Run: 2026-09-23-01M37HBC4GZYXCG2H349GQ15YC

Run Details

Duration:
47.4s
Cost:
$0.029306
Provider:
OpenAI

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
Aurora recognised Eva by the way she stood at the bar: one foot tucked behind the other, her weight balanced on the toes of her left shoe, ready to leave before she’d decided to. At fourteen, she’d stood that way outside the headteacher’s office. At seventeen, on the platform at Cardiff Central, while they waited for a train they had no money to board. The woman beneath the green light of the Raven’s Nest sign was older, of course. So was Aurora. Eva’s hair, once dyed a red that bled onto every white towel in her mother’s house, was its natural brown. She wore a good black coat and held a glass of water she hadn’t touched. Aurora stopped just inside the door with the cold at her back. She had come down from the flat above the bar for milk. Silas usually kept a carton in the small fridge beneath the counter, and she’d meant to ask for a splash of it, go back upstairs, and make tea. Instead, Eva turned at the sound of the door. For a moment neither of them moved. “Rory?” Nobody in London said her name with that Cardiff lift at the end. Aurora felt it low in her chest, a quick tightening. “Eva.” Eva set the water down. “I thought it was you.” “You could have come upstairs.” “I didn’t know you lived here.” Aurora glanced at Silas. He stood at the far end of the bar polishing a glass, close enough to have heard but giving no sign of it. Behind him, old maps and black-and-white photographs crowded the wall. One showed Soho before either of them was born, the street outside full of people in hats. “I do,” Aurora said. “Above the bar.” Eva gave a small nod, as if this explained something she’d been unable to work out. “I was meeting someone nearby. They cancelled.” “And you ended up here.” “It was raining.” It had stopped twenty minutes ago. Eva’s coat was dry. Aurora looked at the glass of water, then away. Silas came over, his left leg stiff for the first step. “Evening, Rory. What can I get you?” “Milk, if you’ve got it.” “Tea, surely.” “I was going to make it upstairs.” “Sit down. I’ll make you one.” He took in the two of them with a brief look. His silver signet ring clicked against the glass when he set it aside. “For your friend?” Eva shook her head. “I’m fine, thanks.” Silas went to put the kettle on. Aurora took the stool beside Eva because standing there had begun to feel absurd. The bar was quiet for a Thursday. Two men played cards under a map of Europe, speaking too softly for their words to carry. Someone had left a wet umbrella against the door. Eva studied Aurora’s face with an openness that made her want to turn. “Your hair’s black again.” “It’s been black for years.” “I suppose it has.” The last time they had seen each other, Aurora’s hair had been a badly maintained blonde. Eva had helped her dye it in a bathroom sink three days after she arrived in London. She remembered the smell, the dark water rinsing down the drain, Eva telling her to keep her head tilted back. “You look well,” Eva said. Aurora almost laughed. She had worked a double shift at the Golden Empress, a stack of delivery receipts still folded in her jacket pocket. Her feet hurt. There was a crescent of soy sauce on one cuff. “You look different,” she said. Eva looked down at her coat. “I work in an office now.” “That wasn’t what I meant.” “No.” Eva ran a finger along the rim of her glass. “I know.” Silas placed a mug of tea in front of Aurora. He’d remembered the milk without asking. “There’s more hot water if you need it,” he said, and moved away. Aurora wrapped her hands around the mug. Eva had called when Aurora left Evan. She had stayed on the phone while Aurora packed, asking what she had and what she still needed, giving her an address in London and a train time. For eleven days, Aurora slept on Eva’s sofa. Eva left a spare key on the kitchen table and never asked her to explain the bruise under her sleeve. Then, once Aurora had found a room and begun delivering for Yu-Fei, their calls grew shorter. They met twice for coffee. Aurora cancelled the third time. When Eva went home to Cardiff for Christmas, she sent a message that Aurora read and forgot to answer until January. There had been no quarrel she could point to. That made seeing her worse. “How’s your mum?” Aurora asked. “Still in Cardiff. She’s had the kitchen done. It looks like a showroom now, and she won’t put anything on the counters.” Eva smiled. “She asks after you.” “That’s kind.” “She means it.” Aurora took a sip of tea too soon and burned her tongue. “My parents are well, if yours asks.” “I know. Your dad ran into my uncle last month.” Of course he had. Cardiff kept people in one another’s paths long after they wanted to be found. Eva folded her arms on the bar. On her right hand was a plain gold band. Aurora saw it only because Eva shifted her glass into a ring of lamplight. “You’re married.” “Yes.” Aurora waited. Eva turned the band once with her thumb. “His name’s Tom. We got married in June.” “Congratulations.” “Thanks.” “Is he kind?” Eva’s mouth tightened, though not quite into a smile. “Yes. Very.” “I’m glad.” Eva looked at her then. “You don’t have to sound surprised.” “I’m not.” “You are a bit.” Aurora set her mug down. “I didn’t know you were seeing anyone.” “You didn’t know I was engaged.” “I didn’t, no.” “I wrote to you.” The two men at the table laughed at something in their game. Silas changed the music behind the bar; the singer’s voice lowered, then returned through a different set of speakers. “When?” Aurora asked. “Last winter. A card. I sent it to the address you gave me.” Aurora thought of the room in Camden with the bad lock, the heap of post that grew in the hall whenever the downstairs tenant went away. She had left in February. She could see herself standing beside a suitcase, checking the cupboard once more for anything she’d missed. She could not remember a card. “I moved.” “I gathered that.” “I’m sorry.” Eva rubbed her palms together, then stopped. “I should have called.” “I had your number.” “That too.” For the first time, Eva drank her water. Aurora watched her and saw a woman who could bear a silence without rushing to fill it. The girl she’d known would have made a joke by now, probably a good one. “I thought you’d moved out of London,” Aurora said. “I nearly did. Tom got offered a job in Bristol, and I went down to look at flats with him. We spent a whole Saturday there. Then we had dinner at the station and came home.” “Why?” “I couldn’t imagine knowing where everything was yet. Here, at least, I know how to get home from most places.” “That sounds like you.” “Does it?” Eva said it gently. Aurora heard the correction anyway. She thought of the first week in Eva’s flat, of lying awake on the sofa while traffic hissed past below. Eva had gone to work each morning after showing her which cupboard held the mugs and how to turn the shower on without getting soaked. At night she brought home bread or noodles and sat with Aurora on the floor because the sofa wasn’t wide enough for both of them. Aurora had taken those evenings for granted while they were happening. She had been grateful. She had also been waiting, each minute, to be able to shut a door of her own. “I was hard work when I came here,” she said. Eva shook her head. “You were frightened.” “I was rude to you.” “Sometimes.” “You could say I was a nightmare.” “I could. It wouldn’t be true.” Eva glanced toward the door, where the umbrella had begun to drip onto the mat. “You wanted to make your own decisions again. I understood that. I just didn’t always know when you wanted me there.” Aurora pressed her thumb over the little scar on her left wrist. The skin was smooth and pale. She’d cut it on a broken cup when she was eight; Eva had been at the table with her, holding a tea towel to it while Aurora’s mother searched for the car keys. “I didn’t know either,” Aurora said. Eva looked down at Aurora’s hand, and for a second she seemed to remember that kitchen too. “Do you still see him?” Eva asked. Aurora knew who she meant. “No.” “Good.” “He tried calling a few times. A while ago.” “I’m sorry.” “It’s stopped.” Aurora let go of her wrist. “I should have told you when it happened.” “You didn’t owe me a report.” “I could have told you I was all right.” Eva took her time answering. “I would have liked that.” The quiet statement hurt more than blame would have. Aurora turned the mug by its handle. Tea had slopped onto the saucer, and she wiped it with her finger. “What do you do in the office?” she asked. Eva gave her a look. Then she told her. She worked for a housing charity, arranging repairs for people whose landlords had learned to ignore the phone. It was exhausting. Tom made dinner most nights because she forgot how late it was until the cleaners came through. They had a small flat in south London with a window that didn’t close properly. “When we saw it, the agent said the window gave it character,” Eva said. “And you signed the lease.” “It was the cheapest one with a bedroom.” Aurora smiled despite herself. Eva had once made her walk forty minutes in the rain to save the bus fare, then spent twice as much on chips for them both. Silas came by to collect a glass from the next stool. “Another tea, Rory?” “No, thanks.” She hesitated. “Could I have some milk to take up?” “Of course.” He reached beneath the bar for a clean little bottle and filled it from the carton. He put a lid on it, set it beside her mug, and went back to his work. Eva followed him with her eyes. “He seems nice.” “He’s my landlord.” “I’d guessed that much.” “He is nice,” Aurora said. “He also asks whether I’ve paid my electricity bill.” Eva laughed. It was a quieter laugh than Aurora remembered, but she knew it. The men at the table began putting on their coats. One of them waved to Silas, who raised a hand in return. Through the door, when it opened, came the damp smell of the street and a brief wash of green light. Eva checked her phone. “I should go. Tom’s expecting me.” Aurora nodded. She wanted to ask Eva to stay long enough to finish the water, or to come upstairs and see the flat. She also knew how long it took Eva to get home. “Let me give you my number,” she said. “The one I use now.” Eva passed her the phone. Aurora entered it carefully and rang her own, so Eva’s number appeared on her screen. There it was, immediate and ordinary. “I’d like to meet Tom,” Aurora said. “He’d like that. I’ve told him about you.” “What did you say?” “That we were friends when we were young.” Eva paused. “And that you came to London because I asked you to.” Aurora handed the phone back. “That’s true.” “I told him you once ate the sandwich I’d brought on a school trip because you’d forgotten yours. You said you’d pay me back.” “I bought you crisps.” “Two years later.” “They were the good ones.” Eva smiled and put her phone away. At the door, she stopped to pull on her gloves. Aurora had seen her off from thresholds before: her mother’s front step, a train carriage, the flat where she’d slept on the sofa. This time she went over to her. Eva hugged her first. Aurora put one arm around the good black coat, then the other. It was a brief, awkward embrace, interrupted by the door pushing at Eva’s elbow. “I’m glad I came in,” Eva said. “Me too.” “I’ll text you tomorrow.” “I’ll answer.” Eva nodded. Aurora held the door while she stepped out. The green sign lit her face for a moment before she turned up the street and passed beyond it. Aurora stayed there until the cold reached through her shirt. Then she shut the door and went back for the bottle of milk. Her tea had gone lukewarm. She drank the last of it anyway, standing at the bar while Silas counted the till. When her phone lit up, it was a message from Eva: I left my umbrella there. Aurora looked toward the mat. She picked up the umbrella and typed, I’ll keep it safe. Eva’s reply arrived before she could put the phone down. I know.
created_at"2026-09-23T16:25:00.312Z" 9/23/2026, 4:25:00 PM
model"openai/gpt-6-sol-20260922"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency2311
moderation_latency(null)
generation_time47383
tokens_prompt846
tokens_completion3298
native_tokens_prompt808
native_tokens_completion2769
native_tokens_completion_images0
native_tokens_reasoning0
native_tokens_cached0
num_media_prompt(null)
num_input_audio_prompt(null)
num_media_completion0
num_search_results(null)
num_fetches(null)
web_search_engine(null)
origin"https://ncbench.com/"
is_byokfalse
finish_reason"stop"
native_finish_reason"completed"
service_tier"default"
usage0.029306
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.20.0; linux; x64))"
http_referer(null)
request_id"req-1790180700-gr3miccbLxRXKKpu2BDc"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1790180700-CWr19SfMv2N0qLkMHAjS"
upstream_id"resp_09fb83b5ff713975016ab3fd5c6b7487d19c2e6e47e0039f9d"
provider_responses
0
endpoint_id"1cfc7d9d-4404-4b8e-9ee4-58ae45c9dcd4"
id"resp_09fb83b5ff713975016ab3fd5c6b7487d19c2e6e47e0039f9d"
is_byokfalse
latency655
model_permaslug"openai/gpt-6-sol-20260922"
provider_name"OpenAI"
status200
total_cost0.029306
cache_discount(null)
upstream_inference_cost0
provider_name"OpenAI"
response_cache_source_id(null)
data_region"global"
workspace_id"97e315e5-d303-487d-83c1-83180e8a13d4"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags24
adverbTagCount0
adverbTags(empty)
dialogueSentences111
tagDensity0.216
leniency0.432
rawRatio0
effectiveRatio0
90.99% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount2219
totalAiIsmAdverbs4
found
0
adverb"softly"
count1
1
adverb"very"
count1
2
adverb"gently"
count1
3
adverb"carefully"
count1
highlights
0"softly"
1"very"
2"gently"
3"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)
95.49% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount2219
totalAiIsms2
found
0
word"weight"
count1
1
word"silence"
count1
highlights
0"weight"
1"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
narrationSentences159
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount1
narrationSentences159
filterMatches
0"know"
hedgeMatches
0"seemed to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences246
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen36
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords2219
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions31
unquotedAttributions0
matches(empty)
0.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions134
wordCount1627
uniqueNames19
maxNameDensity3.26
worstName"Eva"
maxWindowNameDensity5.5
worstWindowName"Eva"
discoveredNames
Eva53
Cardiff4
Central1
Raven1
Nest1
Aurora50
London4
Silas9
Soho1
Thursday1
Europe1
Golden1
Empress1
Evan1
Yu-Fei1
Christmas1
January1
Camden1
February1
persons
0"Eva"
1"Raven"
2"Aurora"
3"Silas"
4"Evan"
places
0"Cardiff"
1"London"
2"Soho"
3"Europe"
4"Golden"
5"Camden"
6"February"
globalScore0
windowScore0
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences105
glossingSentenceCount1
matches
0"not quite into a smile"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount2219
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences246
matches
0"remember that kitchen"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs137
mean16.2
std18.08
cv1.116
sampleLengths
064
153
261
37
41
523
61
710
85
96
1054
117
1223
135
143
1519
1618
175
182
197
2033
217
2254
2317
245
254
2653
275
2837
295
3012
315
3213
3329
3470
3547
3614
375
3828
392
403
4119
4210
4318
4430
452
461
4710
488
491
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences159
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs301
matches
0"were happening"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount2
flaggedSentences2
totalSentences246
ratio0.008
matches
0"Silas changed the music behind the bar; the singer’s voice lowered, then returned through a different set of speakers."
1"She’d cut it on a broken cup when she was eight; Eva had been at the table with her, holding a tea towel to it while Aurora’s mother searched for the car keys."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1630
adjectiveStacks0
stackExamples(empty)
adverbCount56
adverbRatio0.0343558282208589
lyAdverbCount9
lyAdverbRatio0.005521472392638037
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences246
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences246
mean9.02
std6.82
cv0.756
sampleLengths
034
110
220
315
43
520
615
712
812
928
109
117
121
1313
1410
151
165
175
185
196
204
2123
2210
2317
244
253
2616
277
285
293
306
314
329
3311
347
355
362
377
3817
3913
403
414
423
437
4414
457
4617
479
4813
494
43.50% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats16
diversityRatio0.23577235772357724
totalSentences246
uniqueOpeners58
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount5
totalSentences136
matches
0"Instead, Eva turned at the"
1"Then, once Aurora had found"
2"Of course he had."
3"Then she told her."
4"Then she shut the door"
ratio0.037
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount32
totalSentences136
matches
0"She wore a good black"
1"She had come down from"
2"He stood at the far"
3"It had stopped twenty minutes"
4"He took in the two"
5"His silver signet ring clicked"
6"She remembered the smell, the"
7"She had worked a double"
8"Her feet hurt."
9"He’d remembered the milk without"
10"he said, and moved away"
11"She had stayed on the"
12"They met twice for coffee."
13"She had left in February."
14"She could see herself standing"
15"She could not remember a"
16"She thought of the first"
17"She had been grateful."
18"She had also been waiting,"
19"She’d cut it on a"
ratio0.235
44.56% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount113
totalSentences136
matches
0"Aurora recognised Eva by the"
1"The woman beneath the green"
2"Eva’s hair, once dyed a"
3"She wore a good black"
4"Aurora stopped just inside the"
5"She had come down from"
6"Silas usually kept a carton"
7"Nobody in London said her"
8"Aurora felt it low in"
9"Eva set the water down."
10"Aurora glanced at Silas."
11"He stood at the far"
12"Eva gave a small nod,"
13"It had stopped twenty minutes"
14"Eva’s coat was dry."
15"Aurora looked at the glass"
16"Silas came over, his left"
17"He took in the two"
18"His silver signet ring clicked"
19"Eva shook her head."
ratio0.831
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences136
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences72
technicalSentenceCount1
matches
0"Eva gave a small nod, as if this explained something she’d been unable to work out."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags24
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags16
fancyCount0
fancyTags(empty)
dialogueSentences111
tagDensity0.144
leniency0.288
rawRatio0
effectiveRatio0
89.1512%