Run: 2026-02-17-01KHMQWEJBVGFVS0NA78REFPAE

Run Details

Duration:
2m 45s
Cost:
$0.005837
Provider:
Parasail

Model input

System
You are an expert novelist with a gift for vivid, immersive prose. You write in tight third-person limited point of view with a strong narrative voice. Your prose is clean, precise, and engaging — you favor strong verbs, concrete details, and natural-sounding dialogue. You will be given a story bible (codex) containing canonical details about characters, locations, items, and lore. Use these details to ground your writing in the established world. Stay consistent with the codex. Write only the prose of the scene. Do not include titles, chapter headings, markdown formatting, or any commentary before or after the scene.
User/Human
<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> 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.

Model response

AI
The rain had been falling since morning, that particular London drizzle that neither committed to downpour nor dignity. Rory wiped her hands on her jeans and pushed through the heavy door of The Raven's Nest, the warmth hitting her face like a held breath. The green neon sign buzzed overhead, casting its familiar glow across the wet pavement, and for the hundredth time she felt the odd comfort of this place—its dim corners and smoke-stained walls, the old maps that Silas had collected from estate sales and forgotten attics. She was early for her shift. Yu-Fei had cut her deliveries short after the bicycle chain snapped halfway through an order to a block of flats in Kennington, and she'd wheeled the thing home with rain running down the back of her collar. Now she just wanted to sit somewhere dry and drink something hot. The bar was quiet for a Thursday. Two men in rain-slicked coats argued softly in a corner booth, their voices blending into the ambient hum of the refrigerator behind the counter. Silas wasn't behind the bar—probably in the back, maybe in the hidden room she'd discovered three months into living upstairs, maybe just doing inventory. She knew better than to ask. She ordered a tea from the girl working the evening shift, a university student with silver rings on every finger, and carried it to a table near the window where she could watch the rain
created_at"2026-02-17T02:49:31.354543+00:00" 2/17/2026, 2:49:31 AM
model"z-ai/glm-5-20260211"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency524
moderation_latency(null)
generation_time164695
tokens_prompt867
tokens_completion1553
native_tokens_prompt867
native_tokens_completion1553
native_tokens_completion_images(null)
native_tokens_reasoning1200
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)
usage0.0058366
router(null)
provider_responses
0
id"92c82141498d4c51b6b6050d097d1d28"
status200
is_byokfalse
latency524
endpoint_id"14b8ca12-e20c-422a-b2e1-0b0f7f8048bf"
provider_name"Parasail"
model_permaslug"z-ai/glm-5-20260211"
api_type"completions"
id"gen-1771296406-fdbVkLTgD6RubVAsCzP2"
upstream_id"92c82141498d4c51b6b6050d097d1d28"
total_cost0.0058366
cache_discount(null)
upstream_inference_cost0
provider_name"Parasail"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags0
79.34% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount242
totalAiIsmAdverbs1
found
0
adverb"softly"
count1
highlights
0"softly"
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)
58.68% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount242
totalAiIsms2
found
0
word"warmth"
count1
1
word"familiar"
count1
highlights
0"warmth"
1"familiar"
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
narrationSentences11
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences11
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences11
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen47
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords240
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions1
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions7
wordCount240
uniqueNames6
maxNameDensity0.83
worstName"Silas"
maxWindowNameDensity1
worstWindowName"Silas"
discoveredNames
London1
Raven1
Nest1
Silas2
Kennington1
Thursday1
persons
0"Raven"
1"Silas"
places
0"London"
1"Kennington"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences11
glossingSentenceCount0
matches(empty)
0.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches1
per1kWords4.167
wordCount240
matches
0"neither committed to downpour nor"
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences11
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs4
mean0
std0
cv0
sampleLengths
089
155
261
335
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences11
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs34
matches(empty)
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount2
semicolonCount0
flaggedSentences2
totalSentences11
ratio0.182
matches
0"The green neon sign buzzed overhead, casting its familiar glow across the wet pavement, and for the hundredth time she felt the odd comfort of this place—its dim corners and smoke-stained walls, the old maps that Silas had collected from estate sales and forgotten attics."
1"Silas wasn't behind the bar—probably in the back, maybe in the hidden room she'd discovered three months into living upstairs, maybe just doing inventory."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount245
adjectiveStacks0
stackExamples(empty)
adverbCount7
adverbRatio0.02857142857142857
lyAdverbCount3
lyAdverbRatio0.012244897959183673
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences11
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences11
mean21.82
std12.79
cv0.586
sampleLengths
018
126
245
36
437
512
67
724
824
96
1035
90.91% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats1
diversityRatio0.6363636363636364
totalSentences11
uniqueOpeners7
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences11
matches(empty)
ratio0
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount3
totalSentences11
matches
0"She was early for her"
1"She knew better than to"
2"She ordered a tea from"
ratio0.273
50.91% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount9
totalSentences11
matches
0"The rain had been falling"
1"Rory wiped her hands on"
2"The green neon sign buzzed"
3"She was early for her"
4"Yu-Fei had cut her deliveries"
5"The bar was quiet for"
6"Silas wasn't behind the bar—probably"
7"She knew better than to"
8"She ordered a tea from"
ratio0.818
100.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount1
totalSentences11
matches
0"Now she just wanted to"
ratio0.091
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences8
technicalSentenceCount0
matches(empty)
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags0
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags0
85.9945%