We do not mix. He stepped sideways, blocking her path toward the deeper tunnels. Vendors continued their haggling. A woman sold glass vials filled with swirling green liquid. A man sharpened a blade that hummed against a whetstone. The noise wrapped around her, pressing in. Harlow weighed her options. Protocol demanded she call it in, secure the perimeter, wait for backup. Backup never arrived fast enough when the rules bent. Morris knew that. He chased a lead into a dead zone and never walked out. Harlow’s thumb brushed the trigger guard. The rain still fell above, muffled now, separated by concrete and iron. Down here, the shadows breathed. The suspect waited. The market stretched into darkness. She took a step forward. Herrera tensed. A vendor dropped a glass vial. It shattered, hissing on the tiles. Harlow stood at the edge of the platform. Water dripped from her coat. The case file on her desk had been redacted, stamped with her supervisor’s signature. Three years of dead ends. One ghost in a black coat. She could retreat. File a report. Watch the trail go cold. Or she could cross the line, leave the known world behind, and step into the dark. Her breath slowed. Her fingers tightened on the pistol grip. The decision hung in the damp air.
created_at"2026-04-27T09:25:12.616Z" 4/27/2026, 9:25:12 AM
model"qwen/qwen3.6-35b-a3b-20260415"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency845
moderation_latency(null)
generation_time51320
tokens_prompt1140
tokens_completion6856
native_tokens_prompt1065
native_tokens_completion6103
native_tokens_completion_images(null)
native_tokens_reasoning5260
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"
usage0.006062598
router(null)
provider_responses
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endpoint_id"179c6985-3a1c-48f1-a009-9f3e3e402cdd"
id"ac78722905b74e39af522e4485d32c59"
is_byokfalse
latency845
model_permaslug"qwen/qwen3.6-35b-a3b-20260415"
provider_name"AtlasCloud"
status200
user_agent"langchainjs-openai/1.0.0 ((node/v24.14.1; linux; x64))"
http_referer(null)
request_id"req-1777281912-gJ0TX9xHJtMMRQO1wApy"
session_id(null)
api_type"completions"
id"gen-1777281912-DzctU8pyfS1RzkbjxlM7"
upstream_id"ac78722905b74e39af522e4485d32c59"
total_cost0.006062598
cache_discount(null)
upstream_inference_cost0
provider_name"AtlasCloud"
response_cache_source_id(null)

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount623
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)
43.82% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount623
totalAiIsms7
found
0
word"streaming"
count1
1
word"weight"
count1
2
word"pulse"
count1
3
word"echoed"
count1
4
word"gloom"
count1
5
word"glistening"
count1
6
word"shattered"
count1
highlights
0"streaming"
1"weight"
2"pulse"
3"echoed"
4"gloom"
5"glistening"
6"shattered"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches1
maxInWindow1
found
0
label"eyes widened/narrowed"
count1
highlights
0"eyes narrowed"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences88
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences88
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences88
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen17
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords623
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions0
unquotedAttributions0
matches(empty)
77.77% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions21
wordCount623
uniqueNames10
maxNameDensity1.44
worstName"Harlow"
maxWindowNameDensity2
worstWindowName"Harlow"
discoveredNames
Quinn1
Soho1
Morris2
Veil1
Market1
Saint1
Christopher1
Herrera3
Detective1
Harlow9
persons
0"Quinn"
1"Morris"
2"Market"
3"Saint"
4"Christopher"
5"Herrera"
6"Harlow"
places
0"Soho"
globalScore0.778
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences55
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount623
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences88
matches(empty)
23.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs7
mean89
std20.51
cv0.231
sampleLengths
082
1138
274
378
479
590
682
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences88
matches
0"been redacted"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs124
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences88
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount627
adjectiveStacks0
stackExamples(empty)
adverbCount9
adverbRatio0.014354066985645933
lyAdverbCount1
lyAdverbRatio0.001594896331738437
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences88
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences88
mean7.08
std3.72
cv0.526
sampleLengths
06
112
211
315
411
514
613
71
86
96
1010
1110
124
138
1416
154
1613
179
1813
196
2012
212
226
2312
244
2511
266
2713
289
296
303
3111
325
336
344
356
362
378
3810
398
402
412
427
437
448
453
465
476
486
496
68.18% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats5
diversityRatio0.4431818181818182
totalSentences88
uniqueOpeners39
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences80
matches(empty)
ratio0
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount22
totalSentences80
matches
0"Her breath tore through her"
1"She read the suspect’s rhythm"
2"He vaulted a rusted dumpster,"
3"She checked her wrist."
4"She closed the gap."
5"He pried the gate open"
6"She reached a landing."
7"Her boots echoed on wet"
8"You are not invited."
9"His left forearm bore a"
10"He held out a hand,"
11"I am looking for a"
12"He pays in secrets."
13"You pay in badges."
14"We do not mix."
15"He stepped sideways, blocking her"
16"He chased a lead into"
17"She took a step forward."
18"It shattered, hissing on the"
19"She could retreat."
ratio0.275
53.75% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount65
totalSentences80
matches
0"Harlow Quinn sprinted down the"
1"Her breath tore through her"
2"The closely cropped salt-and-pepper hair"
3"She read the suspect’s rhythm"
4"A black coat flapped behind"
5"The command cracked through the"
6"The figure did not break"
7"He vaulted a rusted dumpster,"
8"Harlow followed, gloves scraping peeling"
9"She checked her wrist."
10"The worn leather strap creaked"
11"She closed the gap."
12"The suspect cut left toward"
13"Camden’s old railway tunnels lay"
14"He pried the gate open"
15"Harlow rounded the corner, chest"
16"Water pooled in the stairwell,"
17"The air grew thick, smelling"
18"She reached a landing."
19"A heavy steel door stood"
ratio0.813
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences80
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences25
technicalSentenceCount0
matches(empty)
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags0
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags0
85.5507%