Run: 2026-04-25-01KQ2FP6DHJWXEHGHBTYXVYR24

Run Details

Duration:
23.8s
Cost:
$0.000534
Provider:
DeepSeek

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="4415" name="Lucien Moreau" aliases="Lucien, Luc, the Frenchman" pronouns="he/him" species="Half-Demon" storyRole="Ally"> <eyeColor>heterochromatic — one amber, one black</eyeColor> <hair>slicked-back platinum blond</hair> <height>5'11"</height> <age>32</age> <cane>ivory-handled cane that conceals a thin blade</cane> <dress>tailored charcoal suits</dress> <description>An impeccably dressed fixer and information broker in London's supernatural underworld. He speaks four languages fluently.</description> <backstory>Born in Marseille to a human mother and a demon father from the realm of Avaros.</backstory> </character> <location id="858" name="Eva's Flat" aliases="Eva's place, the flat on Brick Lane" realm="Earth"> <cat>tabby cat named Ptolemy</cat> <security>three deadbolts on the front door</security> <description>A cramped one-bedroom flat above a curry house on Brick Lane, East London. Every surface is covered with books, scrolls, and research notes.</description> </location> </codex> Write a fully fleshed out scene of at least 1000 words in the romance genre. Two people who parted on complicated terms are forced together again when one shows up unannounced at the other's flat. There's history between them — attraction, hurt, and things left unsaid. Write the scene from the moment the door opens. 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
The rain had followed Rory all the way from Whitechapel, plastering her black hair to her scalp and seeping through the shoulders of her jacket. She took the stairs to Eva’s flat two at a time, eager for the warmth of the place, for the familiar chaos of books and the smell of cardamom drifting up from the curry house below. She shoved the key into the first deadbolt, then the second. The third turned with a reluctant grind. The door swung open. The flat was wrong. The air was wrong. Not just the silence — though Ptolemy usually greeted her with a chirp and a figure-eight around her ankles — but the quality of the stillness. The sort of stillness that meant someone was holding their breath. Rory’s hand went to the knife in her jacket pocket. Then she saw him. Lucien Moreau sat in the worn armchair by the window, one leg crossed over the other, his platinum blond hair slicked back even in the dim grey light. The ivory handle of his cane rested between his knees, his long fingers draped over it like he had all the time in the world. He looked like a funeral director who’d wandered into a jumble sale. “Laila,” he said, and the old name from last spring settled in her chest like a splinter. “Get out.” He didn’t move. His lips curved — just barely, just enough to show he’d heard her. “Eva let me in. She’s at the British Library until closing.” Rory stepped inside and shut the door behind her, keeping it close, keeping her hand on the knife. “That doesn’t answer the question.” “I came to warn you.” “Warn me.” She laughed, a short, hollow sound. “You don’t get to show up in my friend’s flat and warn me. You don’t get to do anything, Lucien. We settled that.” He watched her with those mismatched eyes — one amber, one black as coal dust. The left one had changed since she’d known him. It was darker now, the pupil bleeding into the iris like ink into water. She wondered what that meant. She wondered if she cared. “Sit down, Rory.” “Don’t call me that.” “Aurora, then.” He said it like a concession, soft around the edges. “Please.” She didn’t sit. She crossed her arms and leaned against the doorframe, letting the rain drip from her sleeves onto the worn floorboards. The flat smelled of him — sandalwood and cigarette smoke and something metallic. Old blood, maybe. He always carried that scent after a job. “I have two minutes,” she said. “You have as long as it takes.” He rose from the chair with the careful precision of a man who knew exactly how much space he occupied, and then he closed the distance between them. Not fast, not threatening. Just deliberate. Rory held her ground. She’d learned to. He stopped an arm’s length away. Close enough that she could see the fine lines at the corners of his eyes, the way his jaw tightened when he was working up to something he didn’t want to say. “You’ve been seen,” he said. “I’m a delivery driver. I’m seen every day.” “Not by the delivery customers. By someone else. Someone who remembers you from the Verlaine job.” Her stomach dropped. She kept her face still. “That was six months ago.” “And they have long memories, and longer reach.” His voice dropped, and something shifted in his expression — something that looked like the thing she’d pretended not to see the night she walked out of his flat in Soho. “You burned them, Rory. You burned them and you ran, and they want to know who taught you how to use a silver blade like that.” “I taught myself.” “And they don’t believe that. So they’re asking questions. I’ve been tracking their movements for two weeks. They’re circling closer.” She wanted to call him a liar. She wanted to believe he’d invented this, cooked it up as a reason to stand in Eva’s flat and look at her with that heavy, hungry gaze. But Lucien didn’t lie about danger. He was too practical for that. “Why do you care?” she asked. He tilted his head. The amber eye caught the grey light, and for a moment it looked almost gold. “You know why.” “I told you not to.” “I know.” “I meant it.” “I know that too.” He took a breath. She could see him making a decision, could see the exact moment he lost a war with himself. “I’m not here to pick up where we left off. I’m here because you’re alive, and I’d like you to stay that way.” The words lodged somewhere in her throat. She had to look away. The rain drummed against the window. Ptolemy meowed from somewhere under the sofa. Normal sounds. Safe sounds. She focused on them until her heart stopped trying to climb out of her chest. “So what’s your plan?” she said. “Hide me somewhere? Ship me back to Cardiff?” “No. You’d hate Cardiff.” She almost smiled. Almost. “Teach you,” he said. “I’ll teach you to handle yourself properly. Not just silver and quick exits. Real fighting. Real control. The kind you’d need if they put a blade to your throat.” “And if I say no?” “Then I’ll leave. I’ll stay away. I’ll keep your secrets until I die.” She didn’t doubt that. For all his sharp suits and sharper tongue, Lucien had never broken a promise to her. He’d broken her heart, sure — but that had been her fault. She’d known what he was. Half-demon. Fixer. A man who couldn’t afford to want anything too badly. But he’d wanted her. That much had been real. She looked at him now, standing in her friend’s messy flat with rain in his hair and blood on his hands, and she remembered what it had felt like to be seen by him. Not just wanted — seen. Like she was the only interesting thing in a city that bored him. “One session,” she said. His brows lifted. “One session. If you’re lying, or if this is some elaborate way to get me alone, I’ll put this knife through your ribcage.” He smiled then. A real one. Small and crooked and devastating. “I’d expect nothing less.” He turned toward the door, and for a moment she thought he would leave, just walk out into the rain and disappear. But he paused with his hand on the deadbolt. “Tomorrow evening. Nine o’clock. I’ll send the address.” “I don’t have your number anymore.” He reached into his jacket and pulled out a card — cream-coloured, embossed with a single phone number. He held it out to her. She took it. Their fingers brushed. His were cold. “Don’t be late, Aurora.” He was gone before she could answer, the door clicking shut behind him. The flat felt suddenly smaller, emptier. She stood there with the card in her hand and the rain in her ears, and she tried to remember how to breathe the way she had before he walked in.
created_at"2026-04-25T14:11:23.701Z" 4/25/2026, 2:11:23 PM
model"deepseek/deepseek-v4-flash-20260423"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency694
moderation_latency(null)
generation_time23716
tokens_prompt808
tokens_completion1820
native_tokens_prompt763
native_tokens_completion1578
native_tokens_completion_images(null)
native_tokens_reasoning0
native_tokens_cached128
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"
usage0.000534324
router(null)
provider_responses
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endpoint_id"722e0746-2034-4d6e-a08f-e6271849ee9e"
id"bc13c0ed-18d1-4d0c-8da6-5ad8d70399a6"
is_byokfalse
latency280
model_permaslug"deepseek/deepseek-v4-flash-20260423"
provider_name"DeepSeek"
status200
user_agent"langchainjs-openai/1.0.0 ((node/v24.14.1; linux; x64))"
http_referer(null)
request_id"req-1777126283-imEV6cbMMCv2gyog9C4t"
session_id(null)
api_type"completions"
id"gen-1777126283-PU7tbqRWjj0herYERSDp"
upstream_id"bc13c0ed-18d1-4d0c-8da6-5ad8d70399a6"
total_cost0.000534324
cache_discount0.000014336
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provider_name"DeepSeek"
response_cache_source_id(null)

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags12
adverbTagCount0
adverbTags(empty)
dialogueSentences41
tagDensity0.293
leniency0.585
rawRatio0
effectiveRatio0
95.75% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1177
totalAiIsmAdverbs1
found
0
adverb"suddenly"
count1
highlights
0"suddenly"
100.00% AI-ism character names
Target: 0 AI-default names (17 tracked, −20% each)
codexExemptions(empty)
found(empty)
100.00% AI-ism location names
Target: 0 AI-default location names (33 tracked, −20% each)
codexExemptions(empty)
found(empty)
83.01% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1177
totalAiIsms4
found
0
word"warmth"
count1
1
word"familiar"
count1
2
word"silence"
count1
3
word"stomach"
count1
highlights
0"warmth"
1"familiar"
2"silence"
3"stomach"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches1
maxInWindow1
found
0
label"stomach dropped/sank"
count1
highlights
0"stomach dropped"
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
hedgeCount1
narrationSentences89
filterMatches(empty)
hedgeMatches
0"tried to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences118
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen38
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1186
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions20
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions14
wordCount871
uniqueNames7
maxNameDensity0.46
worstName"Rory"
maxWindowNameDensity1
worstWindowName"Rory"
discoveredNames
Rory4
Whitechapel1
Eva2
Ptolemy2
Moreau1
Soho1
Lucien3
persons
0"Rory"
1"Eva"
2"Ptolemy"
3"Moreau"
4"Lucien"
places
0"Soho"
globalScore1
windowScore1
0.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences47
glossingSentenceCount3
matches
0"looked like a funeral director who’d wand"
1"looked like the thing she’d pretended not"
2"felt like to be seen by him"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1186
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences118
matches
0"carried that scent"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs61
mean19.44
std18.29
cv0.941
sampleLengths
061
118
24
345
410
54
665
717
82
916
1011
1123
125
1331
1448
153
164
1713
1847
196
2041
217
2238
235
248
2516
268
275
2865
293
3020
3146
326
3322
345
352
363
3749
3812
3932
4014
414
424
4333
445
4513
4649
479
4852
494
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences89
matches
0"was gone"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount2
totalVerbs158
matches
0"was holding"
1"was working"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount9
semicolonCount0
flaggedSentences8
totalSentences118
ratio0.068
matches
0"Not just the silence — though Ptolemy usually greeted her with a chirp and a figure-eight around her ankles — but the quality of the stillness."
1"His lips curved — just barely, just enough to show he’d heard her."
2"He watched her with those mismatched eyes — one amber, one black as coal dust."
3"The flat smelled of him — sandalwood and cigarette smoke and something metallic."
4"“And they have long memories, and longer reach.” His voice dropped, and something shifted in his expression — something that looked like the thing she’d pretended not to see the night she walked out of his flat in Soho."
5"He’d broken her heart, sure — but that had been her fault."
6"Not just wanted — seen."
7"He reached into his jacket and pulled out a card — cream-coloured, embossed with a single phone number."
99.60% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount865
adjectiveStacks0
stackExamples(empty)
adverbCount35
adverbRatio0.04046242774566474
lyAdverbCount6
lyAdverbRatio0.006936416184971098
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences118
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences118
mean10.05
std8.94
cv0.89
sampleLengths
025
136
211
37
44
54
64
726
811
910
104
1128
1225
1312
1417
152
163
1713
1811
1918
205
215
228
2323
2415
259
2614
275
285
293
304
3112
321
333
3420
3513
363
378
386
3935
404
412
424
433
446
4532
465
478
4816
493
52.82% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats11
diversityRatio0.3728813559322034
totalSentences118
uniqueOpeners44
43.29% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences77
matches
0"Then she saw him."
ratio0.013
0.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount44
totalSentences77
matches
0"She took the stairs to"
1"She shoved the key into"
2"He looked like a funeral"
3"he said, and the old"
4"He didn’t move."
5"His lips curved — just"
6"She laughed, a short, hollow"
7"He watched her with those"
8"It was darker now, the"
9"She wondered what that meant."
10"She wondered if she cared."
11"He said it like a"
12"She didn’t sit."
13"She crossed her arms and"
14"He always carried that scent"
15"He rose from the chair"
16"She’d learned to."
17"He stopped an arm’s length"
18"Her stomach dropped."
19"She kept her face still."
ratio0.571
37.92% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount65
totalSentences77
matches
0"The rain had followed Rory"
1"She took the stairs to"
2"She shoved the key into"
3"The third turned with a"
4"The door swung open."
5"The flat was wrong."
6"The air was wrong."
7"The sort of stillness that"
8"Rory’s hand went to the"
9"Lucien Moreau sat in the"
10"The ivory handle of his"
11"He looked like a funeral"
12"he said, and the old"
13"He didn’t move."
14"His lips curved — just"
15"Rory stepped inside and shut"
16"She laughed, a short, hollow"
17"He watched her with those"
18"The left one had changed"
19"It was darker now, the"
ratio0.844
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences77
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences29
technicalSentenceCount0
matches(empty)
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags12
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags9
fancyCount1
fancyTags
0"She laughed (laugh)"
dialogueSentences41
tagDensity0.22
leniency0.439
rawRatio0.111
effectiveRatio0.049
80.4131%