Run: 2026-02-17-01KHNFSZPXNJZB606QRM3YER8N

Run Details

Duration:
15.6s
Cost:
$0.000627
Provider:
ModelRun

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 Raven's Nest smelled of spilled whiskey and old wood. Rory wiped down the sticky mahogany counter, her fingers tracing the whorls in the grain. The green neon sign outside flickered through the rain-streaked windows, casting ghostly reflections across the empty stools. Silas was upstairs reviewing inventory, leaving her alone with last call stragglers—a couple murmuring in a corner booth and an old man nursing his third bourbon. The door groaned open with that familiar protest of unoiled hinges. Cold air rushed in, carrying the scent of wet pavement and distant pretzels from the night market. Rory glanced up, towel still pressed into the wood. She froze. The man hesitated under the entrance awning, water dripping from his dark coat. Twelve years had sculpted his face—deeper lines around the mouth, flecks of grey at the temples—but she'd know that hesitant half-step anywhere. Evan used to shift his weight like that whenever he lied to her about where he'd been. "Rory?" His voice cracked on the second syllable. Her fingers tightened around the towel. She counted the bottles on the back shelf—Glenfiddich, Talisker, Lagavulin—letting the familiar names anchor her. "You're tracking rainwater on Silas' floor," she said at last. Evan shrugged out of his coat, revealing a tailored suit beneath. Designer. Probably cost more than her monthly rent. "Didn't know you worked here," he said. "Yeah." She gestured toward a dry section of counter. "Take a seat or don't." He limped forward. That was new—the slightest catch in his right step. He eased onto a stool, hands folded like he used to do during their university debates. Rory noticed the wedding band first. Platinum. Then the scar along his left knuckles, pale against his tanned skin. "You look well," Evan said. She snorted. "Cut the shit." His mouth twitched. Same tell. She'd made him do that once with a particularly bad pun about kangaroos. "Fair enough," he said. "How's Cardiff?" "London," she corrected. "Three years now." The old man snorted into his bourbon. Evan's gaze flickered toward the sound before returning to her face. "I heard you quit law school." Rory reached for a rocks glass. Polished it harder than necessary. "Heard you married Sophia Whitley." Silence pooled between them. The couple in the booth erupted in laughter—something high and bright and oblivious. "I saw your father last month," Evan said at last. "At the Royal Courts." The glass nearly slipped from her fingers. Brendan Carter hadn't spoken to rapid-infiltration-Aurora-chewing-through-the-walls-in-the-dark-dark-dark-Holy-shit everyone okay? mm
created_at"2026-02-17T09:45:08.047339+00:00" 2/17/2026, 9:45:08 AM
model"deepseek/deepseek-chat-v3-0324"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency1204
moderation_latency(null)
generation_time15561
tokens_prompt867
tokens_completion693
native_tokens_prompt815
native_tokens_completion602
native_tokens_completion_images(null)
native_tokens_reasoning0
native_tokens_cached137
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"
usage0.00062654
router(null)
provider_responses
0
id"chatcmpl-b47f78ca665b4e3cbec8e5a50abc016e"
status200
is_byokfalse
latency105
endpoint_id"a3938710-1b99-4a1f-85f4-2494f3f659d3"
provider_name"ModelRun"
model_permaslug"deepseek/deepseek-chat-v3-0324"
api_type"completions"
id"gen-1771321491-1fWfrzS9zNMuIO6tNuY9"
upstream_id"chatcmpl-b47f78ca665b4e3cbec8e5a50abc016e"
total_cost0.00062654
cache_discount(null)
upstream_inference_cost0
provider_name"ModelRun"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags8
adverbTagCount0
adverbTags(empty)
dialogueSentences15
tagDensity0.533
leniency1
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount434
totalAiIsmAdverbs0
found(empty)
highlights(empty)
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)
19.35% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount434
totalAiIsms7
found
0
word"tracing"
count1
1
word"flickered"
count2
2
word"familiar"
count2
3
word"weight"
count1
4
word"silence"
count1
highlights
0"tracing"
1"flickered"
2"familiar"
3"weight"
4"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
narrationSentences43
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences43
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences50
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen27
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords412
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions8
unquotedAttributions0
matches(empty)
80.36% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions15
wordCount359
uniqueNames8
maxNameDensity1.39
worstName"Evan"
maxWindowNameDensity2.5
worstWindowName"Evan"
discoveredNames
Raven1
Nest1
Glenfiddich1
Talisker1
Lagavulin1
Evan5
Carter1
Rory4
persons
0"Raven"
1"Nest"
2"Lagavulin"
3"Evan"
4"Carter"
5"Rory"
places(empty)
globalScore0.804
windowScore0.833
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences27
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount412
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences50
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs19
mean21.68
std17.89
cv0.825
sampleLengths
068
137
22
352
48
531
626
714
847
95
105
1124
126
1324
1416
1517
1614
1715
181
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences43
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs61
matches(empty)
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount7
semicolonCount0
flaggedSentences5
totalSentences50
ratio0.1
matches
0"Silas was upstairs reviewing inventory, leaving her alone with last call stragglers—a couple murmuring in a corner booth and an old man nursing his third bourbon."
1"Twelve years had sculpted his face—deeper lines around the mouth, flecks of grey at the temples—but she'd know that hesitant half-step anywhere."
2"She counted the bottles on the back shelf—Glenfiddich, Talisker, Lagavulin—letting the familiar names anchor her."
3"That was new—the slightest catch in his right step."
4"The couple in the booth erupted in laughter—something high and bright and oblivious."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount381
adjectiveStacks0
stackExamples(empty)
adverbCount12
adverbRatio0.031496062992125984
lyAdverbCount6
lyAdverbRatio0.015748031496062992
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences50
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences50
mean8.24
std5.61
cv0.681
sampleLengths
010
115
217
326
411
517
69
72
813
922
1017
118
126
1315
1410
1511
161
177
187
199
205
213
229
2316
246
251
2612
275
282
293
303
312
3213
334
342
353
363
377
3811
396
406
415
425
434
4413
4510
464
477
488
491
100.00% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats0
diversityRatio0.64
totalSentences50
uniqueOpeners32
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences33
matches
0"Probably cost more than her"
1"Then the scar along his"
ratio0.061
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount9
totalSentences33
matches
0"His voice cracked on the"
1"Her fingers tightened around the"
2"She counted the bottles on"
3"she said at last"
4"She gestured toward a dry"
5"He limped forward."
6"He eased onto a stool,"
7"His mouth twitched."
8"She'd made him do that"
ratio0.273
35.76% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount28
totalSentences33
matches
0"The Raven's Nest smelled of"
1"Rory wiped down the sticky"
2"The green neon sign outside"
3"Silas was upstairs reviewing inventory,"
4"The door groaned open with"
5"Rory glanced up, towel still"
6"The man hesitated under the"
7"Evan used to shift his"
8"His voice cracked on the"
9"Her fingers tightened around the"
10"She counted the bottles on"
11"she said at last"
12"Evan shrugged out of his"
13"She gestured toward a dry"
14"He limped forward."
15"That was new—the slightest catch"
16"He eased onto a stool,"
17"Rory noticed the wedding band"
18"His mouth twitched."
19"She'd made him do that"
ratio0.848
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences33
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences18
technicalSentenceCount0
matches(empty)
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags8
uselessAdditionCount0
matches(empty)
83.33% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags6
fancyCount1
fancyTags
0"she corrected (correct)"
dialogueSentences15
tagDensity0.4
leniency0.8
rawRatio0.167
effectiveRatio0.133
87.2936%