Run: 2026-10-07-01M4ATN9NJMKQB27DBV1ET9M6C

Run Details

Duration:
11.9s
Cost:
$0.010243
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 fell in sheets, turning the Soho streets into a slick, shimmering maze. Detective Harlow Quinn’s boots splashed through puddles, her breath steady despite the cold bite of the night. The salt-and-pepper stubble on her scalp bristled as she scanned the alley ahead. A shadow darted between the neon glow of The Raven’s Nest and the next building, quick as a rat down a drainpipe. She didn’t hesitate. Quinn pushed off the damp brick wall and gave chase, her leather watch snug against her wrist as she timed her steps. The suspect—tall, lanky, moving with the kind of urgency that screamed guilt—ducked around a corner. She followed, her sharp jaw set. The alley narrowed, the stench of wet garbage and stale beer thick in the air. A fire escape groaned above her, but the suspect was already gone. Then she saw it: a flicker of movement near a rusted service door, half-hidden by a pile of sodden cardboard. The door swung shut. Quinn reached it in three strides, her hand pressing against the cold metal. It gave way with a creak, revealing a staircase descending into darkness. No light. No sound but the distant drip of water. She pulled her torch from her belt, the beam cutting through the gloom. The steps were steep, the air thick with the scent of damp earth and something else—something metallic, like old coins or blood. She descended, one hand trailing the wall. The torchlight caught glimpses of graffiti, symbols she didn’t recognise. The deeper she went, the more the air hummed, not with the usual city noise, but with a low, almost imperceptible vibration, like the pulse of a living thing. Then she heard it: footsteps. Not the slap of wet shoes on pavement, but the soft scuff of boots on stone. The suspect was down here. And he wasn’t alone. The staircase opened into a cavernous space, the walls lined with stalls that looked like they’d been carved from the earth itself. Lanterns flickered, casting long shadows over tables laden with jars of murky liquids, bundles of dried herbs, and things she couldn’t—didn’t want to—identify. The Veil Market. She’d heard whispers of it in the force, dismissed them as urban legends. But here it was. A figure moved ahead, slipping between the stalls. The same lanky build. She lost sight of him behind a curtain of beads that clattered like bones. Quinn stepped forward, her torch now useless against the sea of lantern light. The market’s patrons turned to stare—faces half-hidden under hoods, eyes gleaming in the dimness. A woman with a necklace of teeth bared her own in something that wasn’t a smile. A man with too many fingers on one hand adjusted his coat, watching her with the stillness of a predator. She ignored them, her gaze locked on the beads. They swayed, then stilled. The suspect was gone. A hand clamped down on her shoulder. She spun, her fist already raised, but the man who stood there didn’t flinch. Olive skin, dark curls, a scar running the length of his forearm. Tomás Herrera. His Saint Christopher medallion glinted in the lantern light. “You shouldn’t be here, Detective.” Quinn didn’t lower her fist. “Herrera. Didn’t peg you for the type to shop in places like this.” His lips twitched, but there was no humour in it. “And I didn’t peg you for the type to chase a man into a hole without knowing what’s at the bottom.” She glanced past him, towards the beads. “He went that way.” “He’s not your concern anymore.” “He’s my suspect.” Tomás exhaled, his breath visible in the cold air. “This isn’t your city down here. The rules are different.” A shout echoed from deeper in the market, followed by the clatter of something heavy hitting stone. The beads rattled again. Quinn didn’t wait. She shoved past Tomás, her shoulder brushing his, and pushed through the beads. The space beyond was a dead end, the walls lined with shelves of glass jars. The suspect was gone. But the air smelled of ozone and burnt copper. And then she saw it: a bone token on the floor, its surface etched with symbols that matched the graffiti on the stairs. Tomás appeared beside her, his voice low. “That’s your way out. Use it.” She picked up the token, the surface smooth under her fingers. “And if I don’t?” He didn’t answer. Instead, he nodded towards the far wall, where a group of figures had gathered. They weren’t looking at her. They were looking past her, their expressions shifting from curiosity to something darker. Quinn turned. The market’s entrance—the staircase she’d come down—was gone. In its place stood a solid wall of stone, as if it had always been there. Tomás’s voice was a growl. “We need to move. Now.” A figure detached itself from the shadows behind them. Tall, gaunt, its face stretched too wide for its skull. It smiled, and the lanterns flickered as if in warning. Quinn’s fingers tightened around the token. The suspect was here. The answers she wanted were here. But the market wasn’t just a place. It was a test. And she hadn’t come this far to turn back.
created_at"2026-10-07T09:21:07.773Z" 10/7/2026, 9:21:07 AM
model"mistralai/mistral-medium-3.5-20260430"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency539
moderation_latency(null)
generation_time11886
tokens_prompt1104
tokens_completion1309
native_tokens_prompt1039
native_tokens_completion1158
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"
usage0.0102435
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1791364867-NVujgElvhdnpV3VdxXZC"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1791364867-kqmKJVef9gWZ88G19fIx"
upstream_id"634de7cf24dc4da4901da0413ee5277c"
provider_responses
0
endpoint_id"d21d316f-d540-4718-b36e-3beff85d0cf9"
id"634de7cf24dc4da4901da0413ee5277c"
is_byokfalse
latency539
model_permaslug"mistralai/mistral-medium-3.5-20260430"
provider_name"Mistral"
status200
total_cost0.0102435
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
totalTags0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount875
totalAiIsmAdverbs0
found(empty)
highlights(empty)
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)
37.14% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount875
totalAiIsms11
found
0
word"scanned"
count1
1
word"flicker"
count1
2
word"gloom"
count1
3
word"pulse"
count1
4
word"footsteps"
count1
5
word"flickered"
count2
6
word"gleaming"
count1
7
word"predator"
count1
8
word"echoed"
count1
9
word"etched"
count1
highlights
0"scanned"
1"flicker"
2"gloom"
3"pulse"
4"footsteps"
5"flickered"
6"gleaming"
7"predator"
8"echoed"
9"etched"
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
narrationSentences76
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences76
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences86
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen29
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords868
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions1
unquotedAttributions0
matches(empty)
99.49% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions22
wordCount792
uniqueNames11
maxNameDensity1.01
worstName"Quinn"
maxWindowNameDensity2
worstWindowName"Tomás"
discoveredNames
Soho1
Harlow1
Quinn8
Raven1
Nest1
Veil1
Market1
Herrera1
Saint1
Christopher1
Tomás5
persons
0"Harlow"
1"Quinn"
2"Raven"
3"Herrera"
4"Saint"
5"Christopher"
6"Tomás"
places
0"Soho"
globalScore0.995
windowScore1
57.41% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences54
glossingSentenceCount2
matches
0"looked like they’d been carved from the e"
1"appeared beside her, his voice low"
0.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches3
per1kWords3.456
wordCount868
matches
0"No sound but"
1"not with the usual city noise, but with a low, almost imperceptible vibration, like the pulse o"
2"Not the slap of wet shoes on pavement, but the soft scuff of boots on stone"
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences86
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs28
mean31
std19.84
cv0.64
sampleLengths
066
146
251
370
446
530
665
726
863
917
1044
115
1218
1331
1411
155
163
1719
1837
1951
2013
2115
2235
2326
2410
2529
2627
279
86.80% Passive voice overuse
Target: ≤2% passive sentences
passiveCount4
totalSentences76
matches
0"been carved"
1"was gone"
2"was gone"
3"was gone"
92.47% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount2
totalVerbs124
matches
0"weren’t looking"
1"were looking"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount8
semicolonCount0
flaggedSentences5
totalSentences86
ratio0.058
matches
0"The suspect—tall, lanky, moving with the kind of urgency that screamed guilt—ducked around a corner."
1"The steps were steep, the air thick with the scent of damp earth and something else—something metallic, like old coins or blood."
2"Lanterns flickered, casting long shadows over tables laden with jars of murky liquids, bundles of dried herbs, and things she couldn’t—didn’t want to—identify."
3"The market’s patrons turned to stare—faces half-hidden under hoods, eyes gleaming in the dimness."
4"The market’s entrance—the staircase she’d come down—was gone."
94.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount803
adjectiveStacks1
stackExamples
0"suspect—tall, lanky, moving"
adverbCount18
adverbRatio0.0224159402241594
lyAdverbCount0
lyAdverbRatio0
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences86
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences86
mean10.09
std6.14
cv0.608
sampleLengths
014
117
213
322
43
522
615
76
815
912
1020
114
1213
1312
142
158
1613
1722
187
1910
2029
215
2216
235
244
2522
2623
273
2813
294
308
314
3214
3313
3414
3516
3620
379
384
394
407
4114
4212
432
449
455
465
4713
4810
4921
40.70% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats8
diversityRatio0.29069767441860467
totalSentences86
uniqueOpeners25
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount3
totalSentences73
matches
0"Then she saw it: a"
1"Then she heard it: footsteps."
2"Instead, he nodded towards the"
ratio0.041
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount20
totalSentences73
matches
0"She didn’t hesitate."
1"She followed, her sharp jaw"
2"It gave way with a"
3"She pulled her torch from"
4"She descended, one hand trailing"
5"She’d heard whispers of it"
6"She lost sight of him"
7"She ignored them, her gaze"
8"They swayed, then stilled."
9"She spun, her fist already"
10"His Saint Christopher medallion glinted"
11"His lips twitched, but there"
12"She glanced past him, towards"
13"She shoved past Tomás, her"
14"She picked up the token,"
15"He didn’t answer."
16"They weren’t looking at her."
17"They were looking past her,"
18"It smiled, and the lanterns"
19"It was a test."
ratio0.274
49.04% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount60
totalSentences73
matches
0"The rain fell in sheets,"
1"Detective Harlow Quinn’s boots splashed"
2"The salt-and-pepper stubble on her"
3"A shadow darted between the"
4"She didn’t hesitate."
5"Quinn pushed off the damp"
6"The suspect—tall, lanky, moving with"
7"She followed, her sharp jaw"
8"The alley narrowed, the stench"
9"A fire escape groaned above"
10"The door swung shut."
11"Quinn reached it in three"
12"It gave way with a"
13"She pulled her torch from"
14"The steps were steep, the"
15"She descended, one hand trailing"
16"The torchlight caught glimpses of"
17"The deeper she went, the"
18"The suspect was down here."
19"The staircase opened into a"
ratio0.822
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences73
matches(empty)
ratio0
67.67% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences38
technicalSentenceCount4
matches
0"She lost sight of him behind a curtain of beads that clattered like bones."
1"She spun, her fist already raised, but the man who stood there didn’t flinch."
2"And then she saw it: a bone token on the floor, its surface etched with symbols that matched the graffiti on the stairs."
3"They were looking past her, their expressions shifting from curiosity to something darker."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags0
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags0
80.8241%