Run: 2026-09-03-01M1M02G2Q1FSW61A83BF2WE0H

Run Details

Duration:
2m 38s
Cost:
$0.029402
Provider:
Phala

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 green sign over the Raven's Nest threw a hospital pallor onto the wet brick. Quinn stepped from the side street with her coat collar up and her flashlight low. Behind her, the bar’s front door held a line of smoke. The suspect came out that same door three seconds after she did, a narrow man in a waxed jacket, his right hand clamped over a paper bag. “Evening, Detective.” Her beam caught his chin. He turned. The beam slid across a face built for hiding in doorways. The paper bag leaked a brown liquid. Quinn counted one step, then crossed the alley. The man ran. “Turn around.” Her heels struck the pavement. The rain took her words and scattered them. Quinn followed at a pace that kept distance and momentum. She kept her shoulders loose, her watch strap tight against her left wrist. The leather had worn thin at the thumb. She could feel the second hand jump. Soho folded behind them in strings of light. The suspect cut through a restaurant doorway, spun past a bouncer, and took the narrow lane between a laundrette and a closed bookshop. Quinn used the gap between the bouncer and the wall. She came out with her coat sleeve wet and her breathing steady. He knew the city. He took Chinatown at an angle that ignored the main road. Lanterns hung low over the wet street, and each one made a red pool where the water stood. Quinn moved through the pools, boots making no sound. The man ahead of her had the stride of a man who paid for shortcuts. He jumped a chain, dropped a cardboard box of cabbages, and disappeared into the lane behind a tea shop. Quinn stopped at the chain. Her light found the box. Inside, the cabbages held their water. A single green leaf lay open as a map. She crouched. The leaf’s veins matched the lines on the old maps pinned to the Raven’s Nest walls. The thought landed hard. “Wait for me.” No answer came from the lane. Quinn pulled her radio. Static returned. She switched it off and followed. The lane narrowed. Camden rose ahead, its old Tube station half hidden under iron railings and a wall of black ivy. The suspect used a service door at the side of the station. He pushed the door open, held it, and looked back. The rain filled the space between them. “Morris didn’t get out,” Quinn called. The man’s mouth tightened. He stepped aside. Quinn crossed the gap. The service door had no handle. A plate of bone-white metal stood set into the brick. The metal held a single slot, narrow as a keyhole. The suspect placed his paper bag against the plate, then pressed a small white token into the slot. The plate moved inward. Behind it, a stairwell breathed cold air. The air smelled of salt, lamp oil, and something sweet like burnt sugar. Quinn’s light dropped the stairs into shadow. The man descended. “Wait.” The stairwell swallowed him. Quinn stood before the plate. The bone token had vanished into the wall. She pulled from her jacket the token she kept in the same pocket as her warrant card. It had belonged to Morris. The bone was smooth, warm from her body, and the carved ring around its edge had no maker’s mark. A voice rose from the landing below. “Put the light away before it starts arguing.” Tomás Herrera came up the stairs with his hands raised. His olive skin gleamed under Quinn’s beam. A Saint Christopher medallion rested at the hollow of his throat. His left forearm crossed his chest, the old scar pulling tight. Behind him, a girl with a cut lip sat on a step, and Tomás pressed a folded towel to the wound. “You followed him all the way from Soho.” “Where is he?” “He’s selling. That’s what the market does. It takes a thing and makes it useful.” Tomás’s fingers tightened on the towel. “He had a bad night. You made it worse.” Quinn kept her light on his face. “He was with the clique. You treat their wounds. You sell their things.” “I keep people alive.” “Then keep that girl still and answer me.” Tomás glanced at the girl. The girl did not move. The market below made a low sound, like a crowd moving through a room that had no walls. Quinn heard coins, a bell, a woman’s voice calling a price in a language her throat refused to copy. “Where?” “Beneath Camden. An old station. It moves at the full moon. Tonight is the full moon.” He held up the towel. A dark spot spread through the fabric. “The man you want? He trades in memories. He sold a vial this week. Inside, someone’s last hour. You don’t follow a man into that room and come out with the same name.” Quinn’s jaw tightened. The light trembled once. She steadied it. “Morris’s name is in that room.” Tomás’s hands went still. The stairwell dropped below them. Lamps hung from hooks. Stalls opened under cloth that had seen better weather. A row of people moved between the stalls: a woman with a bird’s grey eyes, a boy carrying a jar of black water, a clerk wiping a counter with a rag the colour of blood. The light caught the counter and stopped there. Quinn saw a glass vial. The vial held a curl of smoke that moved against the air. A label read: Last argument, Mrs E. Quinn’s thumb found the bone token in her pocket. Her pulse pushed through the leather of her watch. “He’s at the far counter,” Tomás said. “The one with the white cloth.” “Can I enter?” “You have his token.” “Morris’s token.” Tomás looked at the plate above their heads. The plate’s edge was black with old soot. “The market keeps what it is given. It gives back what it can spare.” Quinn looked down the stairwell. The suspect stood at the white cloth, his back to her. His hand reached for a small box. The crowd parted. A child’s laughter cut across the stalls. The sound was too clean, too close. Tomás took her sleeve. “Wait.” His fingers closed on the wool. Quinn saw the medallion catch the light. It showed a man in a coat, arms wide. The saint faced the market. “Tell me what he’ll sell.” “A warrant won’t stop him. He’ll sell your last hour before you get your badge out.” Quinn’s free hand moved to the door plate. “Then I’ll buy back what he sold.” The bone token left her palm and slid into the slot.
created_at"2026-09-03T16:01:47.614Z" 9/3/2026, 4:01:47 PM
model"qwen/qwen3.8-27b-20260814"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency366
moderation_latency(null)
generation_time157665
tokens_prompt1104
tokens_completion8633
native_tokens_prompt1171
native_tokens_completion9652
native_tokens_completion_images(null)
native_tokens_reasoning8193
native_tokens_cached64
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)
usage0.029402
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.19.0; linux; x64))"
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request_id"req-1788451307-Ydvk5du8P71zs1m4pQbx"
session_id(null)
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api_type"completions"
id"gen-1788451307-uWGt7TELl1X440h7YnDu"
upstream_id"req_bdddcbe200ae91ecbf5b551444623cc1"
provider_responses
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is_byokfalse
latency366
model_permaslug"qwen/qwen3.8-27b-20260814"
provider_name"Phala"
status200
total_cost0.029402
cache_discount0.0000224
upstream_inference_cost0
provider_name"Phala"
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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)
dialogueSentences27
tagDensity0.148
leniency0.296
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1111
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)
86.50% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1111
totalAiIsms3
found
0
word"could feel"
count1
1
word"trembled"
count1
2
word"pulse"
count1
highlights
0"could feel"
1"trembled"
2"pulse"
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
narrationSentences105
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount0
narrationSentences105
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences128
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen35
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1111
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions5
unquotedAttributions0
matches(empty)
40.35% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions40
wordCount912
uniqueNames12
maxNameDensity2.19
worstName"Quinn"
maxWindowNameDensity3
worstWindowName"Tomás"
discoveredNames
Raven2
Nest2
Chinatown1
Tube1
Morris1
Herrera1
Quinn20
Saint1
Christopher1
Tomás8
Last1
Mrs1
persons
0"Raven"
1"Nest"
2"Morris"
3"Herrera"
4"Quinn"
5"Saint"
6"Christopher"
7"Tomás"
8"Mrs"
places
0"Chinatown"
globalScore0.404
windowScore0.667
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences69
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1111
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences128
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs57
mean19.49
std20.79
cv1.067
sampleLengths
068
12
233
33
42
551
653
74
872
95
1042
113
1218
1343
147
156
167
1748
184
1930
201
214
2254
237
248
2560
268
273
2830
297
3013
314
328
3347
341
3561
3610
376
384
3961
405
4137
4213
433
444
452
4616
4714
4840
494
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences105
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs145
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences128
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount913
adjectiveStacks0
stackExamples(empty)
adverbCount7
adverbRatio0.007667031763417305
lyAdverbCount0
lyAdverbRatio0
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences128
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences128
mean8.68
std6.09
cv0.702
sampleLengths
015
115
211
327
42
55
62
711
87
98
103
112
125
138
1410
1513
168
177
188
1923
2010
2112
224
2311
2418
259
2615
2719
285
295
306
319
322
3316
344
353
366
374
382
396
403
4118
4212
4310
447
456
464
473
484
496
43.75% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats8
diversityRatio0.234375
totalSentences128
uniqueOpeners30
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences100
matches(empty)
ratio0
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount22
totalSentences100
matches
0"Her beam caught his chin."
1"Her heels struck the pavement."
2"She kept her shoulders loose,"
3"She could feel the second"
4"She came out with her"
5"He knew the city."
6"He took Chinatown at an"
7"He jumped a chain, dropped"
8"Her light found the box."
9"She switched it off and"
10"He pushed the door open,"
11"He stepped aside."
12"She pulled from her jacket"
13"It had belonged to Morris."
14"His olive skin gleamed under"
15"His left forearm crossed his"
16"He held up the towel"
17"She steadied it."
18"Her pulse pushed through the"
19"His hand reached for a"
ratio0.22
0.00% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount95
totalSentences100
matches
0"The green sign over the"
1"Quinn stepped from the side"
2"The suspect came out that"
3"Her beam caught his chin."
4"The beam slid across a"
5"The paper bag leaked a"
6"Quinn counted one step, then"
7"The man ran."
8"Her heels struck the pavement."
9"The rain took her words"
10"Quinn followed at a pace"
11"She kept her shoulders loose,"
12"The leather had worn thin"
13"She could feel the second"
14"Soho folded behind them in"
15"The suspect cut through a"
16"Quinn used the gap between"
17"She came out with her"
18"He knew the city."
19"He took Chinatown at an"
ratio0.95
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences100
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences34
technicalSentenceCount0
matches(empty)
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags4
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags2
fancyCount0
fancyTags(empty)
dialogueSentences27
tagDensity0.074
leniency0.148
rawRatio0
effectiveRatio0
85.6867%