Run: 2026-09-02-01M1HGME1YQ64V0FA8SEF14VX8

Run Details

Duration:
1m 57s
Cost:
$0.003543
Provider:
Alibaba

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 had flattened the street outside the Raven's Nest into black glass. The green neon sign above the door bled into the puddles and shook there whenever a bus pushed past. Aurora pulled off her delivery hood and let the rain take the rest. Inside, the bar sat between closing time and a secret. Old maps curled on the dark wood. Black-and-white photographs lined the shelves: men in coats, women in hats, faces that looked as if they had learned how to wait. Silas stood behind the bar with a cloth over one shoulder. His left foot rested at a cautious angle. The silver ring on his right hand flashed as he turned a glass. “You smell like the river,” he said. “I took the shortcut by the alley.” “You took it because Yu-Fei made you leave through the back again.” Aurora dropped her satchel by the stairs. Her flat above the bar had no separate key for the pub entrance, which suited her when she arrived late and wanted to pretend the night had only asked her for a few parcels. She looked across the room because Silas had gone quiet. A woman sat alone in the far booth, facing the door. Her coat was charcoal and dry at the shoulders, though the rain had been falling hard outside. Her hair, once long and tangled with auburn highlights, had been cut to the jaw. A thin pale line ran from her left temple into the skin behind her ear, visible only where the overhead lamp caught the scar. “Eva?” The woman lifted her face. Bright, guarded eyes. The mouth Aurora remembered from school corridors and shared bedsheets formed a small, uncertain shape before it became something practical. “Hello, Rory.” Aurora crossed the floor. Her boots left wet prints on the worn boards. “I thought you were in Lisbon.” “You thought wrong.” “I thought you wrote it on a card with a picture of a tram.” “That was Berlin.” Aurora sat opposite her. The vinyl booth squeaked. For the first time since Evan had made her leave Cardiff, she felt the strange pressure of a door closing behind her without warning. “Seven years,” she said. Eva moved her glass half an inch. It was empty. “Eight, if you count the part where you didn’t speak to me after the funeral.” “My mother didn’t send me a card.” “She sent you a card. You didn’t open it.” Aurora’s hand stopped over the crescent scar on her left wrist. The scar had always been there, small and silvered, from the night she had fallen against a radiator at seven. Eva used to trace it when they were drunk and sentimental. “You look different,” Aurora said. “You look like your landlord’s bar.” “That’s not what I mean.” “Then don’t mean it.” Silas appeared with two mugs. He set one near Eva. “Tea,” he said. “No milk. Too hot.” Aurora looked at him. Eva wrapped both hands around the mug and waited for it to cool. “You still hold the cup like it owes you money.” “You still check the exits.” “I learned from better people than you.” Aurora laughed once. The sound came out harder than she wanted. “Who taught you to sit like that?” “Survival.” “You used to fall asleep on trams.” “I used to have a lot of bad habits.” Eva’s fingers were pale, trimmed, unfamiliar. The small ink stain Aurora remembered from the inside of her thumb was gone. Her nails had been cleaned and cut short. A watch sat at her wrist, low and plain, with a band too worn to advertise the price. “You work for him,” Aurora said. “For whom?” “Don’t.” Eva drank from the mug. Her throat moved. “I do things that pay.” “That’s not work. That’s a riddle.” “You left London before riddles became your job.” “I left because Evan would have broken my hand for saying hello to a man at the corner shop.” “I know.” “You don’t know.” Eva set the mug down. “I know he kept your coat by the door even after you took it. I know you left him a letter and didn’t wait for the reply. I know you cried in a taxi on Charlotte Street because you thought you were being dramatic and not because you were scared.” Aurora’s jaw tightened. “Is that what changed?” she asked. “You started collecting details?” “It helped me get out.” “Out of where? Cardiff? We grew up in the same square.” “Out of needing someone to notice me leave.” Silas had moved away. Eva turned her eyes to the bookshelf behind him. The leather spines seemed ordinary until she tilted her head, counting them like steps. “Did you come through there?” Aurora asked. Eva didn’t answer. “Did you?” “Does it matter?” “It does if you used the front like a guest and then went behind walls like you own them.” “I didn’t come to be invited.” “You never did.” Eva looked at her. For a second, the new carefulness slipped, and Aurora saw the girl who had packed sandwiches into her schoolbag and told her to eat before the prefects saw. Then the surface hardened again. “You’re still angry at me for not coming to Paddington,” Eva said. “I’m angry at you for not answering.” “I was scared.” “You scared easy.” “You were brave. Everyone said you were brave, so I thought bravery meant never asking for help. I thought if I wrote back, you’d ask me to come. If I came, you’d stay. If you stayed, they’d find you.” “They found me in Cardiff anyway.” Eva’s hand curled around the mug. Steam rose between them. “I’m sorry.” “Don’t do that.” “Do what?” “Say it like you bought it.” Eva’s mouth tightened. “I’m learning.” “What are you learning? How to disappear in a room that’s already full of ghosts?” “How not to be a ghost first.” The neon above the door pulsed. Light swept over Eva’s temple scar and disappeared. Aurora remembered Eva at fifteen with blood on her knee and a stolen bicycle leaning against the wall behind her, laughing because the police couldn’t catch her without losing a tire. “When did you get cut?” Aurora asked. “Who?” “You have a scar.” Eva touched the line as if checking whether it had returned to the wrong side of her face. “I fell.” “Where?” “London.” “You mean here?” Eva looked past her, toward the bar. “I mean the city takes what it needs.” “That’s Silas talk.” “He used to talk to you.” “He talked to me because you asked him to.” Eva said nothing. Aurora leaned forward. “You set me up. You had me delivered parcels for Yu-Fei so I would see Soho without seeing you. You kept tabs on me through the flat because you couldn’t keep your promise and didn’t have the nerve to tell me.” “I told you to come.” “You sent a postcard with no signature.” “That was the point.” “That was cowardice dressed up like kindness.” Eva’s eyes flickered. She looked at the empty street outside the window, the dark row of cars, the black mouth of the alley beside the pub. “I have been called worse by people who are paid for accuracy.” Aurora’s hand fell flat on the table. “Don’t quote your handlers.” “My handlers think I am inefficient.” “Good.” “They also think I care too much about things with no value.” “I’m no value?” “You were the reason I had a value.” Silence moved between them. In it, Aurora heard the kitchen fan above the back room, the tap dripping in the corner, the old floorboards complaining under Silas’s cautious steps. Eva said, “You look well. Not happy. Well.” “Thanks.” “You’ve stopped apologizing with your shoulders.” “And you’ve started standing like a person expecting a knife.” Eva almost smiled. The expression broke halfway across her face and failed. “I didn’t think you’d notice,” Eva said. “I notice everything. It’s why I was bad at law school.” “Why you were good at running.” “Why you were good at leaving.” Eva took the mug and drank, though the tea had cooled. She looked into it as if there were more answers printed at the bottom. “I’m not leaving tonight.” “That’s the first thing you said that makes sense.” “I’m here because there was a name.” Aurora’s hand tightened around her mug. “Whose name?” “Not here.” Eva turned her head toward the staircase. The old photographs stared from the wall. Silas had gone into the back room and pulled the shelf closed behind him. The space was no longer a bar. It had become the inside of something larger and less forgiving. “You’re using me.” “I’m asking you not to look away when you find out I did something.” “Find out what?” Eva’s eyes remained on Aurora. They were grey-brown under the neon and carried a weariness that had outlasted youth, distance, and every friendly joke they had ever relied on to soften the truth. “About Prague.” Aurora set her mug down so carefully that the clay made no sound.
created_at"2026-09-02T16:53:40.835Z" 9/2/2026, 4:53:40 PM
model"qwen/qwen3.8-flash-20260826"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency3935
moderation_latency(null)
generation_time105478
tokens_prompt1063
tokens_completion8109
native_tokens_prompt1098
native_tokens_completion7480
native_tokens_completion_images(null)
native_tokens_reasoning5415
native_tokens_cached1024
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"stop"
service_tier(null)
usage0.003543084
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.19.0; linux; x64))"
http_referer(null)
request_id"req-1788368020-JE5cksVd1VvwAWyMkdEW"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1788368020-BX061hRO3UMn9UkPciN6"
upstream_id"chatcmpl-64c0873e-164c-9c45-8f84-b4c4b3aad9a9"
provider_responses
0
endpoint_id"84b1e4a7-5aed-4464-818f-1994f0b4ee18"
id"chatcmpl-64c0873e-164c-9c45-8f84-b4c4b3aad9a9"
is_byokfalse
latency3935
model_permaslug"qwen/qwen3.8-flash-20260826"
provider_name"Alibaba"
status200
total_cost0.003543084
cache_discount0.000137216
upstream_inference_cost0
provider_name"Alibaba"
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
totalTags11
adverbTagCount0
adverbTags(empty)
dialogueSentences100
tagDensity0.11
leniency0.22
rawRatio0
effectiveRatio0
96.70% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1513
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)
90.09% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1513
totalAiIsms3
found
0
word"pulsed"
count1
1
word"flickered"
count1
2
word"silence"
count1
highlights
0"pulsed"
1"flickered"
2"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
narrationSentences89
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences89
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences178
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen50
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1513
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions24
unquotedAttributions0
matches(empty)
0.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions57
wordCount833
uniqueNames7
maxNameDensity3
worstName"Eva"
maxWindowNameDensity7
worstWindowName"Eva"
discoveredNames
Raven1
Nest1
Aurora22
Silas6
Evan1
Cardiff1
Eva25
persons
0"Aurora"
1"Silas"
2"Evan"
3"Eva"
places
0"Raven"
1"Cardiff"
globalScore0
windowScore0
53.85% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences52
glossingSentenceCount2
matches
0"seemed ordinary until she tilted her head, counting them like steps"
1"as if checking whether it had returned to the wrong side of her face"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1513
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences178
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs120
mean12.61
std14.28
cv1.132
sampleLengths
044
171
27
37
412
541
610
767
81
928
102
1119
123
1314
143
1532
164
1725
187
199
2042
215
226
235
244
2510
267
274
2823
295
307
3118
321
337
349
3546
366
372
381
3913
406
418
4219
432
443
4555
463
4710
485
4911
97.38% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences89
matches
0"was gone"
1"been cleaned"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs154
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences178
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount836
adjectiveStacks0
stackExamples(empty)
adverbCount21
adverbRatio0.025119617224880382
lyAdverbCount4
lyAdverbRatio0.004784688995215311
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences178
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences178
mean8.5
std7.66
cv0.901
sampleLengths
012
119
213
310
47
522
611
78
813
97
107
1112
127
1334
1410
1511
1617
1715
1824
191
205
213
2220
232
244
259
266
273
2814
293
304
314
3224
334
347
353
3615
377
389
3911
4020
4111
425
436
445
454
465
475
483
494
43.82% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats11
diversityRatio0.29775280898876405
totalSentences178
uniqueOpeners53
85.47% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences78
matches
0"Bright, guarded eyes."
1"Then the surface hardened again."
ratio0.026
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount14
totalSentences78
matches
0"His left foot rested at"
1"Her flat above the bar"
2"She looked across the room"
3"Her coat was charcoal and"
4"Her hair, once long and"
5"Her boots left wet prints"
6"It was empty."
7"He set one near Eva."
8"Her nails had been cleaned"
9"Her throat moved."
10"She looked at the empty"
11"She looked into it as"
12"It had become the inside"
13"They were grey-brown under the"
ratio0.179
11.28% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount70
totalSentences78
matches
0"Rain had flattened the street"
1"The green neon sign above"
2"Aurora pulled off her delivery"
3"Silas stood behind the bar"
4"His left foot rested at"
5"The silver ring on his"
6"Aurora dropped her satchel by"
7"Her flat above the bar"
8"She looked across the room"
9"A woman sat alone in"
10"Her coat was charcoal and"
11"Her hair, once long and"
12"A thin pale line ran"
13"The woman lifted her face."
14"The mouth Aurora remembered from"
15"Aurora crossed the floor."
16"Her boots left wet prints"
17"Aurora sat opposite her."
18"The vinyl booth squeaked."
19"Eva moved her glass half"
ratio0.897
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences78
matches(empty)
ratio0
77.92% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences33
technicalSentenceCount3
matches
0"Black-and-white photographs lined the shelves: men in coats, women in hats, faces that looked as if they had learned how to wait."
1"For a second, the new carefulness slipped, and Aurora saw the girl who had packed sandwiches into her schoolbag and told her to eat before the prefects saw."
2"They were grey-brown under the neon and carried a weariness that had outlasted youth, distance, and every friendly joke they had ever relied on to soften the tr…"
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags11
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags11
fancyCount0
fancyTags(empty)
dialogueSentences100
tagDensity0.11
leniency0.22
rawRatio0
effectiveRatio0
85.2167%