Run: 2026-09-24-01M39WGBMKXDPS38BFTNXHEHVX

Run Details

Duration:
1m 38s
Cost:
$0.007801
Provider:
AionLabs

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 neon sign buzzed green through the front window, casting a faint glow across the empty bar. Silas Blackwood leaned on his good leg, polishing a glass that didn't need polishing. The Raven's Nest had been quiet all night—a Tuesday, rain chasing even the regulars home early. He tugged at his beard, a habit when the silence stretched too long, and thought about closing early. The door opened. He didn't look up right away. Just the creak of hinges, the hiss of rain, the squeak of wet shoes on floorboards. Then a voice he hadn't heard in nineteen years. "Still watering the drinks down, or was that just your generation?" His hand stopped on the glass. Vivienne Marsh stood in the doorway, dripping. Grey hair now, cropped short. A long wool coat, expensive. She'd lost the Welsh accent somewhere along the way—her vowels had gone flat, Home Counties polish. But the smile was the same crooked one, pulled to the left, the one that used to mean trouble. "Vivi." "Si." He set the glass down. His left knee ached—the one that had ended everything—and he came around the bar without limping, an old habit, a vanity he'd never quite killed. "You're a long way from..." He stopped. He didn't actually know where she was from these days. "Geneva. I'm a long way from Geneva." She shrugged off the coat and hung it on the back of a stool, like she still owned the place. Underneath: a cream turtleneck, a thin gold chain. A wedding ring he hadn't seen before. "I saw the sign from the taxi. Green neon. Thought, that's either him or a very strange coincidence." "Sit down." "Don't mind if I do." She took the stool at the end, the one with a view of the door, and he noted that without comment. Some habits didn't wash out. "What are you drinking?" "Whatever you're having." He poured two whiskies without asking which. Set one in front of her. She turned the glass in her fingers, watching the amber catch the light, and for a moment neither of them spoke. The rain filled the silence. "Nineteen years," she said. "Nineteen." "Last time I saw you, you were being loaded into an ambulance in Prague and I was being loaded into a car with two men who smelled like cabbage." She smiled again, that same crooked smile, but it didn't reach her eyes. "Funny the things you remember. Cabbage." "How did you find me?" "You're in the phone book, Si. 'The Raven's Nest, Soho.' It's not exactly deep cover." She took a sip. Her eyes watered; she blinked it away. "Christ. Still the cheap stuff." "The expensive stuff is for people I like." That earned a short laugh. She set the glass down and looked at him, and he saw it then—the thing that was different. Not the hair, not the accent, not the ring. Something behind her eyes had been packed away. A house someone had moved out of but not yet sold. "You look well," she said. "I look old." "You look old and well. There's a difference." She touched the rim of her glass. "You got fat." "You got married." She looked down at her hand, as if she'd forgotten the ring was there. "Thomas. Twelve years now. He's an accountant. A boring, kind, wonderful man who thinks I was a civil servant in Warsaw for six years." A pause. "He thinks I like gardening." "Do you?" "I'm very good at pretending to." The rain picked up outside, drumming on the awning. Silas came back around the bar and lowered himself onto the stool beside her, his knee protesting, and this time he let it show. Vivi watched him favor the left leg and said nothing, but her jaw tightened. "Does it still hurt?" "Every day." "Good." He turned to look at her. "Don't look at me like that." She stared into her glass. "You know what I mean." "Danny's death wasn't your fault, Vivi." "I didn't say it was." "You just did." She went quiet. When she spoke again, her voice had dropped, flattened out, the way it used to when they were working—when she was reporting something factual, something that had already happened and couldn't be changed. "The men with the car. The cabbage men. They weren't ours, Si. They weren't theirs either. They were mine. I'd been turned six months before Prague. Danny died because I fed them the route, and you got your knee because you were the only one who didn't run when it went sideways." The bar was very quiet. Silas looked at her. She didn't look back. She kept her eyes on the glass, on her hands around the glass, on the wedding ring she'd rotated so the stone faced inward. "Six months," he said. "I know." "You sat across from me in safe houses. You shared—" "I know what I did." Her voice cracked, just barely, and she steadied it. "I know exactly what I did." "Why are you telling me this now? Nineteen years later?" She finally turned to face him. And there it was—the thing behind her eyes, unpacked, laid out on the bar between them. "Because I'm tired, Si. Because I've been a wife and a gardener and a civil servant for twelve years and I've done it well and none of it has made one bit of difference. Because Danny's mother sent me a Christmas card every year until she died three years ago, and I opened every single one, and every single one said 'Thank God someone like you is still looking out for people like us.'" Her breath caught. "I'm sixty-one years old and I have never once told the truth to anyone who mattered." The rain drummed. The neon buzzed. Somewhere in the back, the ice machine clicked on and off. Silas reached across the bar and refilled her glass. His hand was steady. Hers, when she took it, was not. "They cleared you," he said. "After Prague. You went into witness protection—" "I went into a box. They painted the box nicely and called it a garden." "You could have come to me. Any time in nineteen years, you could have—" "What? What could you have done, Si? Look at you. Look at this place." She gestured at the maps on the walls, the black-and-white photographs, the green glow bleeding through the window. "You built a life out of the wreckage too. You just built yours out in the open where everyone could see it. I built mine in a garden in Geneva." "That's not—" "Isn't it?" He didn't have an answer for that. He drank instead, and the cheap whisky burned all the way down, and outside the rain kept coming. Vivi finished her drink. Set the glass down with a soft click. Stood, and reached for her coat. "Vivi." She stopped, one arm in, one arm out. "The Christmas cards," he said. "Danny's mother. Did you ever write back?" "No." She pulled the coat on, buttoned it once, twice. "But I kept every single one." She turned to face him one last time, and for a second—just a second—the crooked smile came back, pulled to the left, the one that used to mean trouble. "Thanks for the drink, Si." The door opened. Rain hissed in. Then it closed behind her. Silas looked at the two empty glasses on the bar. He picked up the cloth and began, methodically, to wipe them clean.
created_at"2026-09-24T14:18:26.846Z" 9/24/2026, 2:18:26 PM
model"aion-labs/aion-3.5-mini-20260923"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency1201
moderation_latency(null)
generation_time98100
tokens_prompt1063
tokens_completion5915
native_tokens_prompt996
native_tokens_completion5444
native_tokens_completion_images(null)
native_tokens_reasoning4140
native_tokens_cached996
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)
usage0.00780088
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1790259506-s5pRMGO6hBTyQmOBdKsy"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1790259506-poMFDUWnNOc9ZCeGk6HV"
upstream_id"2e40640953844902b333a0b5c5dd4f90"
provider_responses
0
endpoint_id"60ac3bdc-90ec-4769-9193-2c00b4d79562"
id"2e40640953844902b333a0b5c5dd4f90"
is_byokfalse
latency1199
model_permaslug"aion-labs/aion-3.5-mini-20260923"
provider_name"AionLabs"
status200
total_cost0.00780088
cache_discount0.00051792
upstream_inference_cost0
provider_name"AionLabs"
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
totalTags15
adverbTagCount2
adverbTags
0"She smiled again [again]"
1"Her voice cracked just [just]"
dialogueSentences58
tagDensity0.259
leniency0.517
rawRatio0.133
effectiveRatio0.069
88.11% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1262
totalAiIsmAdverbs3
found
0
adverb"very"
count3
highlights
0"very"
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)
92.08% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1262
totalAiIsms2
found
0
word"silence"
count2
highlights
0"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
narrationSentences78
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences78
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences120
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen77
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1253
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions10
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions14
wordCount749
uniqueNames10
maxNameDensity0.67
worstName"Silas"
maxWindowNameDensity1.5
worstWindowName"Silas"
discoveredNames
Blackwood1
Raven1
Nest1
Tuesday1
Marsh1
Welsh1
Home1
Counties1
Underneath1
Silas5
persons
0"Blackwood"
1"Raven"
2"Nest"
3"Silas"
places
0"Marsh"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences50
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1253
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences120
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs69
mean18.16
std19.46
cv1.072
sampleLengths
065
13
231
311
46
552
61
71
830
917
1060
112
125
1326
144
153
1639
174
181
1948
205
2131
228
2351
245
253
2618
273
2845
292
306
3147
324
332
341
356
3616
376
385
393
4036
4152
425
4332
444
452
4610
4720
4810
4922
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences78
matches
0"been packed"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount2
totalVerbs146
matches
0"were working"
1"was reporting"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount9
semicolonCount1
flaggedSentences8
totalSentences120
ratio0.067
matches
0"The Raven's Nest had been quiet all night—a Tuesday, rain chasing even the regulars home early."
1"She'd lost the Welsh accent somewhere along the way—her vowels had gone flat, Home Counties polish."
2"His left knee ached—the one that had ended everything—and he came around the bar without limping, an old habit, a vanity he'd never quite killed."
3"Her eyes watered; she blinked it away."
4"She set the glass down and looked at him, and he saw it then—the thing that was different."
5"When she spoke again, her voice had dropped, flattened out, the way it used to when they were working—when she was reporting something factual, something that had already happened and couldn't be changed."
6"And there it was—the thing behind her eyes, unpacked, laid out on the bar between them."
7"She turned to face him one last time, and for a second—just a second—the crooked smile came back, pulled to the left, the one that used to mean trouble."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount760
adjectiveStacks0
stackExamples(empty)
adverbCount29
adverbRatio0.038157894736842106
lyAdverbCount6
lyAdverbRatio0.007894736842105263
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences120
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences120
mean10.44
std10.63
cv1.018
sampleLengths
017
114
216
318
43
56
616
79
811
96
107
115
125
1316
1419
151
161
175
1825
197
2010
2127
228
237
2418
252
265
2721
285
294
303
317
326
3321
345
354
361
3742
386
395
4019
417
425
438
445
4518
469
478
4811
495
66.94% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats8
diversityRatio0.44166666666666665
totalSentences120
uniqueOpeners53
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount4
totalSentences71
matches
0"Just the creak of hinges,"
1"Then a voice he hadn't"
2"Somewhere in the back, the"
3"Then it closed behind her."
ratio0.056
28.45% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount34
totalSentences71
matches
0"He tugged at his beard,"
1"He didn't look up right"
2"His hand stopped on the"
3"She'd lost the Welsh accent"
4"He set the glass down."
5"His left knee ached—the one"
6"He didn't actually know where"
7"She shrugged off the coat"
8"She took the stool at"
9"He poured two whiskies without"
10"She turned the glass in"
11"She smiled again, that same"
12"She took a sip"
13"Her eyes watered; she blinked"
14"She set the glass down"
15"She touched the rim of"
16"She looked down at her"
17"He turned to look at"
18"She stared into her glass"
19"She went quiet."
ratio0.479
51.55% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount58
totalSentences71
matches
0"The neon sign buzzed green"
1"Silas Blackwood leaned on his"
2"The Raven's Nest had been"
3"He tugged at his beard,"
4"The door opened."
5"He didn't look up right"
6"His hand stopped on the"
7"Vivienne Marsh stood in the"
8"A long wool coat, expensive."
9"She'd lost the Welsh accent"
10"He set the glass down."
11"His left knee ached—the one"
12"He didn't actually know where"
13"She shrugged off the coat"
14"A wedding ring he hadn't"
15"She took the stool at"
16"Some habits didn't wash out."
17"He poured two whiskies without"
18"She turned the glass in"
19"The rain filled the silence."
ratio0.817
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences71
matches(empty)
ratio0
53.57% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences24
technicalSentenceCount3
matches
0"Silas Blackwood leaned on his good leg, polishing a glass that didn't need polishing."
1"She looked down at her hand, as if she'd forgotten the ring was there."
2"When she spoke again, her voice had dropped, flattened out, the way it used to when they were working—when she was reporting something factual, something that h…"
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags15
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags5
fancyCount0
fancyTags(empty)
dialogueSentences58
tagDensity0.086
leniency0.172
rawRatio0
effectiveRatio0
86.0235%