Run: 2026-09-23-01M37HAEZ063KJ18TT9Y79P586

Run Details

Duration:
1m 8s
Cost:
$0.039084
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
Aurora carried the last delivery bag into the Raven’s Nest with rain running down the back of her coat. The green sign above the door painted the puddles at her feet. Inside, Silas had taken the stools off the tables, but half the lamps still burned, and a woman stood at the bar with both hands wrapped around a glass of water. “Kitchen’s closed,” Silas called. “Good thing this is for you.” Aurora set the bag beside his till. “Yu-Fei put in the extra chilli oil.” The woman turned at the sound of her voice. Her hair was cut close to her head. A thin white line crossed one eyebrow, and she wore a navy coat with an identification badge clipped inside the lapel. Aurora looked at the badge before she looked back at the face. “Eva?” Eva set down her water. She used to wave both arms when she spotted someone across a street, as if she were bringing a plane to ground. Now she kept her hands on the bar. “Rory. I thought you’d gone back to Cardiff.” “I live upstairs.” Eva glanced at the ceiling. “Of course you do.” Silas took the delivery bag. His gaze moved between them, then he carried the food to the far end of the bar. The limp in his left leg marked each step. He put two clean glasses within reach of the tap and left them there. Aurora pulled off her wet coat. Eva watched her fold it over a stool, watched the crescent scar on her wrist flash white beneath her sleeve. “How long has it been?” Eva asked. “Six years since we’ve stood in the same room.” “We saw each other at the station.” “You were on a train. I was on the platform.” “You ran alongside it.” “For about three seconds.” Eva touched the edge of her eyebrow. “Six years, then.” Aurora sat. The wood of the stool rocked beneath her, and she steadied it with her foot. At nineteen, Eva had painted a pair of gold wings on the shutter of an empty shop in Cardiff. She had persuaded Aurora to hold the paint tins while she worked. When the owner arrived, Eva had handed him the brush and asked whether he wanted to finish the second wing. For weeks afterward, she had greeted every person they passed as if they might be him. “Were you looking for me?” Aurora asked. “No. The rain got through my collar. I saw the sign.” Eva glanced towards the windows, where water blurred the street. “I had a course round the corner.” “In London?” “I’m back for two days. Work paid for a room near Euston.” “What work?” “Emergency housing. The council.” Aurora looked at the badge again. Eva turned it over, showing a photograph of herself with the same short hair and an expression that offered nothing to the camera. “You find people rooms?” “Some nights. Some nights I tell them there aren’t any. This week they’ve taught us a new way to record the second kind.” Silas opened a drawer and took out a clean towel. He placed it beside Aurora’s coat. “Your sleeve’s dripping on my floor.” “Then stop charging me rent.” “You’d miss having something to argue about.” He carried his food into the back room. Eva followed him with her eyes until the door shut. “He owns the place?” “And the flat. That’s Silas.” “You landed well.” Aurora rubbed rain from the table with the towel. “Eventually.” Eva looked down at her glass. “I know.” The last time Aurora had heard Eva’s voice, it had come through a phone pressed against her ear in a Cardiff bus shelter. Evan had been calling her for twenty minutes. Her suitcase sat between her shoes, its zip held shut by a length of string. Eva had given her the address of a vacant flat in London and the number for the woman who held its keys. Aurora had expected to find Eva there too. “You told me to come,” Aurora said. “Yes.” “I did.” “I know that as well.” “For the first week I kept thinking you’d knock on the door.” Eva slid a fingertip around the rim of the glass. She stopped when it made a sound. “I should’ve told you I wasn’t there.” “You sent messages.” “I sent an address and three messages about the boiler.” “You sent one asking if I’d eaten.” “Rory.” Aurora let the towel fall across her knees. Eva’s short hair exposed the shape of her ears, familiar from childhood, though Aurora could not remember when she had last noticed them. Back then Eva had cut holes in her school blazer pockets so she could pull sweets through the lining during lessons. She had once spent a whole Saturday persuading Aurora to board a train just to see the sea from a different town. “Where were you?” Aurora asked. “At home. Mum fell on the stairs. They thought she’d broken her hip.” “You could’ve told me.” “She hadn’t. It was her wrist. But at the hospital she couldn’t give them our address.” Eva lifted her glass and found it empty. “I stayed in Cardiff after that. Dad still went to work, and she kept letting people into the house because she thought they were my aunt.” Aurora pushed her own untouched glass towards Eva. Eva shook her head. “I rang you in March,” Aurora said. “I saw.” “You didn’t pick up.” “I was in a pharmacy with Mum. She’d emptied every packet in their display onto the floor. By the time we got home, you’d sent that message.” Aurora remembered the message. Don’t worry about it. I’ve found somewhere else. She had written it in the borrowed flat while a stranger’s clothes turned in the washing machine beside her. “I meant the room.” “I read it as you meant it.” Silas emerged from the back room with a plate in his hand. He stopped by the sink, scraped a few grains of rice into the bin and began to wash the plate. Behind him, maps covered the wall. Aurora found the Welsh coast on one of them, no wider than her thumb. “Is your mum all right now?” she asked. “She knows Dad most days. I go round before work and write down what she needs to eat. Yesterday she put my note in the fridge and the milk on the table.” “I didn’t know.” “You wouldn’t.” Eva took the badge off her coat and put it face down on the bar. One corner had worn pale where her thumb had rubbed it. “You still paint?” Aurora asked. Eva gave a short breath through her nose. “No. You still make lists of laws you’d change?” “I deliver food.” “That wasn’t what I asked.” “No.” Rain struck the window hard enough to turn a passing bus into a smear of red. Aurora remembered a notebook full of arguments written in the margins of her lecture notes, and Eva reading them aloud in a solemn courtroom voice until both of them laughed. She had thrown the notebook away when she left Cardiff. She could still hear Eva stumbling over the word *jurisdiction* on purpose. “You would’ve been good at this,” Aurora said, touching the badge. “Finding people a place.” “I give them the number of a hostel that’s full. Then I put that in a box on a form.” “You got me out.” “I gave you an address.” “It was enough to get on the bus.” Eva picked up her badge. She worked its pin through the coat lining, missed the hole, then took the coat off so she could see what she was doing. Her left sleeve had frayed at the cuff. Aurora caught hold of it before it dragged through a ring of water on the bar. “Your train?” Aurora asked. “Not till morning.” “Silas makes coffee if you ask him.” From the sink, Silas turned off the tap. “I heard that.” Eva looked at the coat caught in Aurora’s hand. “Does he make it as strong as you used to?” “Stronger. Mine always tasted of the spoon.” Eva eased the pin into place. Aurora let go of the sleeve, and Eva hung the coat over the next stool while Silas reached for the coffee tin.
created_at"2026-09-23T16:24:30.631Z" 9/23/2026, 4:24:30 PM
model"openai/gpt-6-sol-20260922"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency38065
moderation_latency(null)
generation_time68004
tokens_prompt1063
tokens_completion2052
native_tokens_prompt982
native_tokens_completion3712
native_tokens_completion_images0
native_tokens_reasoning2020
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"
usage0.039084
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.20.0; linux; x64))"
http_referer(null)
request_id"req-1790180670-fYjyiaSRxmQO2so3Erf0"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1790180670-OrlCzOYHxvjAHZUr5fkk"
upstream_id"resp_0925754a6c554885016ab3fd3ecd5887d1aafda4ab228b2d8d"
provider_responses
0
endpoint_id"1cfc7d9d-4404-4b8e-9ee4-58ae45c9dcd4"
id"resp_0925754a6c554885016ab3fd3ecd5887d1aafda4ab228b2d8d"
is_byokfalse
latency408
model_permaslug"openai/gpt-6-sol-20260922"
provider_name"OpenAI"
status200
total_cost0.039084
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
totalTags13
adverbTagCount0
adverbTags(empty)
dialogueSentences73
tagDensity0.178
leniency0.356
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1385
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)
89.17% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1385
totalAiIsms3
found
0
word"eyebrow"
count2
1
word"familiar"
count1
highlights
0"eyebrow"
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
narrationSentences81
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount1
narrationSentences81
filterMatches(empty)
hedgeMatches
0"began to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences141
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen32
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans1
markdownWords1
totalWords1385
ratio0.001
matches
0"jurisdiction"
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions12
unquotedAttributions0
matches(empty)
0.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions71
wordCount902
uniqueNames9
maxNameDensity3.1
worstName"Aurora"
maxWindowNameDensity5
worstWindowName"Aurora"
discoveredNames
Raven1
Nest1
Silas7
Aurora28
Eva28
Cardiff3
London1
Saturday1
Welsh1
persons
0"Silas"
1"Aurora"
2"Eva"
3"Saturday"
places
0"Raven"
1"Cardiff"
2"London"
3"Welsh"
globalScore0
windowScore0
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences62
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1385
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences141
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs89
mean15.56
std18.46
cv1.186
sampleLengths
062
14
220
39
441
51
635
78
83
99
1045
1126
127
139
147
1510
164
174
1810
1984
207
2128
222
2312
242
254
2629
274
2823
2916
306
315
327
3318
344
355
363
3710
388
3976
407
411
422
435
4412
4517
467
473
4810
497
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences81
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount2
totalVerbs152
matches
0"were bringing"
1"was doing"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences141
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount902
adjectiveStacks0
stackExamples(empty)
adverbCount14
adverbRatio0.015521064301552107
lyAdverbCount0
lyAdverbRatio0
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences141
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences141
mean9.82
std7.18
cv0.731
sampleLengths
019
112
231
34
413
57
69
78
821
912
101
115
1222
138
148
153
165
174
185
1917
209
2114
226
2320
247
259
267
2710
284
294
307
313
322
3315
3419
3512
3620
3716
387
3921
407
412
4212
432
444
456
4623
474
4823
4910
54.85% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats6
diversityRatio0.3546099290780142
totalSentences141
uniqueOpeners50
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences71
matches(empty)
ratio0
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount17
totalSentences71
matches
0"Her hair was cut close"
1"She used to wave both"
2"His gaze moved between them,"
3"He put two clean glasses"
4"She had persuaded Aurora to"
5"He placed it beside Aurora’s"
6"He carried his food into"
7"Her suitcase sat between her"
8"She stopped when it made"
9"She had once spent a"
10"I’ve found somewhere else."
11"She had written it in"
12"He stopped by the sink,"
13"She had thrown the notebook"
14"She could still hear Eva"
15"She worked its pin through"
16"Her left sleeve had frayed"
ratio0.239
30.42% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount61
totalSentences71
matches
0"Aurora carried the last delivery"
1"The green sign above the"
2"Aurora set the bag beside"
3"The woman turned at the"
4"Her hair was cut close"
5"A thin white line crossed"
6"Aurora looked at the badge"
7"Eva set down her water."
8"She used to wave both"
9"Eva glanced at the ceiling."
10"Silas took the delivery bag."
11"His gaze moved between them,"
12"The limp in his left"
13"He put two clean glasses"
14"Aurora pulled off her wet"
15"Eva watched her fold it"
16"Eva touched the edge of"
17"The wood of the stool"
18"She had persuaded Aurora to"
19"Eva glanced towards the windows,"
ratio0.859
70.42% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount1
totalSentences71
matches
0"Now she kept her hands"
ratio0.014
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences37
technicalSentenceCount2
matches
0"For weeks afterward, she had greeted every person they passed as if they might be him."
1"Eva turned it over, showing a photograph of herself with the same short hair and an expression that offered nothing to the camera."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags13
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags10
fancyCount0
fancyTags(empty)
dialogueSentences73
tagDensity0.137
leniency0.274
rawRatio0
effectiveRatio0
88.1620%