Run: 2026-03-25-01KMJ8B966NQSECVEJ1GTBBHGP

Run Details

Duration:
31.4s
Cost:
$0.001563
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="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 Raven’s Nest was quiet, as it often was on a Tuesday evening. A few regulars murmured in hushed tones over drinks; the clink of glasses punctuated the rhythm of the room. Silas stood behind the bar, polishing a tumbler with slow, deliberate strokes. His silver signet ring caught the dim light once, catching his eye—always a reminder. The front door slid open with a soft groan, and in stepped Aurora Carter. She moved like she belonged there, her black hair neat and shoulder-length, the crescent scar on her wrist hidden beneath a plain cotton sleeve. Her eyes were sharp, bright blue against the dim lighting, scanning briefly before settling on Silas. She walked toward the bar with a calmness that suggested she wasn’t surprised to find him there. “Rory,” he said without looking up. “Aren’t you supposed to be delivering Thai food or something?” Her smile didn’t reach her eyes. “Yu-Fei doesn’t kick me out until three.” She ordered a whiskey, neat, and sat on a stool closer than she usually did. It wasn’t unusual for them to talk late into the night, but tonight felt different, heavier, like the weight of years had settled between them. He watched her sip from the glass, the amber liquid catching her throat’s smooth movement. There was something in her expression now—something sharper than just weariness. Silas leaned on the counter, his leg giving way slightly. He shifted, rolling the pain away with practiced ease. “You look older,” he said. She raised an eyebrow. “So do you.” He chuckled. “Not many survive MI6 without losing their youth in strange ways.” She didn’t answer right away. For a moment, the only sound was the soft hum of the air vent above them. Then someone walked through the door. Silas turned, expecting another customer, but stopped when he saw who entered. It was Evan. He looked worse for wear—his clothes too tight, his skin pale, dark circles rimming his dull eyes. He hesitated in the doorway as if unsure what he’d found, then scanned the bar. When his eyes landed on Aurora, something flickered in them—anger? Regret? Aurora’s grip tightened around the glass in her hand. She took a long drink and exhaled slowly. “Is that…?” Silas didn’t answer. He merely watched as Evan approached the bar, moving as if pulled by invisible strings. “Rory,” he said, voice hoarse, almost disbelieving. “I never thought I’d see you again.” She set the glass down without spilling a drop. “Neither did I.” Evan sat at the far end, leaving several stools empty between them. The tension settled like smoke. He glanced at Silas. “I heard this is your spot these days.” “It is,” Silas said carefully. Aurora stared out past the window. Night had fallen fully, and the city shimmered outside like a bad memory waiting to resurface. “I’m not here for trouble,” Evan said, though the lie rang thin even in his own voice. Aurora finally looked at him. “You always said that.” He opened his mouth to argue, but nothing came out. Silas poured himself another drink. “What are you doing back in London, Evan?” Evan fidgeted with the brim of a napkin. Finally, he spoke. “Needle business.” A pause. “I’m trying to clean up what I left behind.” Aurora snorted softly. “Nice try.” He looked at her—really looked—at the woman he once knew but no longer recognized. She was thinner now, more composed, like she had been carved down to the essentials of herself. “You ran out of me,” he said quietly. “That’s not how it happened,” she replied, equally quiet. “You don’t get to rewrite history just because you feel guilty.” A silence followed, thick as fog. Evan shifted uncomfortably. “I wanted to come sooner.” Silas cut in. “And why now?” “I needed the money. I couldn’t go home. I didn’t have anywhere to go.” Aurora exhaled. “Funny. I remember thinking the same thing when I left.” Evan flinched but kept his head down. Outside, a distant car passed. Inside, time ticked on. “She’s changed,” Silas said to nobody in particular. Aurora lifted her glass. “People do.” Silas studied her face, the fine lines creeping in at the corners. “You’re not the girl who used to sneak into my flat with burnt toast and bad dreams.” “No,” she said. “That girl died in the street one night. Or maybe on a train platform. Either way, I buried her.” Silas nodded. No further words were needed. Evan looked up. “I know what I did to you wasn’t okay. But I didn’t mean for it to last. I thought I could fix it. That I would have another chance.” Aurora met his gaze with something like pity. “You’ve already had your chance.” Evan didn’t move. Instead, he left five pounds on the bar and stood. As he turned to leave, he gave Aurora a small nod. She didn’t return it. When he disappeared into the night, Aurora closed her eyes for a full five seconds too long. Silas placed his hand over hers, stilling it where it hovered near the emptier glass. “You good?” he asked. She blinked and smiled faintly. “Yeah.” But they both knew it wasn’t true. Outside, the neon flickered, casting green shadows across the pavement. Time had passed like water off a stone for some. For others, it had pooled in deep, stagnant places. Inside, Aurora reached for her drink once more.
created_at"2026-03-25T10:23:51.498Z" 3/25/2026, 10:23:51 AM
model"qwen/qwen3-32b-04-28"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency1288
moderation_latency(null)
generation_time31359
tokens_prompt1117
tokens_completion1459
native_tokens_prompt1016
native_tokens_completion1218
native_tokens_completion_images(null)
native_tokens_reasoning0
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"
usage0.0015632
router(null)
provider_responses
0
endpoint_id"2693bf9a-1732-4008-acc9-d241214e936c"
id"cfa923a09eb541ae8fc83eebd8165c12"
is_byokfalse
latency1288
model_permaslug"qwen/qwen3-32b-04-28"
provider_name"AtlasCloud"
status200
user_agent"langchainjs-openai/1.0.0 ((node/v24.14.0; linux; x64))"
http_referer(null)
api_type"completions"
id"gen-1774434231-tSmzDTwe89RkupRJbxCd"
upstream_id"cfa923a09eb541ae8fc83eebd8165c12"
total_cost0.0015632
cache_discount(null)
upstream_inference_cost0
provider_name"AtlasCloud"

Evaluation details

Result Evaluator Details Meta Data
82.35% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags11
adverbTagCount2
adverbTags
0"Silas said carefully [carefully]"
1"he said quietly [quietly]"
dialogueSentences34
tagDensity0.324
leniency0.647
rawRatio0.182
effectiveRatio0.118
72.47% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount908
totalAiIsmAdverbs5
found
0
adverb"slightly"
count1
1
adverb"slowly"
count1
2
adverb"carefully"
count1
3
adverb"softly"
count1
4
adverb"really"
count1
highlights
0"slightly"
1"slowly"
2"carefully"
3"softly"
4"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)
39.43% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount908
totalAiIsms11
found
0
word"reminder"
count1
1
word"scanning"
count1
2
word"weight"
count1
3
word"practiced ease"
count1
4
word"eyebrow"
count1
5
word"scanned"
count1
6
word"flickered"
count2
7
word"tension"
count1
8
word"shimmered"
count1
9
word"silence"
count1
highlights
0"reminder"
1"scanning"
2"weight"
3"practiced ease"
4"eyebrow"
5"scanned"
6"flickered"
7"tension"
8"shimmered"
9"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
narrationSentences80
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences80
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences102
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen29
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords902
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions15
unquotedAttributions1
matches
0"Finally, he spoke."
50.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions38
wordCount683
uniqueNames7
maxNameDensity1.9
worstName"Silas"
maxWindowNameDensity3.5
worstWindowName"Aurora"
discoveredNames
Raven1
Nest1
Tuesday1
Aurora12
Carter1
Silas13
Evan9
persons
0"Raven"
1"Aurora"
2"Carter"
3"Silas"
4"Evan"
places(empty)
globalScore0.548
windowScore0.5
97.92% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences48
glossingSentenceCount1
matches
0"something like pity"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount902
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences102
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs53
mean17.02
std14
cv0.823
sampleLengths
072
157
216
353
426
524
67
713
821
96
1012
113
1243
1319
1418
1514
1612
1729
185
1922
2017
219
2210
2313
2424
255
2631
278
2820
296
308
316
3214
3312
347
359
368
376
3829
3922
407
4132
4213
433
4421
454
4617
4715
484
496
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences80
matches
0"been carved"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs140
matches(empty)
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount6
semicolonCount1
flaggedSentences6
totalSentences102
ratio0.059
matches
0"A few regulars murmured in hushed tones over drinks; the clink of glasses punctuated the rhythm of the room."
1"His silver signet ring caught the dim light once, catching his eye—always a reminder."
2"There was something in her expression now—something sharper than just weariness."
3"He looked worse for wear—his clothes too tight, his skin pale, dark circles rimming his dull eyes."
4"When his eyes landed on Aurora, something flickered in them—anger?"
5"He looked at her—really looked—at the woman he once knew but no longer recognized."
79.08% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount690
adjectiveStacks0
stackExamples(empty)
adverbCount42
adverbRatio0.06086956521739131
lyAdverbCount16
lyAdverbRatio0.02318840579710145
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences102
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences102
mean8.84
std5.77
cv0.653
sampleLengths
013
119
212
314
414
524
616
717
86
910
106
1122
1225
1315
1411
1510
169
175
184
193
202
2111
225
2316
246
2512
263
2717
2815
2910
301
319
328
332
343
3515
367
377
389
393
4012
415
424
438
445
456
4616
4717
485
494
66.01% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats1
diversityRatio0.4019607843137255
totalSentences102
uniqueOpeners41
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount3
totalSentences72
matches
0"Then someone walked through the"
1"Finally, he spoke."
2"Instead, he left five pounds"
ratio0.042
70.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount27
totalSentences72
matches
0"His silver signet ring caught"
1"She moved like she belonged"
2"Her eyes were sharp, bright"
3"She walked toward the bar"
4"he said without looking up"
5"Her smile didn’t reach her"
6"She ordered a whiskey, neat,"
7"It wasn’t unusual for them"
8"He watched her sip from"
9"He shifted, rolling the pain"
10"She raised an eyebrow."
11"She didn’t answer right away."
12"It was Evan."
13"He looked worse for wear—his"
14"He hesitated in the doorway"
15"She took a long drink"
16"He merely watched as Evan"
17"he said, voice hoarse, almost"
18"She set the glass down"
19"He glanced at Silas."
ratio0.375
71.11% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount56
totalSentences72
matches
0"The Raven’s Nest was quiet,"
1"Silas stood behind the bar,"
2"His silver signet ring caught"
3"The front door slid open"
4"She moved like she belonged"
5"Her eyes were sharp, bright"
6"She walked toward the bar"
7"he said without looking up"
8"Her smile didn’t reach her"
9"She ordered a whiskey, neat,"
10"It wasn’t unusual for them"
11"He watched her sip from"
12"Silas leaned on the counter,"
13"He shifted, rolling the pain"
14"She raised an eyebrow."
15"She didn’t answer right away."
16"Silas turned, expecting another customer,"
17"It was Evan."
18"He looked worse for wear—his"
19"He hesitated in the doorway"
ratio0.778
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences72
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences30
technicalSentenceCount1
matches
0"He merely watched as Evan approached the bar, moving as if pulled by invisible strings."
79.55% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags11
uselessAdditionCount1
matches
0"Evan said, though the lie rang thin even in his own voice"
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags11
fancyCount1
fancyTags
0"She ordered (order)"
dialogueSentences34
tagDensity0.324
leniency0.647
rawRatio0.091
effectiveRatio0.059
83.5972%