Run: 2026-02-20-01KHXPM3B00C47XTQ8Q4SF4CHK

Run Details

Duration:
20.8s
Cost:
$0.001968
Provider:
Mistral

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="4416" name="Detective Harlow Quinn" aliases="Quinn, Detective Quinn, Harlow" pronouns="she/her" species="Human" storyRole="Antagonist"> <eyeColor>brown</eyeColor> <hair>closely cropped salt-and-pepper</hair> <height>5'9"</height> <age>41</age> <jawline>sharp jaw</jawline> <watch>worn leather watch on her left wrist</watch> <bearing>military precision</bearing> <description>A Metropolitan Police detective with 18 years of decorated service who suspects the clique is involved in criminal activity.</description> <backstory>She lost her partner, DS Morris, during a case three years ago under unexplained circumstances that had supernatural origins she doesn't yet understand.</backstory> </character> <character id="4414" name="Eva Kowalski" aliases="Eva, Evie" pronouns="she/her" species="Human" storyRole="Best Friend"> <eyeColor>green</eyeColor> <hair>curly red</hair> <height>5'4"</height> <age>26</age> <glasses>round glasses</glasses> <satchel>worn leather satchel full of books</satchel> <freckles>freckled complexion</freckles> <nervousHabit>tucking hair behind her left ear</nervousHabit> <description>Aurora's childhood friend and an occult researcher.</description> <backstory>Moved to London two years before Aurora and works as a research assistant at the British Museum's restricted archives. She holds a master's degree in Ancient History from Oxford.</backstory> </character> <location id="856" name="The Veil Market" aliases="Veil Market, the Market, underground market" realm="Earth"> <currentLocation>abandoned Tube station beneath Camden</currentLocation> <entryRequirement>bone token</entryRequirement> <schedule>moves locations every full moon</schedule> <description>A hidden supernatural black market that sells enchanted goods, banned alchemical substances, and information.</description> </location> <item id="2004" name="Veil Compass" aliases="the Compass, shadow compass" type="Tool" origin="The Veil Market"> <casingMaterial>small brass compass</casingMaterial> <faceMarkings>face etched with protective sigils</faceMarkings> <needle>points toward the nearest supernatural rift or portal</needle> <patina>casing has a patina of verdigris</patina> <creator>crafted by a Shade artisan</creator> <description>A compass attuned to supernatural energy.</description> </item> </codex> Write a fully fleshed out scene of at least 1000 words in the mystery genre. A detective arrives at a crime scene in an unusual location. Something is wrong — the evidence doesn't add up. A colleague is present, offering a different interpretation. Through careful observation and deduction, the detective begins to see what others have missed. 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 abandoned Tube station smelled of damp concrete and rust, the kind of scent that clung to the back of your throat like a bad memory. Detective Harlow Quinn stepped over the police tape, her boots scuffing against the cracked tiles of what had once been Camden Town station. The overhead lights flickered, casting long shadows that stretched and twisted across the graffiti-scarred walls. Her leather watch creaked as she adjusted it, the second hand ticking with the same relentless precision she applied to every case. "Quinn, over here." The voice belonged to DS Patel, her current partner, who stood near the mouth of an old service tunnel. His dark eyes were sharp beneath the brim of his cap, but there was a tightness around his mouth that set her teeth on edge. She joined him, her gaze sweeping over the scene. A body lay sprawled across the tracks, limbs splayed at unnatural angles. Male, mid-thirties, dressed in a rumpled suit that had seen better days. The kind of man who might have been a commuter, if this station had still been in use. But Camden Town had been closed for decades, its tunnels repurposed by the city for storage, its platforms left to rot. And yet, here he was. "Time of death?" Quinn asked, crouching beside the body. The man’s skin was pale, almost waxy, but there was no blood. No visible wounds. Just a stillness that went beyond death. "ME’s on her way, but I’d say within the last two hours," Patel said. "No ID on him. Wallet’s gone, if he even had one." Quinn’s fingers hovered over the man’s jacket, careful not to disturb anything. The fabric was cold, damp from the tunnel’s perpetual chill. She turned his hand over, examining the fingers. No defensive wounds. No dirt under the nails. Just smooth, unmarked skin. "Odd," she murmured. "Yeah. No signs of struggle either." Patel crossed his arms. "Could be a mugging gone wrong. Guy gets jumped, has a heart attack, thief takes his wallet and runs." Quinn stood, brushing her palms against her trousers. "Then where’s the blood? Where’s the mess? Even if he had a heart attack, there’d be *something*." She gestured to the platform. "No scuff marks. No drag marks. No signs anyone else was here at all." Patel exhaled through his nose. "You’re reaching, Quinn. It’s late. We’re tired. Maybe he just—" "Just what? Dropped dead in an abandoned Tube station for no reason?" She turned away from him, scanning the platform again. Her gaze snagged on something half-buried in the grime near the edge of the tracks. A small, brass object, its surface dulled by time and neglect. She knelt, pulling a pen from her pocket to nudge it free. The object rolled into her palm—a compass. Not just any compass. The casing was etched with strange, looping symbols, the kind of thing you’d see in a museum, not on a dead man in a forgotten tunnel. Patel leaned in. "What the hell is that?" Quinn turned it over. The needle didn’t point north. It didn’t point anywhere. It spun lazily, as if caught in an unfelt breeze. "I don’t know," she said slowly. "But I don’t think it’s standard issue." Patel frowned. "You think this is connected?" Quinn pocketed the compass, ignoring the way the metal seemed to hum against her fingers. "I think it’s worth looking into." She stood, her mind already racing. The symbols on the casing—she’d seen them before. Not in any police file, but in the margins of an old case report. Morris’s case. Her partner’s death had been ruled an accident. A fall down a flight of stairs during a chase. But the report had mentioned something else. Something about strange markings at the scene. Something the coroner had dismissed as graffiti. Quinn’s jaw tightened. She’d let it go. Too many questions, not enough answers. But now, here it was again. She turned back to Patel. "I want this place locked down. No one in or out until forensics has gone over every inch. And I want a full background check on this guy. If he’s got any connection to the occult, I want to know about it." Patel blinked. "The *what*?" Quinn ignored him, her gaze drifting back to the tunnel. Somewhere in the dark, the compass in her pocket pulsed faintly, like a heartbeat. And then, for the first time in three years, she let herself consider the possibility that Morris hadn’t just fallen. That something had *pushed* him.
created_at"2026-02-20T14:17:53.772Z" 2/20/2026, 2:17:53 PM
model"mistralai/mistral-large-2512" 1/1/2512, 12:00:00 AM
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency471
moderation_latency(null)
generation_time20745
tokens_prompt923
tokens_completion1139
native_tokens_prompt875
native_tokens_completion1020
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)
origin"https://ncbench.com/"
is_byokfalse
finish_reason"stop"
native_finish_reason"stop"
usage0.0019675
router(null)
provider_responses
0
endpoint_id"fa718841-07ea-4b7e-b8a3-6069545c7e6a"
id"b5f57f94e0a74a11a2f44fff221985f5"
is_byokfalse
latency471
model_permaslug"mistralai/mistral-large-2512" 1/1/2512, 12:00:00 AM
provider_name"Mistral"
status200
api_type"completions"
id"gen-1771597073-UeasbW2253kscEi3CKDV"
upstream_id"b5f57f94e0a74a11a2f44fff221985f5"
total_cost0.0019675
cache_discount(null)
upstream_inference_cost0
provider_name"Mistral"

Evaluation details

Result Evaluator Details Meta Data
0.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags9
adverbTagCount2
adverbTags
0"She turned away [away]"
1"she said slowly [slowly]"
dialogueSentences18
tagDensity0.5
leniency1
rawRatio0.222
effectiveRatio0.222
86.84% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount760
totalAiIsmAdverbs2
found
0
adverb"lazily"
count1
1
adverb"slowly"
count1
highlights
0"lazily"
1"slowly"
80.00% AI-ism character names
Target: 0 AI-default names (17 tracked, −20% each)
codexExemptions(empty)
found
0"Patel"
100.00% AI-ism location names
Target: 0 AI-default location names (33 tracked, −20% each)
codexExemptions(empty)
found(empty)
53.95% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount760
totalAiIsms7
found
0
word"flickered"
count1
1
word"chill"
count1
2
word"scanning"
count1
3
word"etched"
count1
4
word"standard"
count1
5
word"racing"
count1
6
word"pulsed"
count1
highlights
0"flickered"
1"chill"
2"scanning"
3"etched"
4"standard"
5"racing"
6"pulsed"
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
narrationSentences62
matches(empty)
96.77% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount1
narrationSentences62
filterMatches
0"watch"
hedgeMatches
0"seemed to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences71
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen42
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans3
markdownWords3
totalWords755
ratio0.004
matches
0"something"
1"what"
2"pushed"
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions3
unquotedAttributions0
matches(empty)
80.68% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions24
wordCount577
uniqueNames7
maxNameDensity1.39
worstName"Quinn"
maxWindowNameDensity2.5
worstWindowName"Patel"
discoveredNames
Tube1
Harlow1
Quinn8
Camden2
Town2
Patel8
Morris2
persons
0"Harlow"
1"Quinn"
2"Patel"
3"Morris"
places
0"Camden"
1"Town"
globalScore0.807
windowScore0.833
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences39
glossingSentenceCount0
matches(empty)
67.55% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches1
per1kWords1.325
wordCount755
matches
0"Not in any police file, but in the margins of an old case report"
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences71
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs23
mean32.83
std21.71
cv0.661
sampleLengths
086
147
277
331
425
542
63
729
844
915
1047
1149
128
1336
147
1551
1639
1719
1847
194
2024
2120
225
88.29% Passive voice overuse
Target: ≤2% passive sentences
passiveCount3
totalSentences62
matches
0"been closed"
1"was etched"
2"been ruled"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs95
matches(empty)
62.37% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount2
semicolonCount0
flaggedSentences2
totalSentences71
ratio0.028
matches
0"The object rolled into her palm—a compass."
1"The symbols on the casing—she’d seen them before."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount582
adjectiveStacks0
stackExamples(empty)
adverbCount18
adverbRatio0.030927835051546393
lyAdverbCount3
lyAdverbRatio0.005154639175257732
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences71
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences71
mean10.63
std7.49
cv0.705
sampleLengths
026
123
215
322
422
525
69
712
812
918
1021
115
129
1312
143
157
1614
1711
1812
1910
208
213
225
234
243
2510
2619
278
2822
2914
305
3110
3221
3315
3411
3512
367
374
3826
393
405
414
425
434
4410
456
467
472
485
4915
63.85% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats3
diversityRatio0.4084507042253521
totalSentences71
uniqueOpeners29
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount4
totalSentences57
matches
0"Just a stillness that went"
1"Just smooth, unmarked skin."
2"Too many questions, not enough"
3"Somewhere in the dark, the"
ratio0.07
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount15
totalSentences57
matches
0"Her leather watch creaked as"
1"His dark eyes were sharp"
2"She joined him, her gaze"
3"She turned his hand over,"
4"She gestured to the platform"
5"She turned away from him,"
6"Her gaze snagged on something"
7"She knelt, pulling a pen"
8"It didn’t point anywhere."
9"It spun lazily, as if"
10"she said slowly"
11"She stood, her mind already"
12"Her partner’s death had been"
13"She’d let it go."
14"She turned back to Patel."
ratio0.263
91.58% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount42
totalSentences57
matches
0"The abandoned Tube station smelled"
1"Detective Harlow Quinn stepped over"
2"The overhead lights flickered, casting"
3"Her leather watch creaked as"
4"The voice belonged to DS"
5"His dark eyes were sharp"
6"She joined him, her gaze"
7"A body lay sprawled across"
8"The kind of man who"
9"Quinn asked, crouching beside the"
10"The man’s skin was pale,"
11"Quinn’s fingers hovered over the"
12"The fabric was cold, damp"
13"She turned his hand over,"
14"Patel crossed his arms"
15"Quinn stood, brushing her palms"
16"She gestured to the platform"
17"Patel exhaled through his nose."
18"She turned away from him,"
19"Her gaze snagged on something"
ratio0.737
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences57
matches(empty)
ratio0
18.63% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences23
technicalSentenceCount4
matches
0"The abandoned Tube station smelled of damp concrete and rust, the kind of scent that clung to the back of your throat like a bad memory."
1"The overhead lights flickered, casting long shadows that stretched and twisted across the graffiti-scarred walls."
2"His dark eyes were sharp beneath the brim of his cap, but there was a tightness around his mouth that set her teeth on edge."
3"Male, mid-thirties, dressed in a rumpled suit that had seen better days."
69.44% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags9
uselessAdditionCount1
matches
0"She stood, her mind already racing"
94.44% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags4
fancyCount1
fancyTags
0"she murmured (murmur)"
dialogueSentences18
tagDensity0.222
leniency0.444
rawRatio0.25
effectiveRatio0.111
81.8133%