Run: 2026-07-09-01KX41G8AW1604NQJQKRS318G0

Run Details

Duration:
34.0s
Cost:
$0.044290
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
Rain dragged silver threads down the front window of the Raven’s Nest. Outside, Soho shuffled beneath umbrellas and hoods, taxis smearing yellow across wet cobbles. The green neon raven above the door blinked through the weather, one wing lit, the other dead. Aurora set three empty pint glasses into the rack beneath the bar and rubbed the crescent scar on her left wrist with her thumb. “You’re polishing that glass for the fourth time,” Silas called from the far end. He sat on a stool beneath a framed photograph of a man in a naval uniform, one trouser leg angled around his bad knee. His silver signet ring caught the low amber light as he turned a page in the Evening Standard. “It’s still got fingerprints on it.” “It’s a pub glass, Rory. Fingerprints give it character.” “Your character standard has dropped.” “My standards got me shot at in Prague. I’ve adjusted.” Aurora looked towards the window. A man stood under the awning, head lowered against the rain. He pushed through the door when the wind gave him a shove. The bell above it gave a thin, irritated clang. He entered with water darkening the shoulders of his coat. He had once been narrow at the waist and restless in every limb, always moving as if someone had wound a spring in his back. Now he filled the doorway. A navy wool overcoat hung neatly from square shoulders. His hair, once blond enough to look white beneath Cardiff sun, had darkened to brown and receded at the temples. Aurora did not recognise him until he took off his glasses. He saw her at the same moment. The rain had left beads on his lashes. His mouth opened, then closed. “Rory?” The glass slipped from her hand and struck the rubber mat, not breaking. “Tom.” Silas lowered his newspaper. Tom Harcourt’s gaze moved over her face, the black hair brushing her shoulders, the black shirt tucked into faded jeans, the thin gold hoop in her left ear. It stayed on her a second too long, as if he had walked into a room where the furniture stood in the wrong places. “You work here,” he said. “You got observant in your old age.” A flicker passed through him. He smiled, but the smile did not land. “I was looking for somewhere dry.” “You found a bar. London still had miracles.” He came closer. His shoes were expensive, dark leather without a spot of mud despite the rain. Aurora watched him take in the old maps on the walls, the bottles stacked behind her, the row of black-and-white faces in crooked frames. “This place used to be a butcher’s,” he said. “No, it didn’t.” “It did. On Dean Street.” “This is Greek Street.” Tom glanced at the frosted window, then back at her. “Right.” Silas folded the newspaper along its crease. “What’ll you have?” Tom looked towards him. “Whisky, please.” “Which one?” “Whatever’s decent.” Silas’s hazel eyes settled on him. “That narrows it down less than you’d think.” “A Talisker, then.” Silas reached for a bottle. Aurora kept both hands on the counter. “You lived in Cardiff last I heard.” Tom took off his coat. A wedding ring flashed on his left hand before he folded the coat over one arm. The sight of it struck her harder than it should have. Not because she wanted him. That had never been the shape of it. Because she had once known every scrape on his hands, every lie he told when he skipped lectures, the exact way he rolled cigarettes beneath the library fire escape. A ring belonged to a stranger. “I’m in London now,” he said. “Have been for a while.” “How long?” His jaw tightened. “Four years.” Aurora let out a short breath through her nose. “That qualifies as a while.” Silas placed the whisky before him. Tom paid with a crisp note, then left the change on the bar without looking at it. “Keep it,” he said. Silas slid the coins towards the till. “We don’t do charity for staff.” “It wasn’t—” “It’s your whisky. Your money stays your money.” Tom nodded once. Aurora poured herself a small glass of tonic water and lifted it. “Four years in London. You never thought to mention it?” “I didn’t know where you were.” “Eva knew.” “Eva stopped answering.” “She didn’t stop answering. You stopped being worth the effort.” Tom stared at the whisky. The room held three other customers: a couple muttering over a crossword by the radiator, a man asleep over a half-finished stout, and a woman in a red scarf typing furiously at a laptop. Nobody turned around. The bar had learned long ago how to keep its eyes on its own drink. “I deserved that,” Tom said. “You came in for a whisky. Don’t act like you bought a confession with it.” His fingers tightened around the glass. “I came in because I saw the sign. I thought it looked familiar.” “The raven?” “The colour.” “What colour?” He looked up. “Green.” Aurora’s mouth flattened. “You remembered that.” Tom touched the rim of his glass. “You had that green coat. The horrible one with the ripped lining.” “It wasn’t horrible.” “It had a button shaped like a fox.” “You tore that off.” “You told everyone I did.” “You did.” “I did, yes.” Silas picked up his paper again, though he did not read it. Tom gave a small laugh. It cracked in the middle. “God. That coat.” Aurora remembered the coat. Her mother had bought it second-hand from a woman near Roath Park. It smelled of cedar blocks and somebody else’s perfume. Tom had stolen it from the back of her chair during a sixth-form party, then worn it around the garden while pretending to interview hedgehogs for a wildlife programme. She had been furious until he bowed before her, fox button held between his teeth. The memory arrived whole, bright as a shard of glass. She hated it for still fitting. “You look different,” he said. “People do that.” “No. I mean—” “You mean I don’t look like the girl who used to help you cheat in chemistry.” “You never helped me cheat.” “I gave you my notes.” “You put wrong answers in them.” “Only twice.” “You nearly made me fail.” “You passed.” “Barely.” Aurora lifted her tonic. “A triumph.” Tom did not drink. “You were going to be a barrister.” “There are lots of things I was going to be.” “What happened?” She looked at him then. Properly. “What happened to you?” The question took the colour from his face. He glanced down at his coat, the tailored shirt beneath it, the pale line of a watch beneath his cuff. “I work in finance.” “Of course you do.” “That sounded crueler than I expected.” “It sounded exactly how you expected.” “I didn’t choose it because I loved it.” “Nobody held a gun to your head.” “No,” Tom said. “They used tuition fees.” Aurora’s laugh came out sharp. “You always had a family who could have paid them.” “My father could have. He didn’t.” “Your father bought you a car at nineteen.” “He bought it because he crashed mine.” “You had a mine.” Tom’s eyes went flat. “There she is.” Aurora set down her glass. “Don’t do that.” “Do what?” “Turn me into a story you can tell yourself. Rory, the furious girl behind the bar. Rory, who never let anyone off the hook.” “You were never just that.” “You don’t know what I am.” His ring tapped once against the glass. “No. I don’t.” The woman in the red scarf shut her laptop, tucked it into her bag, and left without a glance. The door opened. Rain smell washed through the room, cold and mineral. The bell complained again. Tom watched the door close. “I looked for you,” he said. Aurora leaned against the back counter. “When?” “After university.” “You mean when you left Cardiff without saying goodbye?” “I didn’t leave without saying goodbye.” “You left me a voicemail.” “I was twenty-two.” “That was old enough to know better.” “I knew I was being a coward.” “Then why did you do it?” He pressed his lips together. For a moment she thought he would give her something polished, something that belonged in a boardroom or a marriage photograph. Then his shoulders dropped. “Because you asked me to come with you.” The bar seemed to pull away from her. “I asked you to come to London for a weekend.” “You asked if I wanted to see what else was out there.” “That wasn’t a proposal.” “It felt like one.” Aurora blinked. “Tom.” “You were packed.” He stared into the whisky. “You had that bag with the broken zip. You had a train ticket in your coat pocket. You kept saying Cardiff was too small, that your dad had already picked out the life he wanted you to live. I thought you wanted me to choose you over everything.” “I wanted my best friend to get on a train with me.” “I was scared.” “So you vanished.” “My dad had offered me a job.” “At his firm.” “Yes.” “And you took it.” “Yes.” “You could have told me.” “I thought you’d laugh at me.” “I would have.” “I know.” “Then I would have stopped.” Tom’s expression shifted. “Would you?” Aurora looked past him at the map of Europe behind the bar. Its corners had curled away from the wall. Thin red pins marked cities Silas never discussed. At twenty-two, she had stood on Cardiff Central platform with a cheap ticket in her pocket and an overnight bag at her feet. Tom had promised to meet her beneath the departure board. She had waited until the board changed twice. When his voicemail came, she had listened to it once. He had sounded breathless. Sorry. Something came up. I can’t do this. You’ll understand one day. She had not understood. Not then. “I waited for you,” she said. “I know.” The words hung there. “You know?” “I went to the station.” Her hand curled around the edge of the counter. “You what?” “I got there after the train left.” His voice stayed low. “I saw you from the street. You were sitting on the bench.” Aurora felt heat climb her throat. “You watched me?” “I couldn’t walk in.” “You couldn’t walk in.” “I know what it sounds like.” “It sounds like you stood there while I thought you were dead in a ditch.” “I wanted to go to you.” “But you didn’t.” “No.” Silas set the newspaper aside. “Mr Harcourt,” he said, “there’s a limit to how much self-pity one man can pour into a whisky before it spoils the place.” Tom nodded, accepting the blow. “Fair.” Aurora looked at Silas. His face had not changed, but his signet ring rested against the bar as if he had put a weight there. Tom reached for his coat. “I should go.” “That’s your speciality,” Aurora said. He stopped. The sentence tasted bitter the moment it left her. She had wanted it to cut. It did. Not only him. Tom put the coat back down. “You’re right,” he said. “I left. I left Cardiff, and I left you there, and I spent years making it sound like the sensible thing. I got good at explaining it. To my wife. To people at work. To myself.” Aurora’s gaze dropped to the ring. “Your wife knows about me?” “She knows there was someone I failed.” “That must make dinner parties exciting.” “She left six months ago.” The ring had already told her enough, yet she watched him twist it once around his finger. “I’m sorry,” she said before she could stop herself. Tom gave a tired nod. “Don’t be. I didn’t lose her because of you. I lost her because I treated every difficult thing like a train I could miss and catch later.” Aurora’s chest tightened. He took the ring off. The pale groove beneath it looked raw in the bar’s low light. He placed it beside his empty whisky glass. “I kept it on because people asked fewer questions,” he said. Silas looked at the ring, then at Tom. “People ask questions. That’s their hobby.” Tom almost smiled. Aurora picked up the ring between finger and thumb. It was heavier than it looked. A small inscription ran along the inner band. She did not read it. She held it out. “You should put this away.” Tom looked at her hand. The scar on her wrist showed pale against her skin. “You still have that,” he said. “It’s a scar, Tom. They don’t leave because you ignore them.” His breath caught. He took the ring, but did not put it back on. “No,” he said. “They don’t.” The sleeping man at the end of the bar woke with a snort, fumbled for his coat, and stumbled towards the door. Silas watched until he left, then took a clean glass from the rack. “Another?” he asked Tom. Tom looked at Aurora. She moved aside from the till and reached for the Talisker. “One,” she said.
created_at"2026-07-09T18:16:08.807Z" 7/9/2026, 6:16:08 PM
model"openai/gpt-5.6-terra-20260709"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency486
moderation_latency(null)
generation_time33524
tokens_prompt1094
tokens_completion3647
native_tokens_prompt982
native_tokens_completion2789
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"
usage0.04429
router(null)
provider_responses
0
endpoint_id"3cc89000-ae34-4dde-9c7d-5ce009c8af0b"
id"resp_0b802ac2c2a32d88016a4fe568ecb48195a05dec80138a7db9"
is_byokfalse
latency260
model_permaslug"openai/gpt-5.6-terra-20260709"
provider_name"OpenAI"
status200
user_agent"langchainjs-openai/1.0.0 ((node/v24.18.0; linux; x64))"
http_referer(null)
request_id"req-1783620968-caHG2Z7xEcEgFRT2QA1Y"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1783620968-Aps4YBHz560e0aqiGO9i"
upstream_id"resp_0b802ac2c2a32d88016a4fe568ecb48195a05dec80138a7db9"
total_cost0.04429
cache_discount(null)
upstream_inference_cost0
provider_name"OpenAI"
response_cache_source_id(null)
data_region"global"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags21
adverbTagCount0
adverbTags(empty)
dialogueSentences157
tagDensity0.134
leniency0.268
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount2189
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)
88.58% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount2189
totalAiIsms5
found
0
word"standard"
count2
1
word"flicker"
count1
2
word"familiar"
count1
3
word"weight"
count1
highlights
0"standard"
1"flicker"
2"familiar"
3"weight"
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
filterCount0
hedgeCount1
narrationSentences159
filterMatches(empty)
hedgeMatches
0"seemed to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences295
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen48
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords2189
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions34
unquotedAttributions0
matches(empty)
50.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions74
wordCount1302
uniqueNames15
maxNameDensity2
worstName"Tom"
maxWindowNameDensity3.5
worstWindowName"Tom"
discoveredNames
Raven1
Nest1
Soho1
Evening1
Standard1
Cardiff2
Harcourt1
Tom26
Silas13
Roath1
Park1
Aurora22
Europe1
Central1
Talisker1
persons
0"Raven"
1"Harcourt"
2"Tom"
3"Silas"
4"Aurora"
places
0"Soho"
1"Evening"
2"Cardiff"
3"Roath"
4"Park"
5"Europe"
globalScore0.502
windowScore0.5
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences83
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount2189
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences295
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs206
mean10.63
std13.13
cv1.236
sampleLengths
042
124
214
342
46
59
65
710
828
99
1069
1111
127
1313
141
1513
161
174
1852
195
207
2113
226
238
2441
259
263
275
284
2910
301
317
323
336
342
352
3614
373
385
3914
4080
4111
422
433
442
4514
4623
474
4813
492
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
pastProgressiveCount0
totalVerbs228
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences295
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1308
adjectiveStacks0
stackExamples(empty)
adverbCount39
adverbRatio0.02981651376146789
lyAdverbCount4
lyAdverbRatio0.0030581039755351682
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences295
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences295
mean7.42
std6.45
cv0.869
sampleLengths
012
113
217
324
414
524
618
76
89
95
1010
115
1211
1312
149
1510
1625
175
189
1920
2011
217
228
235
241
2513
261
274
2828
2924
305
317
325
338
346
358
363
3714
3824
399
403
415
424
4310
441
457
463
474
482
492
43.22% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats20
diversityRatio0.22033898305084745
totalSentences295
uniqueOpeners65
48.66% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences137
matches
0"Then his shoulders dropped."
1"Not only him."
ratio0.015
88.61% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount45
totalSentences137
matches
0"He sat on a stool"
1"His silver signet ring caught"
2"He pushed through the door"
3"He entered with water darkening"
4"He had once been narrow"
5"His hair, once blond enough"
6"He saw her at the"
7"His mouth opened, then closed."
8"It stayed on her a"
9"He smiled, but the smile"
10"He came closer."
11"His shoes were expensive, dark"
12"His jaw tightened."
13"His fingers tightened around the"
14"He looked up."
15"It cracked in the middle."
16"Her mother had bought it"
17"It smelled of cedar blocks"
18"She had been furious until"
19"She hated it for still"
ratio0.328
0.15% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount126
totalSentences137
matches
0"Rain dragged silver threads down"
1"The green neon raven above"
2"Aurora set three empty pint"
3"Silas called from the far"
4"He sat on a stool"
5"His silver signet ring caught"
6"Aurora looked towards the window."
7"A man stood under the"
8"He pushed through the door"
9"The bell above it gave"
10"He entered with water darkening"
11"He had once been narrow"
12"A navy wool overcoat hung"
13"His hair, once blond enough"
14"Aurora did not recognise him"
15"He saw her at the"
16"The rain had left beads"
17"His mouth opened, then closed."
18"The glass slipped from her"
19"Silas lowered his newspaper."
ratio0.92
72.99% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount2
totalSentences137
matches
0"Now he filled the doorway."
1"Because she had once known"
ratio0.015
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences45
technicalSentenceCount2
matches
0"For a moment she thought he would give her something polished, something that belonged in a boardroom or a marriage photograph."
1"His face had not changed, but his signet ring rested against the bar as if he had put a weight there."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags21
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags19
fancyCount0
fancyTags(empty)
dialogueSentences157
tagDensity0.121
leniency0.242
rawRatio0
effectiveRatio0
89.7404%