Run: 2026-02-25-01KJBGRKDNN8DYV87VDVX6PZ9G

Run Details

Duration:
1m 4s
Cost:
$0.012947
Provider:
Alibaba

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="4412" name="Aurora Carter" aliases="Rory, Laila, Aurora, Carter, Malphora" pronouns="she/her" species="Human" storyRole="Protagonist"> <eyeColor>bright blue</eyeColor> <hair>straight shoulder-length black</hair> <height>5'6"</height> <age>25</age> <scar>small crescent-shaped scar on her left wrist from a childhood accident</scar> <description>Aurora, referred to by her friends as Rory, is cool-headed, intelligent, and known for quick out-of-the-box thinking.</description> <backstory>Born in Cardiff to an Irish barrister father (Brendan Carter) and Welsh teacher mother (Jennifer Carter née Ellis), Rory reluctantly pursued Pre-Law at Cardiff University. She fled to London at the behest of her childhood friend Eva after leaving an abusive ex named Evan. By day, Rory works part-time as a delivery person for Yu-Fei Cheung's Golden Empress restaurant. She lives in a flat above Silas' bar.</backstory> </character> <character id="4413" name="Silas Blackwood" aliases="Silas, Si, the Spymaster" pronouns="he/him" species="Human" storyRole="Mentor"> <eyeColor>hazel</eyeColor> <hair>grey-streaked auburn</hair> <height>6'1"</height> <age>58</age> <beard>neatly trimmed, matching his grey-streaked auburn hair</beard> <limp>slight limp in his left leg from an old knee injury</limp> <signetRing>silver signet ring, always worn on his right hand</signetRing> <description>Silas is a retired intelligence operative turned bar owner. He carries himself with quiet authority.</description> <backstory>Former MI6 field agent who retired after a botched operation in Prague left him with a permanent knee injury. He opened "The Raven's Nest" bar in Soho as a front for his network of contacts.</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> </codex> Continue the story using the following instructions: <instructions> Write a fully fleshed out scene of at least 1000 words in the literary fiction genre. Two old friends who haven't seen each other in years run into each other unexpectedly at a bar. One of them has changed significantly. Through their conversation and the tension between who they were and who they've become, reveal the weight of time and unspoken regret. 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 neon sign buzzed above the entrance, casting a sickly pallor over the wet pavement of Soho. Aurora pushed through the heavy oak door, the bell chiming a dull note that barely cut through the low hum of conversation inside. She pulled her collar up against the damp chill clinging to her coat. The air inside smelled of stale tobacco and polished wood, a scent that had settled into the grain of The Raven’s Nest over decades. Maps covered the walls, yellowed at the edges, depicting territories that no longer existed. Black-and-white photographs showed men with stern faces and women in hats, watching her from the shadows. Silas stood behind the bar, polishing a glass with a rag that looked older than the building. His grey-streaked auburn hair caught the dim light, and the silver signet ring on his right hand flashed as he moved. He didn’t look up. He knew the rhythm of her footsteps. The slight limp in his left leg kept time with the clink of the glass against the counter. She walked to the end of the bar, sliding onto a stool. The leather creaked under her weight. “Usual?” Silas asked. His voice was gravel, worn smooth by years of smoke and silence. “Whisky. Neat.” Silas poured. The amber liquid hit the ice with a sharp crack. He set the glass down without looking at her wrist, though the small crescent scar peeked out from her sleeve. He knew the story behind that mark. He knew the stories behind most of the people who walked through his door. Aurora wrapped her hands around the glass. The heat seeped into her palms. She scanned the room. The crowd was sparse, mostly regulars nursing drinks in the corners. Then the door opened again. A gust of wind swept in, carrying the smell of rain and exhaust. A man stepped inside. He wore a tailored coat that cost more than her monthly rent. His hair was cut short, military precise, but his eyes held a frantic energy. He scanned the room, his gaze locking onto her. Cian. He hadn’t changed in the way people usually did. He hadn’t gained weight or lost hair. He had sharpened. The softness of the boy she knew in Cardiff had been carved away, leaving something hard and angular. He walked toward the bar, his stride confident, too confident. He stopped two stools down from her. He didn’t look at her. He looked at Silas. “Blackwood,” Cian said. Silas nodded once. “Mr. Vance.” Cian turned his head. His eyes met Aurora’s. They were the same colour as they had been twenty years ago, a pale grey that looked washed out in the dim light. But the warmth was gone. “Rory,” he said. She took a sip of the whisky. The burn travelled down her throat, sharp and immediate. “Cian.” He sat down. He didn’t ask for a drink. He placed his hands on the bar, fingers spread wide. The skin looked rough, calloused. “Didn’t think you’d come back to London.” “Didn’t think you’d be in Soho.” Cian laughed. It was a dry sound, devoid of humour. “Business.” “Business.” He nodded. He reached into his pocket and pulled out a pack of cigarettes. He tapped one out, holding it between his lips but didn’t light it. He knew the rules of the bar. “You look good,” he said. She looked at his hands. There was a fresh scar on his knuckle, white and thin. “You look expensive.” He smiled, but it didn’t reach his eyes. “I am.” Silas moved to the other end of the bar, wiping down the surface. He gave them space. The silence stretched between them, heavy and thick. Aurora watched the dust motes dancing in the light of the neon sign. “Evan?” Cian asked. She tightened her grip on the glass. The condensation slicked her fingers. “He’s in the past.” “Good.” “Why did you come here, Cian?” He finally looked at her. Really looked at her. He traced the line of her jaw with his eyes, noting the tension there. “I heard you were working for the Golden Empress.” “Delivery.” “Delivery.” He repeated the word like it was foreign. “You were going to be a barrister.” “I was going to be a lot of things.” He leaned in. The scent of expensive cologne mixed with the smell of rain. “You were the smartest one in the class. You were going to tear them apart in court.” “I didn’t make it.” “Why?” She set the glass down. The sound was loud in the quiet room. “Life happens.” “Life happens,” Cian said. He tapped the cigarette against the bar. “I heard about the accident. The wrist.” She pulled her sleeve down. “It’s just a scar.” “It’s a reminder.” She turned to face him. Her blue eyes locked onto his. “What do you want, Cian?” He lit the cigarette. The flame flickered in the gloom. He took a drag, the ember glowing bright orange. “I need a favour.” She laughed. It was a short, sharp sound. “You don’t know me.” “I knew you in Cardiff. I knew you when we were running through the rain in the park. I knew you when you studied until your eyes bled.” “I’m not that girl.” “No.” He exhaled a stream of smoke. “You’re not.” He leaned closer. The heat from the cigarette brushed against her face. “I need someone to move a package. Tonight.” She stood up. The stool scraped against the floor. “I don’t do packages.” “It’s not what you think.” She walked around the bar. She stood in front of him. She was shorter than him, but she didn’t back down. She looked down at the silver ring on his hand. “I live above this place,” she said. “Silas knows me. He knows what I carry.” “Silas is retired.” “Silas is a spymaster.” Cian’s eyes narrowed. He took another drag. “I know who he is. I know who you are. You’re the girl who ran.” “I’m the girl who survived.” He crushed the cigarette into the ashtray. “I can pay you.” She looked at the money in his pocket. She could see the outline of the bills. “I don’t need money.” “Everyone needs something.” She turned away. She walked back to her stool. She picked up her glass. The whisky was warm now. She drank it in one swallow. “Tell me where you are going,” she said. “Why?” “Because I don’t want to see you on the news.” Cian stood up. He buttoned his coat. He looked at the maps on the wall. “I’m going to Prague.” Aurora froze. The name hung in the air, heavy with history. Silas stopped polishing. He turned his head, his hazel eyes fixed on the back of Cian’s head. “Prague,” she said. “Business.” “Silas went to Prague.” Cian turned to look at Silas. The bar owner didn’t move. He just watched. “Ten years ago,” Cian said. “Ten years ago,” Aurora repeated. Cian pulled a key from his pocket. He placed it on the bar. It was silver, cold and heavy. “Take it,” he said. She looked at the key. She didn’t touch it. “Why?” “Because I might not come back.” Silas stepped forward. His limp was more pronounced now. He placed a hand on the bar. “You know the rules of this house, Vance.” Cian looked at Silas. “I know the rules.” “Then leave.” Cian nodded. He turned to Aurora. “Think about it.” He walked to the door. The bell chimed again as he pushed it open. The cold air rushed in, extinguishing the smoke from his cigarette. He stepped out into the rain. Aurora looked at the key. It sat on the wood, gleaming in the dim light. She looked at Silas. He was watching her. “Don’t,” he said. She reached out. Her fingers hovered over the metal. The scar on her wrist throbbed. “Just tell me where it goes,” she said. Silas picked up the key. He slid it into his pocket. “Some things stay buried,” he said. Aurora looked at the door. The rain lashed against the glass. The neon sign buzzed louder. She turned back to the bar. “I’m not her,” she said. Silas poured another drink. He didn’t offer it to her. He drank it himself. “No,” he said. “You’re not.” She sat down. She looked at the empty space where Cian had been. The stool was still warm. “Where does it go?” she asked. Silas looked at the maps on the wall. He traced a line with his finger. He didn’t answer. The door opened again. The bell chimed.
created_at"2026-02-25T23:04:51.9Z" 2/25/2026, 11:04:51 PM
model"qwen/qwen3.5-27b-20260224"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency894
moderation_latency(null)
generation_time63459
tokens_prompt1094
tokens_completion5442
native_tokens_prompt1061
native_tokens_completion5262
native_tokens_completion_images(null)
native_tokens_reasoning3244
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"
usage0.0129471
router(null)
provider_responses
0
endpoint_id"f3f88554-0c22-4d57-973b-0dec948808e8"
id"chatcmpl-d923fc7e-075c-9b3d-9f26-0017cbc7657e"
is_byokfalse
latency894
model_permaslug"qwen/qwen3.5-27b-20260224"
provider_name"Alibaba"
status200
api_type"completions"
id"gen-1772060691-imv92qzLIR4JVVXCzFKo"
upstream_id"chatcmpl-d923fc7e-075c-9b3d-9f26-0017cbc7657e"
total_cost0.0129471
cache_discount(null)
upstream_inference_cost0
provider_name"Alibaba"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags20
adverbTagCount0
adverbTags(empty)
dialogueSentences72
tagDensity0.278
leniency0.556
rawRatio0
effectiveRatio0
96.52% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1437
totalAiIsmAdverbs1
found
0
adverb"really"
count1
highlights
0"really"
100.00% AI-ism character names
Target: 0 AI-default names (16 tracked, −20% each)
codexExemptions
0"Blackwood"
found(empty)
100.00% AI-ism location names
Target: 0 AI-default location names (33 tracked, −20% each)
codexExemptions(empty)
found(empty)
37.37% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1437
totalAiIsms18
found
0
word"chill"
count1
1
word"footsteps"
count1
2
word"weight"
count2
3
word"silence"
count2
4
word"scanned"
count2
5
word"warmth"
count1
6
word"dancing"
count1
7
word"traced"
count2
8
word"tension"
count1
9
word"reminder"
count1
10
word"flickered"
count1
11
word"gloom"
count1
12
word"gleaming"
count1
13
word"throbbed"
count1
highlights
0"chill"
1"footsteps"
2"weight"
3"silence"
4"scanned"
5"warmth"
6"dancing"
7"traced"
8"tension"
9"reminder"
10"flickered"
11"gloom"
12"gleaming"
13"throbbed"
66.67% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches2
maxInWindow2
found
0
label"eyes widened/narrowed"
count1
1
label"hung in the air"
count1
highlights
0"eyes narrowed"
1"hung in the air"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences167
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences167
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences219
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen28
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1437
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions18
unquotedAttributions0
matches(empty)
33.33% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions42
wordCount1131
uniqueNames7
maxNameDensity1.33
worstName"Cian"
maxWindowNameDensity4
worstWindowName"Cian"
discoveredNames
Soho1
Raven1
Nest1
Cardiff1
Silas14
Aurora9
Cian15
persons
0"Raven"
1"Silas"
2"Aurora"
3"Cian"
places
0"Soho"
1"Cardiff"
globalScore0.837
windowScore0.333
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences86
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1437
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences219
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs96
mean14.97
std15.89
cv1.062
sampleLengths
0108
167
218
315
42
553
646
739
81
947
1016
113
125
1336
143
1517
1624
177
186
1911
201
2134
225
2319
2410
2538
263
2716
281
296
3032
311
3216
339
3431
354
361
3715
3818
399
403
4116
4223
4312
4428
454
469
4720
4813
495
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences167
matches
0"been carved"
1"was gone"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs217
matches
0"was watching"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences219
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1134
adjectiveStacks0
stackExamples(empty)
adverbCount24
adverbRatio0.021164021164021163
lyAdverbCount7
lyAdverbRatio0.006172839506172839
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences219
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences219
mean6.56
std4.57
cv0.696
sampleLengths
018
123
213
324
414
516
617
721
84
97
1018
1112
126
133
1412
152
162
1710
1820
197
2014
217
226
234
2411
255
2613
274
2812
2914
309
311
329
337
343
3518
3610
377
385
394
403
413
422
434
444
4523
465
473
487
499
32.65% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats38
diversityRatio0.2009132420091324
totalSentences219
uniqueOpeners44
46.95% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences142
matches
0"Then the door opened again."
1"Really looked at her."
ratio0.014
0.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount87
totalSentences142
matches
0"She pulled her collar up"
1"His grey-streaked auburn hair caught"
2"He didn’t look up."
3"He knew the rhythm of"
4"She walked to the end"
5"His voice was gravel, worn"
6"He set the glass down"
7"He knew the story behind"
8"He knew the stories behind"
9"She scanned the room."
10"He wore a tailored coat"
11"His hair was cut short,"
12"He scanned the room, his"
13"He hadn’t changed in the"
14"He hadn’t gained weight or"
15"He had sharpened."
16"He walked toward the bar,"
17"He stopped two stools down"
18"He didn’t look at her."
19"He looked at Silas."
ratio0.613
0.00% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount137
totalSentences142
matches
0"The green neon sign buzzed"
1"Aurora pushed through the heavy"
2"She pulled her collar up"
3"The air inside smelled of"
4"Maps covered the walls, yellowed"
5"Silas stood behind the bar,"
6"His grey-streaked auburn hair caught"
7"He didn’t look up."
8"He knew the rhythm of"
9"The slight limp in his"
10"She walked to the end"
11"The leather creaked under her"
12"His voice was gravel, worn"
13"The amber liquid hit the"
14"He set the glass down"
15"He knew the story behind"
16"He knew the stories behind"
17"Aurora wrapped her hands around"
18"The heat seeped into her"
19"She scanned the room."
ratio0.965
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences142
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences35
technicalSentenceCount2
matches
0"The air inside smelled of stale tobacco and polished wood, a scent that had settled into the grain of The Raven’s Nest over decades."
1"Silas stood behind the bar, polishing a glass with a rag that looked older than the building."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags20
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags19
fancyCount2
fancyTags
0"He repeated (repeat)"
1"Aurora repeated (repeat)"
dialogueSentences72
tagDensity0.264
leniency0.528
rawRatio0.105
effectiveRatio0.056
80.4496%