Run: 2026-10-07-01M4AV5NYQRREKDGEH1VD9W5AE

Run Details

Duration:
11.5s
Cost:
$0.010341
Provider:
Mistral

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="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="4417" name="Tomás Herrera" aliases="Tommy, Tomás, Herrera" pronouns="he/him" species="Human" storyRole="Support"> <eyeColor>warm brown</eyeColor> <hair>short curly dark brown</hair> <height>5'10"</height> <age>29</age> <skinTone>olive skin</skinTone> <scar>scar running along his left forearm from a knife attack</scar> <medallion>Saint Christopher medallion around his neck</medallion> <birthplace>Seville, Spain</birthplace> <description>A former paramedic who provides off-the-books medical care for the clique.</description> <backstory>Born in Seville, Spain. He moved to London to work for the NHS but lost his license after administering unauthorized treatments to supernatural patients.</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> <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> </codex> Continue the story using the following instructions: <instructions> Write a fully fleshed out scene of at least 1000 words in the thriller genre. A detective is pursuing a suspect through rain-soaked city streets at night. The chase takes them from the street level down into an underground market. The detective must decide whether to follow the suspect into unfamiliar and potentially dangerous territory. 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 rain hammered the pavement, turning Soho’s cobbles into a slick, reflective mess. Detective Harlow Quinn’s boots splashed through puddles, her breath sharp in the cold air. She kept her eyes locked on the figure ahead—tall, lean, moving with the kind of urgency that screamed guilt. The suspect had bolted from The Raven’s Nest the moment she’d stepped inside, knocking over a stool in his haste. Now, he was leading her deeper into the labyrinth of London’s underbelly. She rounded a corner, her leather watch strap damp against her wrist. The green neon glow of the bar’s sign faded behind her, swallowed by the gloom. The suspect’s silhouette flickered under the flickering streetlamps, his coat flapping like a flag of surrender. But surrender wasn’t on the agenda. Not tonight. A left turn. A right. The streets narrowed, the buildings pressing in like spectators leaning in for a better view. The rain eased, but the air stayed thick, cloying. Quinn’s sharp jaw tightened. She wasn’t about to lose him now. The suspect skidded to a halt at the mouth of an alley, glancing back. His face was half-hidden under a hood, but she caught the glint of fear in his eyes. Then he was gone, vanishing down a set of rusted metal stairs that spiralled into darkness. Quinn didn’t hesitate. She took the stairs two at a time, her grip firm on the railing. The descent was steep, the air growing colder, damper. The scent of wet stone and something older, something metallic, filled her nose. At the bottom, a dim light flickered—an abandoned Tube station, its arches blackened with time. The suspect was already moving through the shadows, his footsteps echoing. Quinn followed, her pulse steady despite the adrenaline. She’d chased worse than this in her time. But the station wasn’t empty. Figures lurked in the corners, their faces obscured, their postures tense. A market. The Veil Market. Stalls lined the platform, their wares spread out under the sickly glow of lanterns. Jars of murky liquids, bundles of dried herbs, things that looked like they didn’t belong in this world. The air hummed with whispers, the low murmur of hushed deals. The suspect wove through the crowd, his path erratic. Quinn slowed, her instincts screaming. This wasn’t just a chase anymore. This was a plunge into the unknown. A woman with silver-streaked hair eyed her from behind a table of blackened bones. “You’re not one of us,” she said, her voice like gravel. Quinn ignored her, pushing forward. The suspect was almost out of sight, his dark coat blending into the shadows near a stall draped in tattered velvet. Then she saw it—the glint of a bone token in his hand. The entry requirement. He was going deeper. A man stepped into her path, his bulk blocking her way. His arms were crossed, his expression unreadable. “This ain’t your turf, copper.” Quinn didn’t break stride. “Move.” He didn’t. She sidestepped, but he mirrored her, his stance widening. “Last warning.” She exhaled through her nose. Time was ticking. The suspect was getting away. She reached into her coat, her fingers brushing the cold metal of her cuffs. But this wasn’t a collar. Not yet. “You don’t know what’s down there,” the man said, his voice dropping. “Things that’ll chew you up and spit you out before you can scream.” Quinn’s gaze flicked past him. The suspect had stopped, his back pressed against a wall of crumbling brick. He was watching her, waiting. Taunting. She made her decision. Quinn lunged, driving her shoulder into the man’s gut. He grunted, stumbling back, and she was past him, sprinting toward the suspect. The crowd parted, some in fear, others in amusement. The suspect bolted again, this time through a curtain of heavy beads that clattered like bones as he pushed through. Quinn followed, the beads slapping against her arms. The space beyond was tighter, the ceiling lower. The air thickened, the scent of damp earth and something acrid biting at her throat. The suspect was ahead, his breath ragged now. Then the ground gave way. Not a drop, not a stumble—an actual collapse. The floor beneath her splintered, and Quinn was falling, her stomach lurching. She hit the ground hard, the impact knocking the wind out of her. Dirt and debris rained down as she rolled onto her side, coughing. Above her, the suspect’s face appeared at the edge of the hole, his expression a mix of shock and something darker. Satisfaction? Then he was gone, his footsteps fading. Quinn pushed herself up, her body protesting. The tunnel around her was narrow, the walls slick with moisture. She could turn back. Climb out. Retreat to the familiar, the safe. But the suspect was getting away. She wiped the dirt from her face, her fingers coming away smudged. The tunnel stretched ahead, disappearing into blackness. No lights. No sounds. Just the oppressive weight of the unknown. Quinn adjusted her watch, the leather strap a familiar comfort. She’d come this far. She wasn’t about to stop now. She stepped forward, her boots sinking slightly into the damp earth. The tunnel swallowed her whole.
created_at"2026-10-07T09:30:04.687Z" 10/7/2026, 9:30:04 AM
model"mistralai/mistral-medium-3.5-20260430"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency532
moderation_latency(null)
generation_time11380
tokens_prompt1104
tokens_completion1320
native_tokens_prompt1039
native_tokens_completion1171
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"
service_tier"default"
usage0.010341
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1791365404-0PAw9xaUY1xxROwUIeO4"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1791365404-xwZpC7gFOFv2DpddaK7C"
upstream_id"86bac5f28b314ca281813f8ef76fb9b6"
provider_responses
0
endpoint_id"d21d316f-d540-4718-b36e-3beff85d0cf9"
id"86bac5f28b314ca281813f8ef76fb9b6"
is_byokfalse
latency532
model_permaslug"mistralai/mistral-medium-3.5-20260430"
provider_name"Mistral"
status200
total_cost0.010341
cache_discount(null)
upstream_inference_cost0
provider_name"Mistral"
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
totalTags2
adverbTagCount0
adverbTags(empty)
dialogueSentences6
tagDensity0.333
leniency0.667
rawRatio0
effectiveRatio0
94.19% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount861
totalAiIsmAdverbs1
found
0
adverb"slightly"
count1
highlights
0"slightly"
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)
0.00% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount861
totalAiIsms18
found
0
word"gloom"
count1
1
word"flickered"
count2
2
word"glint"
count2
3
word"footsteps"
count2
4
word"echoing"
count1
5
word"pulse"
count1
6
word"velvet"
count1
7
word"unreadable"
count1
8
word"flicked"
count1
9
word"stomach"
count1
10
word"lurching"
count1
11
word"familiar"
count2
12
word"oppressive"
count1
13
word"weight"
count1
highlights
0"gloom"
1"flickered"
2"glint"
3"footsteps"
4"echoing"
5"pulse"
6"velvet"
7"unreadable"
8"flicked"
9"stomach"
10"lurching"
11"familiar"
12"oppressive"
13"weight"
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
emotionTells1
narrationSentences94
matches
0"e in fear"
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount0
narrationSentences94
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences98
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen21
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords857
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions3
unquotedAttributions0
matches(empty)
71.21% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions21
wordCount825
uniqueNames9
maxNameDensity1.58
worstName"Quinn"
maxWindowNameDensity2.5
worstWindowName"Quinn"
discoveredNames
Soho1
Harlow1
Quinn13
Raven1
Nest1
London1
Tube1
Veil1
Market1
persons
0"Harlow"
1"Quinn"
places
0"Soho"
1"Raven"
2"London"
globalScore0.712
windowScore0.833
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences63
glossingSentenceCount1
matches
0"looked like they didn’t belong in this wo"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount857
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences98
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs28
mean30.61
std17.7
cv0.578
sampleLengths
078
151
240
347
454
548
652
718
825
926
1019
1123
125
1313
1434
1525
1624
174
1851
1939
205
2145
2229
2330
246
2530
2620
2716
94.06% Passive voice overuse
Target: ≤2% passive sentences
passiveCount3
totalSentences94
matches
0"was gone"
1"were crossed"
2"was gone"
0.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount6
totalVerbs138
matches
0"was leading"
1"was already moving"
2"was going"
3"was ticking"
4"was watching"
5"was falling"
26.24% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount4
semicolonCount0
flaggedSentences4
totalSentences98
ratio0.041
matches
0"She kept her eyes locked on the figure ahead—tall, lean, moving with the kind of urgency that screamed guilt."
1"At the bottom, a dim light flickered—an abandoned Tube station, its arches blackened with time."
2"Then she saw it—the glint of a bone token in his hand."
3"Not a drop, not a stumble—an actual collapse."
94.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount831
adjectiveStacks1
stackExamples
0"ahead—tall, lean, moving"
adverbCount18
adverbRatio0.021660649819494584
lyAdverbCount3
lyAdverbRatio0.0036101083032490976
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences98
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences98
mean8.74
std5.12
cv0.585
sampleLengths
013
114
219
320
412
512
615
716
86
92
103
112
1215
139
144
157
1614
1717
1816
193
2014
219
2213
2315
2411
258
268
275
2811
292
303
3114
3218
3311
349
355
366
377
3814
3911
405
4121
4212
433
444
4511
467
475
484
491
37.76% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats12
diversityRatio0.29591836734693877
totalSentences98
uniqueOpeners29
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount5
totalSentences84
matches
0"Then he was gone, vanishing"
1"Then she saw it—the glint"
2"Then the ground gave way."
3"Then he was gone, his"
4"Just the oppressive weight of"
ratio0.06
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount21
totalSentences84
matches
0"She kept her eyes locked"
1"She rounded a corner, her"
2"She wasn’t about to lose"
3"His face was half-hidden under"
4"She took the stairs two"
5"She’d chased worse than this"
6"she said, her voice like"
7"He was going deeper."
8"His arms were crossed, his"
9"She sidestepped, but he mirrored"
10"She exhaled through her nose."
11"She reached into her coat,"
12"He was watching her, waiting."
13"She made her decision."
14"He grunted, stumbling back, and"
15"She hit the ground hard,"
16"She could turn back."
17"She wiped the dirt from"
18"She’d come this far."
19"She wasn’t about to stop"
ratio0.25
43.33% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount70
totalSentences84
matches
0"The rain hammered the pavement,"
1"Detective Harlow Quinn’s boots splashed"
2"She kept her eyes locked"
3"The suspect had bolted from"
4"She rounded a corner, her"
5"The green neon glow of"
6"The suspect’s silhouette flickered under"
7"A left turn."
8"The streets narrowed, the buildings"
9"The rain eased, but the"
10"Quinn’s sharp jaw tightened."
11"She wasn’t about to lose"
12"The suspect skidded to a"
13"His face was half-hidden under"
14"Quinn didn’t hesitate."
15"She took the stairs two"
16"The descent was steep, the"
17"The scent of wet stone"
18"The suspect was already moving"
19"Quinn followed, her pulse steady"
ratio0.833
59.52% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount1
totalSentences84
matches
0"Now, he was leading her"
ratio0.012
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences37
technicalSentenceCount2
matches
0"The suspect was already moving through the shadows, his footsteps echoing."
1"The floor beneath her splintered, and Quinn was falling, her stomach lurching."
0.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags2
uselessAdditionCount1
matches
0"the man said, his voice dropping"
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags2
fancyCount0
fancyTags(empty)
dialogueSentences6
tagDensity0.333
leniency0.667
rawRatio0
effectiveRatio0
80.6774%