Run: 2026-02-26-01KJCRX8F6WH3E6HN1GDHP2FF7

Run Details

Model:
LFM2 24B
Duration:
21.9s
Cost:
$0.000182
Provider:
Together

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
Rain fell in sheets across the cracked pavement. Streetlights blurred into smears of gold behind Detective Harlow Quinn’s goggles. She ran hard, boots splashing through puddles that reflected the neon glow of The Raven’s Nest sign. Her leather watch ticked against her wrist, counting seconds like a metronome. Behind her, footsteps echoed. A shadow darted between alleyways. Harlow didn’t slow. She’d chased this man before—three years ago, when DS Morris vanished. The memory of his last scream still haunted her dreams. “Come on,” she muttered, breath fogging in the cold air. “You’re not getting away this time.” The suspect turned a corner, ducked under a tattered awning, and slipped through a narrow door marked with a faded sign: Veil Market. Harlow hesitated only a heartbeat. The air inside smelled of damp earth and something sharper—like ozone before lightning. The market sprawled beneath the city, lit by flickering lanterns strung between crumbling brick walls. Stalls lined the narrow passageways, each crowded with oddities. A woman with eyes like polished obsidian offered a vial of swirling mist. A man in a moth-eaten coat haggled over a bundle of dried herbs. The place hummed with secrets. Harlow’s hand drifted to her holster. She scanned the crowd. No one looked up. The suspect disappeared deeper into the maze. “Where are you going?” she called, voice cutting through the murmur. A figure emerged from the shadows. Tall, lean, scarred forearm visible beneath a sleeve. He wore a Saint Christopher medallion that caught the light. “Just looking,” he replied, tone even. “You should go back. This isn’t your place.” Harlow stepped forward. “I’m not leaving until I know what you’re hiding.” He tilted his head. “You ever wonder why they call it the Veil? It’s not about hiding. It’s about choosing who sees what.” Her gaze flicked to the medallion. “You’re not from around here.” “Neither are you,” he countered. “But here we both are.” Rain drummed on the corrugated roof above. Somewhere distant, thunder growled. Harlow’s pulse thrummed in her ears. She could feel the weight of her watch, the grip of her gun. “Tell me,” she pressed, “what did you do with Morris?” The man’s smile was thin. “You think I’d talk about him? He wasn’t just a partner. He was family.” A sudden commotion erupted—a shout, a clatter of glass. The crowd scattered. Harlow moved instinctively toward the source. The suspect vanished again, weaving through stalls. She followed, boots splashing through shallow pools. The air thickened with incense and smoke. A stall displayed jars filled with glowing insects. Another sold folded papers sealed with wax. “Stop!” Harlow shouted. “Show me what you’ve got!” The man laughed softly. “You want answers? Look closer.” He gestured toward a stack of papers stacked beside a rusted lantern. Harlow approached. The pages were covered in symbols—some familiar, others alien. One symbol matched the scar on his forearm. “What does it mean?” she demanded. He leaned in. “It means I know things you don’t. Things that could change everything.” A flicker of movement caught her eye. A figure slipped behind a curtain of hanging cloth. Harlow drew her weapon. The suspect turned, eyes narrowing. “You can’t make me talk,” he said quietly. “Not here.” The rain outside intensified. Water dripped from the awning above. Harlow’s knuckles whitened on the grip. She could feel the tension coiling like a spring. “Then we’ll see who breaks first,” she replied. The man reached for a drawer beneath the counter. Inside lay a small box. He opened it slowly. Inside lay a photograph—Morris, smiling, standing beside someone Harlow didn’t recognize. The man’s face was obscured by shadow. “Who is that?” she asked. The suspect’s expression hardened. “Someone you’ll regret asking about.” Harlow stepped closer. The air crackled. She saw the glint of metal in his hand—a knife, perhaps. Or something worse. “Give it up,” she warned. He held the photo steady. “You want to know the truth? Then step back.” Lightning flashed outside. For a moment, the entire market was bathed in white. In that instant, Harlow saw it—the reflection in the window behind him. A figure watching from the shadows. The man in the photograph smiled. Harlow’s breath caught. She turned sharply. The suspect was gone. Only the photograph remained, trembling slightly in his grasp. The rain fell harder now. The market seemed to close in. Harlow’s watch ticked louder. She knew she had to decide—follow the trail deeper or turn back. Behind her, footsteps echoed. Not hers. Someone else. She tightened her grip on her weapon. The choice hung heavy in the air. “Come out,” she called. “Or I’ll make you.” Silence answered. Then, a whisper carried on the wind. “You shouldn’t have come here.” Harlow’s heart pounded. She took a step forward. The lantern light flickered. Shadows stretched long across the floor. And then, from the darkness ahead, a hand reached out. The chase wasn’t over. It had only just begun.
created_at"2026-02-26T10:46:27.398Z" 2/26/2026, 10:46:27 AM
model"liquid/lfm-2-24b-a2b-20260224"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency289
moderation_latency(null)
generation_time21788
tokens_prompt1140
tokens_completion1402
native_tokens_prompt1071
native_tokens_completion1245
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"
usage0.00018153
router(null)
provider_responses
0
endpoint_id"b5f259c3-1e77-40d6-8fa4-161cdf0e4a7c"
id"oYuezbJ-2j9zxn-9d3edbd555249227"
is_byokfalse
latency289
model_permaslug"liquid/lfm-2-24b-a2b-20260224"
provider_name"Together"
status200
api_type"completions"
id"gen-1772102787-nEniArQpFVyYfJsUF4zM"
upstream_id"oYuezbJ-2j9zxn-9d3edbd555249227"
total_cost0.00018153
cache_discount(null)
upstream_inference_cost0
provider_name"Together"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags12
adverbTagCount1
adverbTags
0"he said quietly [quietly]"
dialogueSentences27
tagDensity0.444
leniency0.889
rawRatio0.083
effectiveRatio0.074
75.87% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount829
totalAiIsmAdverbs4
found
0
adverb"softly"
count1
1
adverb"slowly"
count1
2
adverb"sharply"
count1
3
adverb"slightly"
count1
highlights
0"softly"
1"slowly"
2"sharply"
3"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)
wordCount829
totalAiIsms17
found
0
word"footsteps"
count2
1
word"echoed"
count2
2
word"scanned"
count1
3
word"flicked"
count1
4
word"pulse"
count1
5
word"could feel"
count2
6
word"weight"
count1
7
word"familiar"
count1
8
word"flicker"
count1
9
word"tension"
count1
10
word"glint"
count1
11
word"silence"
count1
12
word"whisper"
count1
13
word"flickered"
count1
highlights
0"footsteps"
1"echoed"
2"scanned"
3"flicked"
4"pulse"
5"could feel"
6"weight"
7"familiar"
8"flicker"
9"tension"
10"glint"
11"silence"
12"whisper"
13"flickered"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches1
maxInWindow1
found
0
label"hung in the air"
count1
highlights
0"hung heavy in the air"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences104
matches(empty)
87.91% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount3
hedgeCount1
narrationSentences104
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)
analyzedSentences119
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen23
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords822
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions13
unquotedAttributions0
matches(empty)
22.37% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions30
wordCount666
uniqueNames12
maxNameDensity2.55
worstName"Harlow"
maxWindowNameDensity3.5
worstWindowName"Harlow"
discoveredNames
Detective1
Harlow17
Quinn1
Raven1
Nest1
Morris2
Veil1
Market1
Saint1
Christopher1
Rain2
Silence1
persons
0"Harlow"
1"Quinn"
2"Raven"
3"Morris"
4"Market"
5"Saint"
6"Christopher"
7"Rain"
8"Silence"
places(empty)
globalScore0.224
windowScore0.5
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences54
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount822
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences119
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs45
mean18.27
std11.76
cv0.644
sampleLengths
048
133
216
341
455
521
611
724
814
912
1023
1111
1210
1330
1410
1519
1625
1729
188
199
2031
216
2215
2325
2410
2525
268
2736
285
299
3020
315
3214
3331
346
3519
3627
378
3814
398
409
415
4218
4310
449
91.77% Passive voice overuse
Target: ≤2% passive sentences
passiveCount4
totalSentences104
matches
0"were covered"
1"was obscured"
2"was bathed"
3"was gone"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs132
matches(empty)
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount8
semicolonCount0
flaggedSentences8
totalSentences119
ratio0.067
matches
0"She’d chased this man before—three years ago, when DS Morris vanished."
1"The air inside smelled of damp earth and something sharper—like ozone before lightning."
2"A sudden commotion erupted—a shout, a clatter of glass."
3"The pages were covered in symbols—some familiar, others alien."
4"Inside lay a photograph—Morris, smiling, standing beside someone Harlow didn’t recognize."
5"She saw the glint of metal in his hand—a knife, perhaps."
6"In that instant, Harlow saw it—the reflection in the window behind him."
7"She knew she had to decide—follow the trail deeper or turn back."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount674
adjectiveStacks0
stackExamples(empty)
adverbCount17
adverbRatio0.025222551928783383
lyAdverbCount9
lyAdverbRatio0.013353115727002967
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences119
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences119
mean6.91
std3.69
cv0.534
sampleLengths
08
111
217
312
44
55
63
711
810
910
106
1123
125
1313
1415
159
1613
1713
185
196
204
214
227
2311
246
258
2610
276
288
293
309
314
3219
336
345
355
365
377
384
396
4013
4110
425
4314
449
453
466
477
487
497
55.46% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats4
diversityRatio0.35294117647058826
totalSentences119
uniqueOpeners42
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount3
totalSentences92
matches
0"Somewhere distant, thunder growled."
1"Only the photograph remained, trembling"
2"Then, a whisper carried on"
ratio0.033
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount24
totalSentences92
matches
0"She ran hard, boots splashing"
1"Her leather watch ticked against"
2"She’d chased this man before—three"
3"she muttered, breath fogging in"
4"She scanned the crowd."
5"she called, voice cutting through"
6"He wore a Saint Christopher"
7"he replied, tone even"
8"He tilted his head."
9"Her gaze flicked to the"
10"She could feel the weight"
11"She followed, boots splashing through"
12"He gestured toward a stack"
13"He leaned in."
14"he said quietly"
15"She could feel the tension"
16"He opened it slowly."
17"She saw the glint of"
18"He held the photo steady."
19"She turned sharply."
ratio0.261
36.09% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount78
totalSentences92
matches
0"Rain fell in sheets across"
1"Streetlights blurred into smears of"
2"She ran hard, boots splashing"
3"Her leather watch ticked against"
4"A shadow darted between alleyways."
5"Harlow didn’t slow."
6"She’d chased this man before—three"
7"The memory of his last"
8"she muttered, breath fogging in"
9"The suspect turned a corner,"
10"Harlow hesitated only a heartbeat."
11"The air inside smelled of"
12"The market sprawled beneath the"
13"Stalls lined the narrow passageways,"
14"A woman with eyes like"
15"A man in a moth-eaten"
16"The place hummed with secrets."
17"Harlow’s hand drifted to her"
18"She scanned the crowd."
19"The suspect disappeared deeper into"
ratio0.848
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences92
matches(empty)
ratio0
74.83% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences21
technicalSentenceCount2
matches
0"She ran hard, boots splashing through puddles that reflected the neon glow of The Raven’s Nest sign."
1"He wore a Saint Christopher medallion that caught the light."
41.67% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags12
uselessAdditionCount2
matches
0"she muttered, breath fogging in the cold air"
1"she called, voice cutting through the murmur"
0.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags11
fancyCount5
fancyTags
0"she muttered (mutter)"
1"she pressed (press)"
2"Harlow shouted (shout)"
3"she demanded (demand)"
4"she warned (warn)"
dialogueSentences27
tagDensity0.407
leniency0.815
rawRatio0.455
effectiveRatio0.37
76.1991%