Run: 2026-02-20-01KHY94V8AB57EJ71VPTFEFFJK

Run Details

Duration:
2.9s
Cost:
$0.000187
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 neon glow of the Raven’s Nest flickered like a dying ember against the rain-slicked pavement. Detective Quinn’s breath fogged in the cold air as she sprinted, her boots kicking up splashes of black water. The scent of damp stone and old wood clung to the air, thick and heavy. She had been chasing Tomás Herrera for hours, ever since he’d vanished from the bar’s back room, his scarred forearm twitching as if remembering something. The alley behind the Nest twisted into darkness, the flickering lights of the bar casting long, jagged shadows. Quinn’s fingers tightened around the grip of her pistol, her jaw clenched. She’d seen enough of this city to know that every shadow could hide a blade—or worse. Tomás had been working for the clique, but he’d been slipping. She’d caught him watching her, his eyes dark with something unreadable. Now, he was gone. A distant *clink* of metal echoed from below. Quinn’s pulse spiked. She didn’t need to see it to know—someone was moving. She turned the corner, her boots slapping against the wet pavement, and the scent of damp earth and something metallic filled her nostrils. The alley narrowed, the walls slick with rain. She could hear it now, the rhythmic *tap-tap-tap* of boots on stone. Then she saw it—a set of rusted metal steps descending into darkness. Quinn hesitated. The clique didn’t operate in the open. They moved in the shadows, where no one could see them. But Tomás had been leading her here. She had to know why. She stepped forward, her boots sinking into the wet gravel. The steps creaked under her weight, the sound echoing like a warning. The air grew colder, thicker. The scent of ozone and something faintly metallic filled her lungs. Quinn’s fingers flexed around her pistol, her breath coming in short, sharp gasps. The steps ended in a dimly lit tunnel, the walls lined with strange symbols she didn’t recognize. The *tap-tap-tap* grew louder, closer. Quinn’s stomach twisted. She had no idea what waited below. But she had no choice. She took the first step. The tunnel sloped downward, the air growing heavier with each step. Quinn’s flashlight flickered, casting jagged shadows on the damp stone walls. The *tap-tap-tap* was now a steady rhythm, like a heartbeat. She could hear voices, faint and distant, but too muffled to make out the words. Then the tunnel opened into a vast, underground chamber. The air was thick with the scent of damp stone and something else—something sweet and metallic. Quinn’s breath caught. The chamber was vast, the walls lined with strange artifacts and glowing runes. In the center, a table stood, laden with strange bottles and vials, their contents swirling with an eerie light. And there, in the shadows, Tomás Herrera stood. His eyes were locked on her, his expression unreadable. Behind him, a figure moved, their back to her, their movements fluid and precise. Quinn’s pistol was already drawn. She didn’t hesitate. She fired. The shot echoed through the chamber, the bullet striking the figure in the back. They staggered, their movements slowing, their eyes widening in shock. Quinn didn’t wait to see what happened next. She lunged for Tomás, her boots slamming against the stone floor. “You’re not leaving,” she growled, her voice rough with anger. Tomás didn’t flinch. “You shouldn’t have come down here.” Quinn’s grip tightened on her pistol. “You’re working with them.” Tomás’s scarred forearm twitched. “They’re not what they seem.” The figure in the shadows turned slowly, their face obscured by the dim light. Quinn’s pulse spiked. She recognized the bearing—military precision, just like hers. But this wasn’t a human. It was something else. And it was watching her.
created_at"2026-02-20T19:41:36.919Z" 2/20/2026, 7:41:36 PM
model"mistralai/ministral-3b-2512"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency191
moderation_latency(null)
generation_time2471
tokens_prompt1140
tokens_completion990
native_tokens_prompt1027
native_tokens_completion839
native_tokens_completion_images(null)
native_tokens_reasoning0
native_tokens_cached1024
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"
usage0.0001866
router(null)
provider_responses
0
endpoint_id"b9f6828f-aef9-498a-91f5-6816fbf72420"
id"9e6be3af6c3f4f3b9e9183d2509ee3f2"
is_byokfalse
latency191
model_permaslug"mistralai/ministral-3b-2512"
provider_name"Mistral"
status200
api_type"completions"
id"gen-1771616496-LS6zIhyk6aGbbjt61Lao"
upstream_id"9e6be3af6c3f4f3b9e9183d2509ee3f2"
total_cost0.0001866
cache_discount(null)
upstream_inference_cost0
provider_name"Mistral"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags1
adverbTagCount0
adverbTags(empty)
dialogueSentences4
tagDensity0.25
leniency0.5
rawRatio0
effectiveRatio0
91.96% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount622
totalAiIsmAdverbs1
found
0
adverb"slowly"
count1
highlights
0"slowly"
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)
wordCount622
totalAiIsms13
found
0
word"flickered"
count2
1
word"jaw clenched"
count1
2
word"unreadable"
count2
3
word"echoed"
count2
4
word"pulse"
count2
5
word"rhythmic"
count1
6
word"weight"
count1
7
word"echoing"
count1
8
word"stomach"
count1
highlights
0"flickered"
1"jaw clenched"
2"unreadable"
3"echoed"
4"pulse"
5"rhythmic"
6"weight"
7"echoing"
8"stomach"
66.67% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches2
maxInWindow2
found
0
label"jaw/fists clenched"
count1
1
label"air was thick with"
count1
highlights
0"jaw clenched"
1"The air was thick with"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells1
narrationSentences62
matches
0"h with anger"
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences62
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences65
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen25
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans4
markdownWords4
totalWords616
ratio0.006
matches
0"clink"
1"tap-tap-tap"
2"tap-tap-tap"
3"tap-tap-tap"
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions1
unquotedAttributions0
matches(empty)
49.67% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions24
wordCount598
uniqueNames5
maxNameDensity2.01
worstName"Quinn"
maxWindowNameDensity2.5
worstWindowName"Quinn"
discoveredNames
Raven1
Nest2
Quinn12
Tomás7
Herrera2
persons
0"Raven"
1"Nest"
2"Quinn"
3"Tomás"
4"Herrera"
places(empty)
globalScore0.497
windowScore0.833
91.86% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences43
glossingSentenceCount1
matches
0"as if remembering something"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount616
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences65
matches
0"know that every"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs18
mean34.22
std23.59
cv0.689
sampleLengths
075
172
264
344
451
537
65
747
860
931
1010
1143
1210
139
1410
159
1634
175
93.94% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences62
matches
0"was gone"
1"were locked"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs106
matches
0"was watching"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount5
semicolonCount0
flaggedSentences5
totalSentences65
ratio0.077
matches
0"She’d seen enough of this city to know that every shadow could hide a blade—or worse."
1"She didn’t need to see it to know—someone was moving."
2"Then she saw it—a set of rusted metal steps descending into darkness."
3"The air was thick with the scent of damp stone and something else—something sweet and metallic."
4"She recognized the bearing—military precision, just like hers."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount604
adjectiveStacks0
stackExamples(empty)
adverbCount14
adverbRatio0.023178807947019868
lyAdverbCount3
lyAdverbRatio0.004966887417218543
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences65
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences65
mean9.48
std5.22
cv0.551
sampleLengths
016
119
215
325
418
512
616
711
811
94
108
113
1210
1323
148
1512
1612
172
187
1911
207
215
2210
2312
245
2511
2613
2717
285
293
307
315
325
3311
3411
3510
3615
379
3816
393
4013
4119
428
439
4414
455
463
472
4814
4910
40.77% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats6
diversityRatio0.24615384615384617
totalSentences65
uniqueOpeners16
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences60
matches
0"Then she saw it—a set"
1"Then the tunnel opened into"
ratio0.033
93.33% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount19
totalSentences60
matches
0"She had been chasing Tomás"
1"She’d seen enough of this"
2"She’d caught him watching her,"
3"She didn’t need to see"
4"She turned the corner, her"
5"She could hear it now,"
6"They moved in the shadows,"
7"She had to know why."
8"She stepped forward, her boots"
9"She had no idea what"
10"She took the first step."
11"She could hear voices, faint"
12"His eyes were locked on"
13"She didn’t hesitate."
14"They staggered, their movements slowing,"
15"She lunged for Tomás, her"
16"she growled, her voice rough"
17"She recognized the bearing—military precision,"
18"It was something else."
ratio0.317
43.33% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount50
totalSentences60
matches
0"The neon glow of the"
1"Detective Quinn’s breath fogged in"
2"The scent of damp stone"
3"She had been chasing Tomás"
4"The alley behind the Nest"
5"Quinn’s fingers tightened around the"
6"She’d seen enough of this"
7"Tomás had been working for"
8"She’d caught him watching her,"
9"A distant *clink* of metal"
10"Quinn’s pulse spiked."
11"She didn’t need to see"
12"She turned the corner, her"
13"The alley narrowed, the walls"
14"She could hear it now,"
15"The clique didn’t operate in"
16"They moved in the shadows,"
17"She had to know why."
18"She stepped forward, her boots"
19"The steps creaked under her"
ratio0.833
83.33% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount1
totalSentences60
matches
0"Now, he was gone."
ratio0.017
96.77% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences31
technicalSentenceCount2
matches
0"She had been chasing Tomás Herrera for hours, ever since he’d vanished from the bar’s back room, his scarred forearm twitching as if remembering something."
1"They staggered, their movements slowing, their eyes widening in shock."
0.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags1
uselessAdditionCount1
matches
0"she growled, her voice rough with anger"
0.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags1
fancyCount1
fancyTags
0"she growled (growl)"
dialogueSentences4
tagDensity0.25
leniency0.5
rawRatio1
effectiveRatio0.5
78.3881%