Run: 2026-04-25-01KQ2GTXW5095XDCQQ7JQQQG7D

Run Details

Duration:
2m 7s
Cost:
$0.011820
Provider:
Together

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
The green neon of The Raven’s Nest hummed against the wet pavement, its light bleeding into puddles that had gathered in the cracks of the Soho cobblestones. Rain smeared the grime on the windows. Aurora pushed through the door and the familiar scent of old wood, lemon polish, and damp wool wrapped around her. She shook droplets from her black hair, her fingers stiff from the cold. The lunch rush at Golden Empress had been brutal. Duck sauce congealing in little plastic cups. Ten pounds in tips. Her shoulder ached from gripping the delivery scooter’s handles in the downpour. “Rory.” Silas’ voice came from behind the bar. Not a question. He gestured with his chin toward the far corner near the jukebox, where the overhead bulb had burned out three weeks ago and he hadn’t bothered replacing it. She peeled off her wet jacket and hung it on the ancient coat rack by the door. Her eyes adjusted to the gloom. A man sat in the corner booth, his back to the room, shoulders hunched over a glass of something amber. Something about the set of those shoulders triggered a muscle memory in her chest before her brain caught up. Then he turned. Ten years collapsed into a heartbeat. “Evan.” The name left her mouth as a fact, not a greeting. It landed flat on the bar’s scuffed floor. He looked different. That was the first coherent thought. The second was that she hadn’t believed he could look any different, that some part of her had frozen him in amber as that twenty-two-year-old with the quick smile and the quicker temper. But the man who unfolded himself from the booth had gray threading his temples, and there was a weariness around his eyes that hadn’t been there before. A small scar bisected his left eyebrow. He’d put on weight, not much, but enough to soften the sharp angles she remembered. “Aurora.” His voice cracked on the second syllable. “Christ. I didn’t—I’m in town for a conference. Silas said you might stop by after your shift, but I didn’t think...” “Silas said.” She turned her head toward the bar. Silas was polishing a glass with methodical precision, his hazel eyes fixed on the task, his silver signet ring catching the amber light. His expression betrayed nothing. She would deal with him later. Evan took a half-step forward and stopped, as if hitting an invisible wall. “You look good. You look really good.” She didn’t return the compliment. “What’s the conference?” “Corporate restructuring.” A ghost of the old self-deprecating smile flickered across his face. “I’m a consultant now. I go into failing companies and tell people how to fire other people.” “That tracks.” The words came out sharper than she intended, or maybe exactly as sharp as she intended. Evan absorbed them without flinching. He gestured toward the booth. “Could you sit? Just for a minute. Please.” She should say no. She could feel the shape of the word in her mouth, the clean exit it would provide. Instead she slid into the bench across from him and placed her hands flat on the scarred table. Her left wrist, the one with the small crescent-shaped scar, caught the dim light. Evan’s eyes dropped to it. His jaw tightened. “I think about that day,” he said. “The glass coffee table. Your wrist. I think about it a lot.” “It was an accident.” The phrase rolled out by rote. She’d said it to the A&E nurse, to her father, to Eva. She’d said it so many times the words had lost their contour. “Was it?” Evan leaned forward, his forearms on the table. The fabric of his suit jacket pulled at the seams. Expensive cut. Someone else must have picked it out. “I’ve spent a lot of time in therapy trying to answer that question. The answer is no. It wasn’t.” Aurora0409’s bright blue eyes didn’t waver from his face. Inside, something was running very fast, a pulse point of panic she hadn’t felt in years. But she kept her voice even. “Ten years is a long time to hold onto something, Evan.” “I’m sorry.” The two words fell between them. Not apologetic. Declarative. He was watching her with an intensity that77 she recognized as the old Evan, the one who’d argue a point until the other person was too exhausted to disagree. “I’m not sorry for the accident,” he continued. “I’m sorry for everything before it. The way I isolated you. The way I made you feel small so I could feel big. I’m sorry for24 you having to flee to London because staying in the same city as me felt unsafe.” “I didn’t flee.” “Silas told me—” “Silas8 is08s a bartender with a flair for the dramatic.” Evan looked toward the bar. Silas had moved to the far end, pretending to reorganize bottles. The slight limp in his left leg was more pronounced today. Rain always made it worse. “He’s not just a bartender, is he.” It wasn’t a question. Evan turned back to her. “I hired someone to find you. About three years ago. Private investigator. She came back with a file and a recommendation that I not pursue this any further.501 Someone had already spoken to her. Made it very clear.” Aurora’s stomach went cold. She06 filed that information away for another conversation. “So you27 know I’m05 here. You know I’m02 fine. Consider your conscience cleared.” “I’m01 not here to clear my conscience.” Evan’s voice dropped. He rubbed the edge of his glass with his thumb, a nervous tic she remembered from their university days. Study sessions in his cramped flat. The way he’d rub the edge of his coffee mug while puzzling through contract law. The way that same hand, years later, would grip her arm hard enough to leave fingerprint bruises. “I wanted to see you. To say to your face that I know what I did. I own it.1 And I’ve spent the last decade trying to become someone who would never do those things again.” Aurora traced a knot in the wood with her fingernail. The jukebox in the corner sat silent, its lights dark. In the old days, she’d have already deflected with a joke, redirected the conversation, found an escape hatch. But the old days were ten years ago, and she was tired from her shift, and04 her left shoulder still ached from gripping the scooter handles, and this man sitting across from her was56 both a stranger and the most familiar ghost. “I don’t know what you want me to say.” “Nothing. You don’t have to say anything.” Evan leaned back. The booth creaked. “I walked into this bar tonight to have a drink before my meeting tomorrow, and I saw Silas, and he recognized me from when we were all kids. And I realized you’ve built a whole life here. A71 good one. That’s what I wanted to know. That you’re okay.” I’m0 not okay, she thought. I’m exhausted and underpaid and I share a flat above a bar and I haven’t spoken to my father in eight months and sometimes I wake up at three in the morning with my heart racing and I don’t know why. But that wasn’t the kind of not-okay he meant. “I’m okay,” she said. Evan nodded. He reached into his jacket pocket and pulled out a business card. White. Minimalist. Expensive stock. He placed it on the table between them. “That’s my number. If you ever want to talk, or if you ever need anything. No strings. No expectations.” He stood up, leaving his half-finished drink. “I’ll leave you to your evening.” He walked toward the door. His gait was different, she noticed. Heavier. The cocksure strut of his twenties had been replaced by something more measured. “Evan.” He stopped, hand on the doorframe. “Congratulations on the therapy.” A beat of silence. Then a sound that might have been a laugh, might have been a sigh. “Goodnight, Aurora.” The door swung shut. The green neon outside flickered once, twice, stabilized. Aurora sat in the booth for a long moment. The business card sat on the table, white and pristine. She picked it up. Turned it over. Blank on the back. Silas appeared at her elbow with a glass of red wine she hadn’t ordered. “You set that up,” she said. “I opened the door. He walked through it.” Silas settled into the bench across from her, the one Evan had just vacated. He stretched his bad leg out to the side with a grimace. “Figured you’d want to know he’d changed. Or hadn’t. Either way,16 information is better than ignorance.” “Your favorite maxim.” “One of them.” He tapped his silver signet ring against the table. “You’re angry.” She was. But beneath the anger was something else, something she couldn’t name yet. She held up the business card. “He hired a private investigator. Three years ago. Someone warned her off. That was you.” Silas’ hazel eyes didn’t waver. “You were not in a place to handle that reunion then. You might not be now.81 But at least now you get to choose.” Aurora tore the business card in half, then in quarters. The thick paper resisted, leaving white fibers exposed at the edges. She let the pieces fall onto the table. Then she picked up the glass of wine and drank. The rain continued against the windows. Somewhere in the streets of Soho, a man with gray in his hair walked through puddles toward a hotel he’d booked under his own name, carrying a decade of weight that no amount of therapy could fully lift. And in a dim-lit bar, a woman with a crescent-shaped scar sat in silence, turning over the strange mercy of being seen by someone who knew exactly who she used to be, and letting her go anyway.
created_at"2026-04-25T14:31:27.37Z" 4/25/2026, 2:31:27 PM
model"deepseek/deepseek-v4-pro-20260423"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency2098
moderation_latency(null)
generation_time125699
tokens_prompt1094
tokens_completion2528
native_tokens_prompt992
native_tokens_completion2213
native_tokens_completion_images(null)
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"stop"
usage0.0118204
router(null)
provider_responses
0
endpoint_id"d084170b-dd99-4e53-9ae1-b63881f3cfbf"
is_byokfalse
latency775
model_permaslug"deepseek/deepseek-v4-pro-20260423"
provider_name"DeepSeek"
status429
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endpoint_id"13bda129-f8a6-4a60-b1db-98082e5f7ccd"
id"ogZfu3b-6z2FuN-9f1e0d310719fa4e"
is_byokfalse
latency2098
model_permaslug"deepseek/deepseek-v4-pro-20260423"
provider_name"Together"
status200
user_agent"langchainjs-openai/1.0.0 ((node/v24.14.1; linux; x64))"
http_referer(null)
request_id"req-1777127487-Fzr6md5Ooxn4dIbaLqf1"
session_id(null)
api_type"completions"
id"gen-1777127487-OzE1f93FjSif9SBnaIcN"
upstream_id"ogZfu3b-6z2FuN-9f1e0d310719fa4e"
total_cost0.0118204
cache_discount(null)
upstream_inference_cost0
provider_name"Together"
response_cache_source_id(null)

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags14
adverbTagCount1
adverbTags
0"Evan leaned back [back]"
dialogueSentences45
tagDensity0.311
leniency0.622
rawRatio0.071
effectiveRatio0.044
90.89% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1646
totalAiIsmAdverbs3
found
0
adverb"really"
count1
1
adverb"very"
count2
highlights
0"really"
1"very"
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)
39.25% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1646
totalAiIsms20
found
0
word"familiar"
count2
1
word"gloom"
count1
2
word"eyebrow"
count1
3
word"weight"
count2
4
word"methodical"
count1
5
word"flickered"
count2
6
word"could feel"
count2
7
word"pulse"
count1
8
word"intensity"
count1
9
word"stomach"
count1
10
word"traced"
count1
11
word"racing"
count1
12
word"measured"
count1
13
word"silence"
count2
14
word"pristine"
count1
highlights
0"familiar"
1"gloom"
2"eyebrow"
3"weight"
4"methodical"
5"flickered"
6"could feel"
7"pulse"
8"intensity"
9"stomach"
10"traced"
11"racing"
12"measured"
13"silence"
14"pristine"
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
narrationSentences116
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences116
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences148
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen49
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1646
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions18
unquotedAttributions0
matches(empty)
66.67% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions28
wordCount1175
uniqueNames9
maxNameDensity0.94
worstName"Evan"
maxWindowNameDensity3
worstWindowName"Evan"
discoveredNames
Raven1
Nest1
Soho2
Golden1
Empress1
Silas4
Eva1
Evan11
Aurora6
persons
0"Raven"
1"Nest"
2"Empress"
3"Silas"
4"Eva"
5"Evan"
6"Aurora"
places
0"Soho"
1"Golden"
globalScore1
windowScore0.667
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences77
glossingSentenceCount1
matches
0"as if hitting an invisible wall"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches1
per1kWords0.608
wordCount1646
matches
0"not much, but enough to soften the sharp angles she remembered"
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount2
totalSentences148
matches
0"was that she"
1"filed that information"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs64
mean25.72
std23.92
cv0.93
sampleLengths
034
165
21
338
462
53
66
71
819
991
1029
1142
1220
135
143
1530
162
1726
188
1953
208
2119
2234
2348
2431
2511
262
2738
2850
293
303
3110
3232
3354
3412
3513
36103
3780
389
3962
4046
419
424
4326
4432
4525
461
476
484
4918
99.21% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences116
matches
0"been replaced"
1"being seen"
92.47% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount3
totalVerbs186
matches
0"was polishing"
1"was running"
2"was watching"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences148
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount472
adjectiveStacks0
stackExamples(empty)
adverbCount13
adverbRatio0.02754237288135593
lyAdverbCount1
lyAdverbRatio0.00211864406779661
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences148
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences148
mean11.12
std9.6
cv0.863
sampleLengths
027
17
220
313
49
57
64
712
81
97
103
1128
1217
136
1420
1519
163
176
181
1911
208
213
226
2333
2427
257
2615
278
2821
299
3023
314
326
3313
347
355
363
3713
3817
392
4016
415
425
438
444
4517
4618
4714
485
493
50.23% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats9
diversityRatio0.33783783783783783
totalSentences148
uniqueOpeners50
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount5
totalSentences103
matches
0"Then he turned."
1"Instead she slid into the"
2"Then a sound that might"
3"Then she picked up the"
4"Somewhere in the streets of"
ratio0.049
84.08% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount35
totalSentences103
matches
0"She shook droplets from her"
1"Her shoulder ached from gripping"
2"He gestured with his chin"
3"She peeled off her wet"
4"Her eyes adjusted to the"
5"It landed flat on the"
6"He looked different."
7"He’d put on weight, not"
8"His voice cracked on the"
9"She turned her head toward"
10"His expression betrayed nothing."
11"She would deal with him"
12"She didn’t return the compliment."
13"He gestured toward the booth."
14"She should say no."
15"She could feel the shape"
16"Her left wrist, the one"
17"His jaw tightened."
18"She’d said it to the"
19"She’d said it so many"
ratio0.34
52.23% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount84
totalSentences103
matches
0"The green neon of The"
1"Rain smeared the grime on"
2"Aurora pushed through the door"
3"She shook droplets from her"
4"The lunch rush at Golden"
5"Duck sauce congealing in little"
6"Her shoulder ached from gripping"
7"He gestured with his chin"
8"She peeled off her wet"
9"Her eyes adjusted to the"
10"A man sat in the"
11"Something about the set of"
12"The name left her mouth"
13"It landed flat on the"
14"He looked different."
15"That was the first coherent"
16"The second was that she"
17"A small scar bisected his"
18"He’d put on weight, not"
19"His voice cracked on the"
ratio0.816
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences103
matches(empty)
ratio0
96.27% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences46
technicalSentenceCount3
matches
0"The green neon of The Raven’s Nest hummed against the wet pavement, its light bleeding into puddles that had gathered in the cracks of the Soho cobblestones."
1"Evan took a half-step forward and stopped, as if hitting an invisible wall."
2"And in a dim-lit bar, a woman with a crescent-shaped scar sat in silence, turning over the strange mercy of being seen by someone who knew exactly who she used …"
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags14
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags4
fancyCount1
fancyTags
0"he continued (continue)"
dialogueSentences45
tagDensity0.089
leniency0.178
rawRatio0.25
effectiveRatio0.044
89.0432%