Run: 2026-03-25-01KMJ8ZKKQMH38ZM93WRQG9H89

Run Details

Duration:
2m 48s
Cost:
$0.001478
Provider:
SiliconFlow

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 hummed with a low, familiar murmur—the clink of whiskey glasses, the rasp of a bartender wiping counter space, the distant flicker of a jukebox playing no song. Aurora leaned against the bar, her fingers drumming a restless rhythm on the worn marble. The air smelled like dust and damp wood, a tang she had memorized after three years of sneaking in the flat above. She wore the same pair of Nolan Reeds she’d had since starting at Golden Empress, their soles cracked with the London’s weight. *Time’s a flat tire*, she thought, then winced at the thought’s cliche, but it clung. She had learned to move fast, avoid settlement. The door jingled. Aurora stiffened, her wrist scar tracing the edge of her damp sleeve like an old wound learning a new rhythm. She turned, expecting the usual—another drunk, another whispered request that never materialized. She froze. Silas Blackwood. His broad frame bent slightly at the waist, the limp in his left leg measured, controlled. He wore his auburn hair swept back, the grey strands framing his temples like brushstrokes. A silver signet ring caught the glow of the neon sign. Her breath slowed. This man had been carved into her memory with the same edges as her childhood: the way he’d once tossed her a pocketknife at a gas station, the low German inflection he’d never quite lost after Prague. The last time she’d seen him, he had a shoulder wound and a suitcase full of passports. That had been six years ago now. He paused at the door, coat still on, eyes scanning the room. The scar on her wrist throbbed. She met his gaze. Silas raised a brow, then scanned again, as if the universe had leapt ahead at an incorrect moment. “I don’t reek,” she said, thumb poised over the credit card in her hand. The jukebox switched to a Nina Simone track. “I haven’t used that line since…” He gestured with the hand not gripping his cane, knuckles rough, the signet glinting. “Cardiff. Your father’s firm prosecution a case. Irishman stole a bank vault. You said he should’ve just burned it all.” Aurora’s fingers stayed coiled around the card. “He was a lit student. Not a criminal.” She released it too quickly, card flopping like a bird’s wing. “Conceded the trial.” “You always saw the forest through the leaves. Even when the leaves were set on fire.” He slid into the seat beside her, a hitching sound in his knee as he moved. The bartender, a wiry lad with a lip ring, approached with the indifference of someone trained for it. Aurora ordered a gin and tonic. Silas asked for scotch, neat. He unbuttoned his coat, revealing the same buttonhole pin he’d worn in university: a raven, always facing left. “You moved here,” he said. “I left Evan. Then I had a flat above this place.” She sipped the gin, letting it sting in her chest. “Turns out I inherited some clairvoyance. Happy to repossess the gift now.” Silas stirred his drink with the silver ring when the bartender didn’t hand him a stirrer. “Evan,” he repeated, enunciating the syllables like a code. “Let’s say one foot and one front door. Six weeks, two train tickets.” She mimed a door slamming with her free hand. The gesture felt hollow—Evan had broken the door frame that day. The bony ridge still echoed in her mind, and the smell of lavender from her mother’s favoritism, a perfume she never found herself near again. He nodded toward her
created_at"2026-03-25T10:34:57.531Z" 3/25/2026, 10:34:57 AM
model"qwen/qwen3-32b-04-28"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency1452
moderation_latency(null)
generation_time168336
tokens_prompt1117
tokens_completion2702
native_tokens_prompt1012
native_tokens_completion2345
native_tokens_completion_images(null)
native_tokens_reasoning1532
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(null)
native_finish_reason(null)
usage0.00147833
router(null)
provider_responses
0
endpoint_id"6f9e09e5-e836-4de0-a27d-27c74b866001"
id"019d248fd1f9d1959c7d02d5a26a699e"
is_byokfalse
latency1419
model_permaslug"qwen/qwen3-32b-04-28"
provider_name"SiliconFlow"
status200
user_agent"langchainjs-openai/1.0.0 ((node/v24.14.0; linux; x64))"
http_referer(null)
api_type"completions"
id"gen-1774434897-aPhi0pxs8mEYGJ7Ci8vH"
upstream_id"019d248fd1f9d1959c7d02d5a26a699e"
total_cost0.00147833
cache_discount(null)
upstream_inference_cost0
provider_name"SiliconFlow"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags8
adverbTagCount0
adverbTags(empty)
dialogueSentences11
tagDensity0.727
leniency1
rawRatio0
effectiveRatio0
83.19% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount595
totalAiIsmAdverbs2
found
0
adverb"slightly"
count1
1
adverb"quickly"
count1
highlights
0"slightly"
1"quickly"
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)
7.56% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount595
totalAiIsms11
found
0
word"familiar"
count1
1
word"flicker"
count1
2
word"marble"
count1
3
word"weight"
count1
4
word"tracing"
count1
5
word"measured"
count1
6
word"scanning"
count1
7
word"throbbed"
count1
8
word"scanned"
count1
9
word"glinting"
count1
10
word"echoed"
count1
highlights
0"familiar"
1"flicker"
2"marble"
3"weight"
4"tracing"
5"measured"
6"scanning"
7"throbbed"
8"scanned"
9"glinting"
10"echoed"
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
narrationSentences40
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences40
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences43
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen37
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans1
markdownWords4
totalWords592
ratio0.007
matches
0"Time’s a flat tire"
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions7
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions21
wordCount496
uniqueNames15
maxNameDensity0.81
worstName"Aurora"
maxWindowNameDensity1.5
worstWindowName"Silas"
discoveredNames
Raven1
Nest1
Nolan1
Reeds1
Golden1
Empress1
London1
Blackwood1
German1
Prague1
Nina1
Simone1
Aurora4
Silas4
Evan1
persons
0"Raven"
1"Nest"
2"Nolan"
3"Reeds"
4"Blackwood"
5"Nina"
6"Simone"
7"Aurora"
8"Silas"
places
0"Golden"
1"London"
2"Prague"
3"Evan"
globalScore1
windowScore1
71.88% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences32
glossingSentenceCount1
matches
0"smelled like dust and damp wood, a tang sh"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount592
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences43
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs18
mean32.89
std31.19
cv0.948
sampleLengths
0112
135
22
32
4106
522
618
714
88
940
1029
1150
1229
135
1433
1525
1658
174
96.49% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences40
matches
0"been carved"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs85
matches(empty)
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount3
semicolonCount0
flaggedSentences3
totalSentences43
ratio0.07
matches
0"The Raven’s Nest hummed with a low, familiar murmur—the clink of whiskey glasses, the rasp of a bartender wiping counter space, the distant flicker of a jukebox playing no song."
1"She turned, expecting the usual—another drunk, another whispered request that never materialized."
2"The gesture felt hollow—Evan had broken the door frame that day."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount499
adjectiveStacks0
stackExamples(empty)
adverbCount17
adverbRatio0.03406813627254509
lyAdverbCount2
lyAdverbRatio0.004008016032064128
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences43
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences43
mean13.77
std8.4
cv0.61
sampleLengths
030
115
222
322
415
58
63
720
812
92
102
1116
1215
1311
143
1537
1617
177
1812
196
204
2118
2214
238
2420
2520
267
2719
283
2932
3018
316
325
3318
345
3521
3612
3716
389
3922
4011
4125
424
65.12% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats2
diversityRatio0.4186046511627907
totalSentences43
uniqueOpeners18
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences37
matches(empty)
ratio0
36.22% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount17
totalSentences37
matches
0"She wore the same pair"
1"She had learned to move"
2"She turned, expecting the usual—another"
3"His broad frame bent slightly"
4"He wore his auburn hair"
5"Her breath slowed."
6"He paused at the door,"
7"She met his gaze."
8"she said, thumb poised over"
9"He gestured with the hand"
10"She released it too quickly,"
11"He slid into the seat"
12"He unbuttoned his coat, revealing"
13"She sipped the gin, letting"
14"he repeated, enunciating the syllables"
15"She mimed a door slamming"
16"He nodded toward her"
ratio0.459
0.00% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount37
totalSentences37
matches
0"The Raven’s Nest hummed with"
1"Aurora leaned against the bar,"
2"The air smelled like dust"
3"She wore the same pair"
4"*Time’s a flat tire*, she"
5"She had learned to move"
6"The door jingled."
7"Aurora stiffened, her wrist scar"
8"She turned, expecting the usual—another"
9"His broad frame bent slightly"
10"He wore his auburn hair"
11"A silver signet ring caught"
12"Her breath slowed."
13"This man had been carved"
14"The last time she’d seen"
15"That had been six years"
16"He paused at the door,"
17"The scar on her wrist"
18"She met his gaze."
19"Silas raised a brow, then"
ratio1
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences37
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences19
technicalSentenceCount1
matches
0"He unbuttoned his coat, revealing the same buttonhole pin he’d worn in university: a raven, always facing left."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags8
uselessAdditionCount0
matches(empty)
59.09% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags3
fancyCount1
fancyTags
0"he repeated (repeat)"
dialogueSentences11
tagDensity0.273
leniency0.545
rawRatio0.333
effectiveRatio0.182
77.3182%