Run: 2026-02-20-01KHXPXQKMJDKTVSEPBST89W4R

Run Details

Duration:
24.8s
Cost:
$0.002433
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="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="4413" name="Silas Blackwood" aliases="Silas, Si, the Spymaster" pronouns="he/him" species="Human" storyRole="Mentor"> <eyeColor>hazel</eyeColor> <hair>grey-streaked auburn</hair> <height>6'1"</height> <age>58</age> <beard>neatly trimmed, matching his grey-streaked auburn hair</beard> <limp>slight limp in his left leg from an old knee injury</limp> <signetRing>silver signet ring, always worn on his right hand</signetRing> <description>Silas is a retired intelligence operative turned bar owner. He carries himself with quiet authority.</description> <backstory>Former MI6 field agent who retired after a botched operation in Prague left him with a permanent knee injury. He opened "The Raven's Nest" bar in Soho as a front for his network of contacts.</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> </codex> Continue the story using the following instructions: <instructions> Write a fully fleshed out scene of at least 1000 words in the literary fiction genre. Two old friends who haven't seen each other in years run into each other unexpectedly at a bar. One of them has changed significantly. Through their conversation and the tension between who they were and who they've become, reveal the weight of time and unspoken regret. 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 Raven’s Nest hummed with the low murmur of conversation, the clink of glasses, the occasional burst of laughter that rose and fell like a tide. Rory wiped her hands on her apron, balancing a tray of empty pint glasses against her hip as she weaved through the crowd. The green neon sign outside cast a sickly glow over the faces of the patrons, turning them into shifting silhouettes of gold and shadow. She didn’t see him at first. Then the crowd parted, just for a second, and there he was—leaning against the bar, one elbow propped on the polished wood, a half-empty glass of whiskey in front of him. His auburn hair was streaked with more grey than she remembered, his beard neatly trimmed but threaded with silver. The signet ring on his right hand caught the light as he lifted his glass, slow and deliberate. Silas. Her breath hitched. The tray tilted in her grip. He turned his head, as if sensing the weight of her stare. Hazel eyes locked onto hers, and for a moment, neither of them moved. The noise of the bar faded into a dull roar, the way it did when you were underwater. Then he smiled. Not the easy, familiar grin she remembered, but something tighter, edged with something she couldn’t name. “Rory Carter,” he said, voice rough as gravel. “Look at you.” She set the tray down with a clatter. “Silas Blackwood. Still haunting this place, I see.” “Someone’s got to keep the lights on.” He gestured to the stool beside him. “Sit. Unless you’re too busy playing delivery girl.” She bristled. “I’m not *playing* at anything.” His eyebrows lifted, just a fraction. “No? Then what do you call it?” She opened her mouth, then shut it. The old Rory would’ve snapped back, would’ve matched his tone with something sharp and cutting. But the old Rory wasn’t here anymore. Neither was the old Silas, apparently. She slid onto the stool. “I call it paying rent.” He studied her for a long moment, then pushed his glass toward her. “Drink?” She hesitated, then took it. The whiskey burned going down, but she didn’t flinch. “You look different.” “So do you.” His gaze flicked to the crescent scar on her wrist before meeting her eyes again. “London suits you.” “Does it?” She swirled the whiskey in the glass. “I didn’t think you’d notice.” A muscle twitched in his jaw. “I notice plenty.” The air between them thickened, charged with all the things they weren’t saying. The last time they’d seen each other, she’d been eighteen, fresh out of Cardiff, wide-eyed and full of fire. He’d been the one to tell her to go, to take Eva’s offer and run. *Don’t look back*, he’d said. And she hadn’t. Until now. “How’s the knee?” she asked, nodding at his left leg. He shrugged. “Still limps when it rains.” “And the rest of you?” His fingers tightened around his glass. “Still here.” She exhaled through her nose. “That’s not what I meant.” “I know what you meant.” He leaned forward, just enough that she could see the lines around his eyes, the ones that hadn’t been there before. “You want to know if I’m still the same man who told you to leave. If I regret it.” Her pulse jumped. “Do you?” He didn’t answer right away. Instead, he signaled the bartender for another drink. When it arrived, he took a slow sip, then set the glass down with careful precision. “Regret’s a heavy word,” he said finally. “I don’t waste time on things I can’t change.” “That’s not an answer.” “No,” he agreed. “But it’s the only one you’re getting.” She wanted to press him, to dig until he gave her something real. But the set of his shoulders, the way his jaw clenched—she knew that look. He wasn’t going to break. She changed tack. “You ever hear from Eva?” His expression shuttered. “Not in years.” “Me neither.” She traced the rim of her glass. “Funny how that works, isn’t it? People just… disappear.” “Not funny,” he said quietly. “Just life.” The weight of it settled between them, heavy and unspoken. She thought of all the nights she’d lain awake, wondering if she’d made the right choices. If she’d left too much behind. Silas reached into his pocket and pulled out a folded piece of paper. He slid it across the bar toward her. “Found this in the back room a few weeks ago. Thought you might want it.” She unfolded it carefully. It was a photograph—her and Eva, arms slung around each other’s shoulders, grinning like idiots in front of the Nest. She couldn’t have been older than seventeen. Her hair was shorter then, her smile brighter. She ran her thumb over the crease in the photo. “Where’d you get this?” “You left it behind.” His voice was rough. “When you left.” She swallowed. “I didn’t think you’d keep it.” “I didn’t think you’d come back.” She looked up at him, really looked at him. The grey in his hair, the lines on his face, the way his hands—once steady and sure—now trembled just slightly around his glass. “Neither did I,” she admitted. He didn’t say anything. He didn’t have to. The bar hummed around them, alive and indifferent. Somewhere in the distance, a glass shattered. Rory folded the photo and tucked it into her pocket. Silas watched her, his hazel eyes unreadable. She stood. “I should get back to work.” He nodded, once. “Of course.” She hesitated, just for a second. Then she turned and walked away, the weight of his gaze on her back the whole time.
created_at"2026-02-20T14:23:09.441Z" 2/20/2026, 2:23:09 PM
model"mistralai/mistral-large-2512" 1/1/2512, 12:00:00 AM
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency438
moderation_latency(null)
generation_time24725
tokens_prompt1094
tokens_completion1512
native_tokens_prompt1013
native_tokens_completion1284
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"
usage0.0024325
router(null)
provider_responses
0
endpoint_id"fa718841-07ea-4b7e-b8a3-6069545c7e6a"
id"e3adc6de016c4eb9b8cf35de3fdc79ff"
is_byokfalse
latency438
model_permaslug"mistralai/mistral-large-2512" 1/1/2512, 12:00:00 AM
provider_name"Mistral"
status200
api_type"completions"
id"gen-1771597389-yziljuN3tsyHJ2HDPIda"
upstream_id"e3adc6de016c4eb9b8cf35de3fdc79ff"
total_cost0.0024325
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
totalTags11
adverbTagCount2
adverbTags
0"he said finally [finally]"
1"he said quietly [quietly]"
dialogueSentences43
tagDensity0.256
leniency0.512
rawRatio0.182
effectiveRatio0.093
84.23% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount951
totalAiIsmAdverbs3
found
0
adverb"carefully"
count1
1
adverb"really"
count1
2
adverb"slightly"
count1
highlights
0"carefully"
1"really"
2"slightly"
100.00% AI-ism character names
Target: 0 AI-default names (16 tracked, −20% each)
codexExemptions
0"Blackwood"
found(empty)
100.00% AI-ism location names
Target: 0 AI-default location names (33 tracked, −20% each)
codexExemptions(empty)
found(empty)
31.65% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount951
totalAiIsms13
found
0
word"weight"
count3
1
word"familiar"
count1
2
word"flicked"
count1
3
word"charged"
count1
4
word"pulse"
count1
5
word"jaw clenched"
count1
6
word"traced"
count1
7
word"unspoken"
count1
8
word"trembled"
count1
9
word"shattered"
count1
10
word"unreadable"
count1
highlights
0"weight"
1"familiar"
2"flicked"
3"charged"
4"pulse"
5"jaw clenched"
6"traced"
7"unspoken"
8"trembled"
9"shattered"
10"unreadable"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches1
maxInWindow1
found
0
label"jaw/fists clenched"
count1
highlights
0"jaw clenched"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences80
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences80
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences111
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen33
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans2
markdownWords4
totalWords944
ratio0.004
matches
0"playing"
1"Don’t look back"
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions15
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions14
wordCount736
uniqueNames6
maxNameDensity0.54
worstName"Rory"
maxWindowNameDensity1
worstWindowName"Rory"
discoveredNames
Raven1
Nest2
Rory4
Silas4
Cardiff1
Eva2
persons
0"Raven"
1"Nest"
2"Rory"
3"Silas"
4"Eva"
places
0"Cardiff"
globalScore1
windowScore1
55.66% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences53
glossingSentenceCount2
matches
0"as if sensing the weight of her stare"
1"the old Silas, apparently"
94.07% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches1
per1kWords1.059
wordCount944
matches
0"Not the easy, familiar grin she remembered, but something tighter, edged with something she couldn’t name"
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences111
matches
0"knew that look"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs52
mean18.15
std16.14
cv0.889
sampleLengths
073
16
268
31
49
543
619
711
816
922
107
1113
1235
1310
1414
1517
1621
1714
189
1955
202
2110
227
235
248
2510
2645
275
2829
2916
304
3110
3232
338
346
3518
367
3732
3836
3939
4014
4111
428
436
4432
455
468
4725
487
498
96.49% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences80
matches
0"was streaked"
1"been eighteen"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs129
matches
0"weren’t saying"
39.90% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount5
semicolonCount0
flaggedSentences4
totalSentences111
ratio0.036
matches
0"Then the crowd parted, just for a second, and there he was—leaning against the bar, one elbow propped on the polished wood, a half-empty glass of whiskey in front of him."
1"But the set of his shoulders, the way his jaw clenched—she knew that look."
2"It was a photograph—her and Eva, arms slung around each other’s shoulders, grinning like idiots in front of the Nest."
3"The grey in his hair, the lines on his face, the way his hands—once steady and sure—now trembled just slightly around his glass."
93.78% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount743
adjectiveStacks0
stackExamples(empty)
adverbCount35
adverbRatio0.047106325706594884
lyAdverbCount8
lyAdverbRatio0.010767160161507403
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences111
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences111
mean8.5
std6.26
cv0.737
sampleLengths
026
123
224
36
431
519
618
71
83
96
1012
1113
1218
133
1416
158
163
178
188
1914
208
212
225
236
247
257
2615
277
286
295
305
3113
321
335
349
353
3618
373
389
395
406
413
4213
4319
4415
455
463
472
4810
492
60.36% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats3
diversityRatio0.3783783783783784
totalSentences111
uniqueOpeners42
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount5
totalSentences72
matches
0"Then the crowd parted, just"
1"Then he smiled."
2"Instead, he signaled the bartender"
3"Somewhere in the distance, a"
4"Then she turned and walked"
ratio0.069
0.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount41
totalSentences72
matches
0"She didn’t see him at"
1"His auburn hair was streaked"
2"Her breath hitched."
3"He turned his head, as"
4"he said, voice rough as"
5"She set the tray down"
6"He gestured to the stool"
7"His eyebrows lifted, just a"
8"She opened her mouth, then"
9"She slid onto the stool."
10"He studied her for a"
11"She hesitated, then took it."
12"His gaze flicked to the"
13"She swirled the whiskey in"
14"He’d been the one to"
15"she asked, nodding at his"
16"His fingers tightened around his"
17"She exhaled through her nose."
18"He leaned forward, just enough"
19"Her pulse jumped."
ratio0.569
43.33% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount60
totalSentences72
matches
0"The Raven’s Nest hummed with"
1"Rory wiped her hands on"
2"The green neon sign outside"
3"She didn’t see him at"
4"His auburn hair was streaked"
5"The signet ring on his"
6"Her breath hitched."
7"The tray tilted in her"
8"He turned his head, as"
9"Hazel eyes locked onto hers,"
10"The noise of the bar"
11"he said, voice rough as"
12"She set the tray down"
13"He gestured to the stool"
14"His eyebrows lifted, just a"
15"She opened her mouth, then"
16"The old Rory would’ve snapped"
17"Neither was the old Silas,"
18"She slid onto the stool."
19"He studied her for a"
ratio0.833
69.44% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount1
totalSentences72
matches
0"If she’d left too much"
ratio0.014
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences26
technicalSentenceCount1
matches
0"He turned his head, as if sensing the weight of her stare."
79.55% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags11
uselessAdditionCount1
matches
0"he said, voice rough as gravel"
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags6
fancyCount2
fancyTags
0"he agreed (agree)"
1"she admitted (admit)"
dialogueSentences43
tagDensity0.14
leniency0.279
rawRatio0.333
effectiveRatio0.093
84.9487%