Run: 2026-02-19-01KHTR2BN5BC91KGSDM7GH6809

Run Details

Duration:
1m 14s
Cost:
$0.038106
Provider:
OpenAI

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
She stepped inside. A chipped green neon sign cast fractured emerald across her coat. Chairs, polished smooth by elbows and time, hugged the walls. The air smelled of spilled whisky and damp maps framed in cracked wood. One map showed Prague, its borders folded and frayed. Black-and-white photographs lined the walls: figures in trench coats, faces lit by streetlamps. She froze. He knelt behind the bar, polishing a tumbler with slow precision. Grey-streaked auburn hair fell over his forehead. A neat beard masked most of his jaw. His left leg held a faint hitch. “Aurora?” Her breath snagged. “Silas.” He tucked the glasscloth into his sleeve. Hazel eyes held hers for a calculated moment. A silver signet ring gleamed on his right hand. “Didn’t expect to find you here,” he said, voice low. She drifted closer, boots tapping the wood. “Neither did I.” He straightened, shifting weight off the injured leg. “Been some time.” She pressed a palm to the bar, revealing the crescent scar on her left wrist. “Seven years?” He traced the scar with his thumb. “Six.” His thumb lingered on the mark she’d carried since she’d been eight. She slid her hand away. “You remember.” “I never forgot.” He set the tumbler down and reached under the bar, surfacing with a dusty bottle. “Drink?” She hesitated. He uncorked it. The pop cracked the hush. Amber liquid filled two glasses. “One for the roads we missed,” he murmured. She lifted her glass. “And the bridges we burned.” They drank in silence. Music from a battered jukebox thrummed beneath it all. She felt the wood’s grain under her fingertips, each ridge a year lost. He slid a photograph across. Two silhouettes on Charles Bridge. Her hair was longer then, wind tousling it across her face. He stood beside her, trench coat soaked. “When did you take this?” she asked. “Night before Prague.” He tapped the photo. “You’d just started at Cardiff.” She let her gaze drift to the map overhead. “You left a week later.” He closed his eyes. “I thought I’d come back.” She swallowed, voice low. “I was angry you didn’t.” He opened his eyes. “I thought you’d hate me for it.” She turned the glass, thumb and forefinger grazing the rim. “I hated you for not writing.” He laid the photo beside the ledger he’d slid out moments earlier. “I wrote. You never answered.” Her chest tightened. “I didn’t know what to say.” He studied her. “You needed distance.” She blinked against the bar’s dim light. “I ran toward freedom. Actually, I ran from him.” He set his jaw. “I heard.” She reached inside her coat and pulled out a crumpled receipt from Golden Empress. “Day job keeps me moving.” He eyed the slip. “Still dodging stakes and flash floods?” She let a dry laugh escape. “Spare me the review.” He leaned forward, tapping his signet ring. “You’ve always outrun my worst questions.” She met his gaze. Bright blue eyes steady. “You’ve always found a way to catch up.” He straightened, tossing a cube of ice into each glass. “So. Here we are.” She exhaled a thread of air. “You still limping?” He flexed the uninjured leg. “Old Prague wound.” She touched her scar. “Old Cardiff scar.” He regarded her carefully. “You want to talk about it?” She dragged her gaze away. “Not tonight.” He set the glasses before her. “Fair enough.” A pause stretched. She let the glass cool her fingers. He gestured at the walls. “Maps, photos. This place still smells of missions.” She traced a crack in the bar. “Safe houses. Ghosts.” He tapped the board behind his head. “Hidden room through that shelf.” She pictured the secret door. “You never take me there.” He shrugged, limp and all. “Not necessary.” She stood straighter, as if gathering a shield. “You could have called.” He leaned on both hands, old scars on his knuckles. “You could have come looking.” She lifted her chin. “I didn’t know where to start.” He studied the ring again, its crest worn nearly smooth. “Some promises die harder than others.” She traced the rim of her glass with a fingertip. “I carried mine like ballast.” He met her eyes. “You’re still afloat.” She bit her lip. “Barely.” He lifted his glass. “To staying afloat.” She clinked hers against his. The glass rang clear. He set it down. “I was sorry about Evan.” Her throat clenched. “He left bruises I still feel.” He reached out, fingers brushing her wrist. “You deserved better.” She looked at where their skin nearly met. “I deserved someone who’d stay.” He nodded. “I should’ve stayed in Cardiff.” She tilted her head. “I should’ve let you.” He rested both hands on the bar. “Maybe it’s still not too late.” She held his gaze, searching the corners of his eyes. “Maybe.” He swallowed. “Regret’s a cold bed.” She traced her scar again. “Made me restless.” He flexed the ring. “Made me restless too.” She brushed hair behind her ear, revealing lines of muscle along her jaw. “What do we do now?” He stared at the maps above him. “We start with the next step.” She pushed her glass toward him. “Pour another.” He lifted the bottle. “To new steps.” She watched the liquid pour, golden and warm, like a promise neither dared voice. He raised the fresh drink. “To time wasted.” She met his glass halfway. “Time to reclaim.” They drank again, silence laced with something heavier than whisky. Across the bar, the green sign flickered, lighting their faces, their scars, the years they’d carried apart. Neither stepped forward. Neither pulled back. They simply sat, two figures wound tight with memories and regrets, perched on opposite ends of a counter that held every chance they’d missed—and every one still waiting.
created_at"2026-02-19T10:45:26.314Z" 2/19/2026, 10:45:26 AM
model"openai/o4-mini-high-2025-04-16"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency8513
moderation_latency(null)
generation_time73789
tokens_prompt1094
tokens_completion2493
native_tokens_prompt982
native_tokens_completion8415
native_tokens_completion_images0
native_tokens_reasoning7040
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"completed"
usage0.0381062
router(null)
provider_responses
0
endpoint_id"60020533-2fb2-4aa1-9454-181029fd52de"
id"resp_06ef0e4a3af2077a016996e9c7a4408194893188d589a8571b"
is_byokfalse
latency1405
model_permaslug"openai/o4-mini-high-2025-04-16"
provider_name"OpenAI"
status200
api_type"completions"
id"gen-1771497926-iYCovYeTwBmRnfvnXOCX"
upstream_id"resp_06ef0e4a3af2077a016996e9c7a4408194893188d589a8571b"
total_cost0.0381062
cache_discount(null)
upstream_inference_cost0
provider_name"OpenAI"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags5
adverbTagCount0
adverbTags(empty)
dialogueSentences67
tagDensity0.075
leniency0.149
rawRatio0
effectiveRatio0
94.83% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount967
totalAiIsmAdverbs1
found
0
adverb"carefully"
count1
highlights
0"carefully"
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)
48.29% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount967
totalAiIsms10
found
0
word"fractured"
count1
1
word"calculated"
count1
2
word"weight"
count1
3
word"traced"
count4
4
word"silence"
count2
5
word"flickered"
count1
highlights
0"fractured"
1"calculated"
2"weight"
3"traced"
4"silence"
5"flickered"
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
narrationSentences101
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences101
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences163
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
totalWords963
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions6
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions5
wordCount687
uniqueNames5
maxNameDensity0.15
worstName"Prague"
maxWindowNameDensity0
worstWindowName(null)
discoveredNames
Prague1
Charles1
Bridge1
Golden1
Empress1
persons(empty)
places
0"Prague"
1"Charles"
2"Bridge"
3"Golden"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences50
glossingSentenceCount1
matches
0"as if gathering a shield"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount963
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences163
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs76
mean12.67
std9.62
cv0.76
sampleLengths
061
133
21
34
424
510
610
711
817
98
1017
112
1219
1315
148
159
1626
1728
187
1912
2014
219
229
2311
2416
2517
269
276
2816
296
3019
3110
3210
3313
3416
3514
369
378
387
3910
407
418
4210
4313
4410
4512
4610
477
4812
4915
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences101
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs144
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount1
semicolonCount0
flaggedSentences1
totalSentences163
ratio0.006
matches
0"They simply sat, two figures wound tight with memories and regrets, perched on opposite ends of a counter that held every chance they’d missed—and every one still waiting."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount691
adjectiveStacks0
stackExamples(empty)
adverbCount14
adverbRatio0.020260492040520984
lyAdverbCount4
lyAdverbRatio0.005788712011577424
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences163
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences163
mean5.91
std3.68
cv0.622
sampleLengths
03
111
210
313
49
513
62
711
87
98
107
111
123
131
147
158
169
1710
187
193
208
213
2215
232
247
251
2612
275
282
2918
301
312
323
335
345
358
364
375
384
399
4013
415
425
4311
447
457
467
475
489
495
49.90% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats2
diversityRatio0.3067484662576687
totalSentences163
uniqueOpeners50
35.09% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences95
matches
0"Bright blue eyes steady."
ratio0.011
0.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount76
totalSentences95
matches
0"She stepped inside."
1"He knelt behind the bar,"
2"His left leg held a"
3"Her breath snagged."
4"He tucked the glasscloth into"
5"he said, voice low"
6"She drifted closer, boots tapping"
7"He straightened, shifting weight off"
8"She pressed a palm to"
9"He traced the scar with"
10"His thumb lingered on the"
11"She slid her hand away."
12"He set the tumbler down"
13"He uncorked it."
14"She lifted her glass."
15"They drank in silence."
16"She felt the wood’s grain"
17"He slid a photograph across."
18"Her hair was longer then,"
19"He stood beside her, trench"
ratio0.8
0.00% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount89
totalSentences95
matches
0"She stepped inside."
1"A chipped green neon sign"
2"Chairs, polished smooth by elbows"
3"The air smelled of spilled"
4"He knelt behind the bar,"
5"A neat beard masked most"
6"His left leg held a"
7"Her breath snagged."
8"He tucked the glasscloth into"
9"Hazel eyes held hers for"
10"A silver signet ring gleamed"
11"he said, voice low"
12"She drifted closer, boots tapping"
13"He straightened, shifting weight off"
14"She pressed a palm to"
15"He traced the scar with"
16"His thumb lingered on the"
17"She slid her hand away."
18"He set the tumbler down"
19"He uncorked it."
ratio0.937
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences95
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences22
technicalSentenceCount0
matches(empty)
25.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags5
uselessAdditionCount1
matches
0"he said, voice low"
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags3
fancyCount1
fancyTags
0"he murmured (murmur)"
dialogueSentences67
tagDensity0.045
leniency0.09
rawRatio0.333
effectiveRatio0.03
81.7703%