NC Bench
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Run: 2026-02-17-01KHNFQSWH853F6Q3GVDR86S69
Run Details
Model:
DeepSeek V3.1
Test:
Bad Writing Habits
Scenario:
Literary fiction: old friends reunite
Duration:
2m 34s
Cost:
$0.001749
Provider:
DeepInfra
Evaluation summary
Score
Evaluator
Details Link
38.89%
Adverbs in dialogue tags
(details)
86.51%
AI-ism adverb frequency
(details)
100.00%
AI-ism character names
(details)
100.00%
AI-ism location names
(details)
32.57%
AI-ism word frequency
(details)
66.67%
Cliché density
(details)
100.00%
Emotion telling (show vs. tell)
(details)
100.00%
Filter word density
(details)
100.00%
Gibberish response detection
(details)
100.00%
Markdown formatting overuse
(details)
100.00%
Missing dialogue indicators (quotation marks)
(details)
100.00%
Name drop frequency
(details)
59.64%
Narrator intent-glossing
(details)
100.00%
"Not X but Y" pattern overuse
(details)
100.00%
Overuse of "that" (subordinate clause padding)
(details)
100.00%
Paragraph length variance
(details)
89.02%
Passive voice overuse
(details)
44.24%
Past progressive (was/were + -ing) overuse
(details)
46.82%
Em-dash & semicolon overuse
(details)
94.00%
Purple prose (modifier overload)
(details)
100.00%
Repeated phrase echo
(details)
100.00%
Sentence length variance
(details)
37.25%
Sentence opener variety
(details)
0.00%
Adverb-first sentence starts
(details)
0.00%
Pronoun-first sentence starts
(details)
9.02%
Subject-first sentence starts
(details)
49.02%
Subordinate conjunction sentence starts
(details)
100.00%
Technical jargon density
(details)
32.41%
Useless dialogue additions
(details)
41.89%
Dialogue tag variety (said vs. fancy)
(details)
70.9315%
Overall
Model input
System
You are an expert novelist with a gift for vivid, immersive prose. You write in tight third-person limited point of view with a strong narrative voice. Your prose is clean, precise, and engaging — you favor strong verbs, concrete details, and natural-sounding dialogue. You will be given a story bible (codex) containing canonical details about characters, locations, items, and lore. Use these details to ground your writing in the established world. Stay consistent with the codex. Write only the prose of the scene. Do not include titles, chapter headings, markdown formatting, or any commentary before or after the scene.
User/Human
<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> 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.
Model response
Messages
Meta
AI
The green neon sign hummed
softly
above the door, casting an
otherworldly
glow on the damp Soho pavement. *
The Raven’s Nest
*.
Rory pushed the heavy door
open, the
familiar
scent of old wood, spilled ale, and Silas’s particular brand of lemon polish washing over her.
It was a smell that
had come to mean safety.
The bar was quiet for
a Thursday. A few regulars hunched over their drinks, islands of solitude in the dim light.
The maps and grainy photographs
on the walls
seemed to
lean in, listening to the low murmur of conversation. Behind the bar, Silas
was polishing
a glass with a slow,
methodical
rhythm, his silver signet ring catching the light with each pass. He gave her a slight, almost imperceptible nod, his hazel eyes crinkling at the corners.
His limp was more pronounced
tonight; the damp weather always made the old knee injury ache.
She slid onto her usual
stool at the far end, the one with a clear view of both exits. A habit she’d picked up, one Silas had quietly encouraged. “The usual, Rory?”
he asked, his voice a
low rumble. “Please, Si. Tough day.”
He nodded, reaching for a
bottle of a decent single malt without needing to look.
He poured two fingers into
a clean tumbler and pushed it across the polished wood. His movements were economical, precise, the ghost of the field agent he’d once been. As he turned to put the bottle away, the door opened again, bringing with it a gust of cool night air and the sound of London traffic.
Rory took a sip, letting
the whisky burn a warm path down her throat.
She stared at the crescent-shaped
scar on her left wrist, a pale little moon against her skin.
A relic from a time
when accidents were simple and the biggest worry
was explaining
a broken vase to her mother.
A man’s voice, hesitant and
oddly
familiar
, cut through her thoughts. “Excuse me, is this seat taken?”
She looked up, ready to
offer a polite, dismissive shake of her head, and the words died in her throat. It was Evan. But it wasn’t. Not the Evan she remembered. The man standing before her was a faded photograph of the one she’d known. His once-athletic frame had softened, gone
slightly
paunchy beneath a cheap, ill-fitting suit.
His hair, which he’d always
been so vain about,
was thinning
at the crown. Lines of stress
were carved
deep around his mouth and eyes. He looked… diminished. Worn down by years she hadn’t been there to see.
He was staring at her
with a mix of shock and dawning recognition. “Laila?”
he breathed, the old pet
name sounding foreign and clumsy on his tongue now. He’d always called her that, a name he’d chosen himself, saying ‘Aurora’ was too much of a mouthful.
She’d hated it, but had
let it slide, one of a thousand small concessions. “Rory,”
she corrected, her voice cooler
than she intended. “It’s Rory.”
She saw the flinch, the
tiny wound the correction inflicted. Good. “Rory. Right. God, it’s… it’s been years.”
He hovered awkwardly, his hands
fluttering as if unsure what to do with themselves. Silas had stopped polishing his glass. He
was watching
them, his expression
unreadable
, but his posture had shifted subtly. He was no longer just a bartender; he was a
sentinel
. “It has,” Rory said. She didn’t offer the stool. She didn’t offer anything. He took the
silence
as an invitation and sat down anyway, the wood groaning under his
weight
. He smelled of stale cigarette smoke and cheap aftershave, a far cry from the expensive cologne he used to wear. “I can’t believe it’s you,” he said, shaking his head. “What are you doing in London? Last I heard, you were in Cardiff. Pre-Law, right? Following in your dad’s
footsteps
.” The past
felt like a physical weight pressing on
her chest. “Plans change.” “Yeah,”
he said, a hollow laugh escaping him
. “Tell me about it.” He signaled to Silas for a pint of whatever was on tap. When it arrived, he took a long, grateful pull. “So. What do you do now? You look… good.” His eyes
scanned
her, and she felt a
familiar
, unwelcome prickle of assessment. She
was dressed
in her delivery gear—dark jeans, a
comfortable
jumper, practical boots. She looked capable. She
looked like she could run if she had to
. “I deliver food,” she said flatly. He blinked,
as if waiting for the punchline
. “You… deliver food? For a living?” The disbelief
was tinged
with a condescension that was so intrinsically *
him
* that for a moment, the years melted away and she was twenty again, sitting across from
him
in a café, listening to
him
pick apart her career choices. “It pays the rent. The flat’s just upstairs.” She gestured vaguely toward the ceiling. His
eyes widened
. “You live *
here
*? Above a pub?” He said it like she’d told
him
she lived in a sewer. The Evan she’d known had
been obsessed
with image, with the right postcode, the right car, the right friends. This place, with its worn
charm
and secret histories, would have been his idea of hell. “It’s a bar. And it’s quiet.” She took another sip of whisky, her bright blue eyes fixed on
him
over the rim of the glass. “What about you, Evan? Still in finance? Driving the Beamer?” The question
hung in the air
, sharp and pointed. He looked down into his pint, his shoulders slumping. The bravado drained out of
him
, leaving behind something raw and defeated. “The firm… there were cutbacks. After the crash. I’m in sales now. Office supplies. It’s… it’s a living.” He couldn’t meet her gaze. “Sold the car.” The
silence
stretched between them, thick with everything unsaid. The memories of slammed doors, of cruel words meant to wound, of the constant, exhausting effort to mould herself into someone he wouldn’t be ashamed of. The frantic, terrified phone call to Eva. The train ticket to London bought with trembling hands. The relief of disappearing into a city where he would never think to look for her. He finally looked up, his eyes searching her face. “You just… left. You changed your number. Blocked me everywhere. I didn’t… I never got to apologize.” The word ‘apologize’ felt too small, too flimsy a bridge to span the chasm he’d created. “Some things don’t need an apology, Evan. They just need to be over.” “I was a bastard,” he said, the words rushing out as if he’d been rehearsing them for years. “I know I was. I was jealous, and insecure, and I took it all out on you. I thought… I thought if I could make you feel small, you’d never leave.” He gave a bitter, choked laugh. “Turns out that’s not how it works.” Rory said nothing. She just watched
him
, this ghost from a life she’d worked hard to escape. The anger was still there, a cold, hard stone in her gut, but it
was covered
in layers of time and distance.
Now, looking at this broken
man in his cheap suit, she mostly felt a
profound
, weary sadness. “I heard you were happy,” he mumbled into his beer. “I asked around, once. Someone said you’d moved on. That you were doing well.” “I am,” she said, and for the first time, she meant it. The delivery job, the flat above the bar, the quiet mentorship of Silas—it was a life she had built herself, brick by careful brick. It was hers. He finished his pint and set the glass down with a finality. He looked older than his years, weighed down by regrets he’d carried for far too long. “I should go,”
he said, not moving
. Rory just nodded. He stood up, fumbling for his wallet. Silas, from his post at the other end of the bar, shook his head once. “On the house,”
he said, his voice leaving no room for argument
. Evan looked startled, then grateful. “Thanks.” He turned back to Rory. “It was… good to see you, Aurora. Really.” He used her full name, a peace offering. “Take care of yourself, Evan.” He gave a jerky nod and walked out, the door swinging shut behind
him
, sealing the bar back into its hushed, amber-lit world. The
silence
he left behind was different now,
charged
with the
echoes
of a past finally laid to rest. Rory turned her glass
slowly
on the bar, watching the light play in the amber liquid. Silas limped over and refilled it without a word. He didn’t ask. He just waited. After a long moment, she looked up at
him
, her expression clear. “He’s nobody,” she said, and it was the truth. Silas gave her another of his slow, understanding nods. “I know,” he said. And that was the end of it.
created_at
"2026-02-17T09:46:14.617991+00:00"
2/17/2026, 9:46:14 AM
model
"deepseek/deepseek-chat-v3.1"
app_id
182717
external_user
(null)
streamed
true
cancelled
false
latency
675
moderation_latency
(null)
generation_time
154347
tokens_prompt
884
tokens_completion
2218
native_tokens_prompt
816
native_tokens_completion
1998
native_tokens_completion_images
(null)
native_tokens_reasoning
0
native_tokens_cached
5
num_media_prompt
(null)
num_input_audio_prompt
(null)
num_media_completion
0
num_search_results
(null)
origin
"https://ncbench.com/"
is_byok
false
finish_reason
"stop"
native_finish_reason
"stop"
usage
0.001749380000001
router
(null)
provider_responses
0
id
"cmpl-b40f991f90ec4c9da39e75b267b2ab37"
status
200
is_byok
false
latency
82
endpoint_id
"012f7d1f-23d4-4d03-9aa3-432d8ad82757"
provider_name
"DeepInfra"
model_permaslug
"deepseek/deepseek-chat-v3.1"
api_type
"completions"
id
"gen-1771321419-cBWQhsnD0tNkvDCYReBO"
upstream_id
"cmpl-b40f991f90ec4c9da39e75b267b2ab37"
total_cost
0.001749380000001
cache_discount
3.99999999e-7
upstream_inference_cost
0
provider_name
"DeepInfra"
Evaluation details
Result
Evaluator
Details
Meta Data
38.89%
Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags
27
adverbTagCount
6
adverbTags
0
"He’d always [always]"
1
"He hovered awkwardly [awkwardly]"
2
"she said flatly [flatly]"
3
"the years melted away [away]"
4
"She gestured vaguely [vaguely]"
5
"He turned back [back]"
dialogueSentences
37
tagDensity
0.73
leniency
1
rawRatio
0.222
effectiveRatio
0.222
86.51%
AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount
1483
totalAiIsmAdverbs
4
found
0
adverb
"softly"
count
1
1
adverb
"slightly"
count
1
2
adverb
"really"
count
1
3
adverb
"slowly"
count
1
highlights
0
"softly"
1
"slightly"
2
"really"
3
"slowly"
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)
32.57%
AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount
1483
totalAiIsms
20
found
0
word
"otherworldly"
count
1
1
word
"familiar"
count
3
2
word
"methodical"
count
1
3
word
"unreadable"
count
1
4
word
"sentinel"
count
1
5
word
"silence"
count
3
6
word
"weight"
count
2
7
word
"footsteps"
count
1
8
word
"scanned"
count
1
9
word
"comfortable"
count
1
10
word
"tinged"
count
1
11
word
"charm"
count
1
12
word
"profound"
count
1
13
word
"charged"
count
1
14
word
"echoes"
count
1
highlights
0
"otherworldly"
1
"familiar"
2
"methodical"
3
"unreadable"
4
"sentinel"
5
"silence"
6
"weight"
7
"footsteps"
8
"scanned"
9
"comfortable"
10
"tinged"
11
"charm"
12
"profound"
13
"charged"
14
"echoes"
66.67%
Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches
2
maxInWindow
2
found
0
label
"eyes widened/narrowed"
count
1
1
label
"hung in the air"
count
1
highlights
0
"eyes widened"
1
"hung in the air"
100.00%
Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells
0
narrationSentences
108
matches
(empty)
100.00%
Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount
0
hedgeCount
1
narrationSentences
108
filterMatches
(empty)
hedgeMatches
0
"seemed to"
100.00%
Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences
119
gibberishSentences
0
adjustedGibberishSentences
0
longSentenceCount
0
runOnParagraphCount
0
giantParagraphCount
0
wordSaladCount
0
repetitionLoopCount
0
controlTokenCount
0
maxSentenceWordsSeen
46
ratio
0
matches
(empty)
100.00%
Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans
3
markdownWords
5
totalWords
1477
ratio
0.003
matches
0
"The Raven’s Nest"
1
"him"
2
"here"
100.00%
Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions
21
unquotedAttributions
0
matches
(empty)
100.00%
Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions
27
wordCount
1212
uniqueNames
9
maxNameDensity
0.74
worstName
"Silas"
maxWindowNameDensity
2
worstWindowName
"Silas"
discoveredNames
Soho
1
Raven
1
Silas
9
Thursday
1
London
2
Evan
4
Good
1
Eva
1
Rory
7
persons
0
"Raven"
1
"Silas"
2
"Evan"
3
"Eva"
4
"Rory"
places
0
"Soho"
1
"London"
globalScore
1
windowScore
1
59.64%
Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences
83
glossingSentenceCount
3
matches
0
"felt like a physical weight pressing on"
1
"looked like she could run if she had to"
2
"as if waiting for the punchline"
100.00%
"Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches
0
per1kWords
0
wordCount
1477
matches
(empty)
100.00%
Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount
0
totalSentences
119
matches
(empty)
100.00%
Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs
41
mean
36.02
std
23.5
cv
0.652
sampleLengths
0
55
1
94
2
29
3
10
4
4
5
71
6
52
7
17
8
20
9
3
10
73
11
60
12
22
13
21
14
30
15
13
16
67
17
13
18
81
19
6
20
54
21
14
22
56
23
35
24
56
25
67
26
26
27
29
28
62
29
56
30
24
31
39
32
35
33
3
34
34
35
27
36
5
37
42
38
31
39
21
40
20
89.02%
Passive voice overuse
Target: ≤2% passive sentences
passiveCount
5
totalSentences
108
matches
0
"were carved"
1
"was dressed"
2
"was tinged"
3
"been obsessed"
4
"was covered"
44.24%
Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount
5
totalVerbs
214
matches
0
"was polishing"
1
"was explaining"
2
"was thinning"
3
"was staring"
4
"was watching"
46.82%
Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount
2
semicolonCount
2
flaggedSentences
4
totalSentences
119
ratio
0.034
matches
0
"His limp was more pronounced tonight; the damp weather always made the old knee injury ache."
1
"He was no longer just a bartender; he was a sentinel."
2
"She was dressed in her delivery gear—dark jeans, a comfortable jumper, practical boots."
3
"The delivery job, the flat above the bar, the quiet mentorship of Silas—it was a life she had built herself, brick by careful brick."
94.00%
Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount
1239
adjectiveStacks
1
stackExamples
0
"cheap, ill-fitting suit."
adverbCount
48
adverbRatio
0.0387409200968523
lyAdverbCount
14
lyAdverbRatio
0.011299435028248588
100.00%
Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences
119
echoCount
0
echoWords
(empty)
100.00%
Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences
119
mean
12.41
std
7.36
cv
0.593
sampleLengths
0
18
1
3
2
24
3
10
4
7
5
14
6
19
7
23
8
15
9
16
10
19
11
10
12
10
13
4
14
15
15
15
16
14
17
27
18
14
19
17
20
21
21
11
22
6
23
20
24
3
25
3
26
5
27
14
28
13
29
14
30
11
31
2
32
1
33
10
34
13
35
15
36
18
37
14
38
9
39
12
40
1
41
21
42
6
43
13
44
11
45
4
46
5
47
4
48
17
49
20
37.25%
Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats
19
diversityRatio
0.31932773109243695
totalSentences
119
uniqueOpeners
38
0.00%
Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount
0
totalSentences
102
matches
(empty)
ratio
0
0.00%
Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount
59
totalSentences
102
matches
0
"It was a smell that"
1
"He gave her a slight,"
2
"His limp was more pronounced"
3
"She slid onto her usual"
4
"he asked, his voice a"
5
"He nodded, reaching for a"
6
"He poured two fingers into"
7
"His movements were economical, precise,"
8
"She stared at the crescent-shaped"
9
"She looked up, ready to"
10
"It was Evan."
11
"His once-athletic frame had softened,"
12
"His hair, which he’d always"
13
"He was staring at her"
14
"he breathed, the old pet"
15
"He’d always called her that,"
16
"She’d hated it, but had"
17
"she corrected, her voice cooler"
18
"She saw the flinch, the"
19
"He hovered awkwardly, his hands"
ratio
0.578
9.02%
Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount
92
totalSentences
102
matches
0
"The green neon sign hummed"
1
"*The Raven’s Nest*."
2
"Rory pushed the heavy door"
3
"It was a smell that"
4
"The bar was quiet for"
5
"The maps and grainy photographs"
6
"He gave her a slight,"
7
"His limp was more pronounced"
8
"She slid onto her usual"
9
"A habit she’d picked up,"
10
"he asked, his voice a"
11
"He nodded, reaching for a"
12
"He poured two fingers into"
13
"His movements were economical, precise,"
14
"Rory took a sip, letting"
15
"She stared at the crescent-shaped"
16
"A relic from a time"
17
"A man’s voice, hesitant and"
18
"She looked up, ready to"
19
"It was Evan."
ratio
0.902
49.02%
Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount
1
totalSentences
102
matches
0
"Now, looking at this broken"
ratio
0.01
100.00%
Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences
49
technicalSentenceCount
0
matches
(empty)
32.41%
Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags
27
uselessAdditionCount
5
matches
0
"he asked, his voice a low rumble"
1
"she corrected, her voice cooler than she intended"
2
"he said, a hollow laugh escaping him"
3
"he said, not moving"
4
"he said, his voice leaving no room for argument"
41.89%
Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags
16
fancyCount
4
fancyTags
0
"he breathed (breathe)"
1
"He’d always (would)"
2
"she corrected (correct)"
3
"he mumbled (mumble)"
dialogueSentences
37
tagDensity
0.432
leniency
0.865
rawRatio
0.25
effectiveRatio
0.216
70.9315%