Run: 2026-02-25-01KJBEYWC3R94C70802AEXRV55

Run Details

Duration:
21.1s
Cost:
$0.005590
Provider:
Alibaba

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="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> 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.

Model response

AI
The rain came down in iron sheets, washing the grime from the soot-stained brick of Soho until the pavement gleamed like black glass. Harlow Quinn felt the cold seeping through her trench coat, but she did not pull the collar up. She didn't have the time. Her boots hit the wet asphalt with a rhythmic slap, a military march that cut through the chaotic hiss of London traffic. She scanned the shadows, her brown eyes darting, trained on the figure sprinting fifty yards ahead. The suspect was a courier, a man she had been trailing for three days, ever since the surveillance cameras in Camden failed to capture him entering the zone. He turned a corner, disappearing behind a row of parked vans, and Quinn kicked her heels to increase her stride. She checked her watch. The leather strap on her left wrist was worn soft, the face scuffed, but the hands ticked with relentless precision. 2:14 AM. The city was a graveyard, the silence heavy enough to crush lungs. Quinn was forty-one, her salt-and-pepper hair plastered against her skull, but her bearing remained rigid, a ghost of the discipline that had kept her alive for eighteen years. She gripped the butt of her service pistol, the grip familiar and grounding. She wasn't thinking about her pension. She was thinking about the empty chair at the station where DS Morris used to sit. Three years. Three years since they dragged him out of a alleyway that looked like this, but without the rain. The courier slipped into a narrow alleyway behind a defunct laundromat. Quinn closed the gap, her breath coming in short, white plumes. The alley ended abruptly at a crumbling brick wall, no door, no window, just dead end. Yet, the courier had vanished. She stopped at the wall, her heart hammering against her ribs. She reached out, her fingers brushing the rough mortar. There, behind a rusted fire escape ladder, was a seam in the brickwork that wasn't mortar. It hummed. A low vibration traveled up her arm and settled in her teeth. She looked down. In the wet gutter, caught in the runoff, lay a bone. It was bleached white, carved with runes that hurt her eyes if she looked at them too long. A token. She reached for it, her gloved hand closing around the cold object. It fit perfectly in her palm. This was the only way in. The Veil Market. The rumor mills at the station had called it a myth, a collection of urban legends spun by drunks and addicts who claimed to see things they weren't. Quinn knew better. She had found the body of a grimoire seller last month, dead of a heart attack that looked like magic. She stepped through the seam. The brickwork dissolved like mist. The air changed instantly. The smell of wet asphalt and diesel vanished, replaced by the scent of ozone, old parchment, and burning sage. The light was different here. There were no streetlamps, only floating orbs of green and violet that drifted through the gloom like trapped fireflies. She was in a cavernous space, the high vaulted ceiling lost in darkness. The architecture was Victorian, the iron ribs of the Tube station exposed and twisted, draped in ivy that pulsed with a faint, bioluminescent heartbeat. This was Camden
created_at"2026-02-25T22:33:20.526Z" 2/25/2026, 10:33:20 PM
model"qwen/qwen3.5-35b-a3b-20260224"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency480
moderation_latency(null)
generation_time21012
tokens_prompt914
tokens_completion2681
native_tokens_prompt914
native_tokens_completion2681
native_tokens_completion_images(null)
native_tokens_reasoning1880
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(null)
native_finish_reason(null)
usage0.0055905
router(null)
provider_responses
0
endpoint_id"48c1210f-b7d2-4688-a340-f7424fc99d2b"
id"chatcmpl-54b5125f-4221-9e6c-bbbc-ed1f5d0ebf3b"
is_byokfalse
latency480
model_permaslug"qwen/qwen3.5-35b-a3b-20260224"
provider_name"Alibaba"
status200
api_type"completions"
id"gen-1772058800-62KDkkdbGOYXW8kFdU8P"
upstream_id"chatcmpl-54b5125f-4221-9e6c-bbbc-ed1f5d0ebf3b"
total_cost0.0055905
cache_discount(null)
upstream_inference_cost0
provider_name"Alibaba"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags0
90.99% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount555
totalAiIsmAdverbs1
found
0
adverb"perfectly"
count1
highlights
0"perfectly"
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)
36.94% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount555
totalAiIsms7
found
0
word"rhythmic"
count1
1
word"chaotic"
count1
2
word"scanned"
count1
3
word"silence"
count1
4
word"familiar"
count1
5
word"gloom"
count1
6
word"pulsed"
count1
highlights
0"rhythmic"
1"chaotic"
2"scanned"
3"silence"
4"familiar"
5"gloom"
6"pulsed"
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
narrationSentences46
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences46
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences46
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen31
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords555
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions1
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions14
wordCount555
uniqueNames9
maxNameDensity0.9
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Quinn"
discoveredNames
Soho1
Quinn5
London1
Camden2
Morris1
Veil1
Market1
Victorian1
Tube1
persons
0"Quinn"
1"Morris"
places
0"Soho"
1"London"
globalScore1
windowScore1
0.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences31
glossingSentenceCount2
matches
0"looked like this, but without the rain"
1"looked like magic"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount555
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences46
matches(empty)
80.63% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs6
mean92.5
std39.98
cv0.432
sampleLengths
0132
1121
293
3112
410
587
97.64% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences46
matches
0"was bleached"
51.85% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount2
totalVerbs90
matches
0"wasn't thinking"
1"was thinking"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences46
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount559
adjectiveStacks0
stackExamples(empty)
adverbCount14
adverbRatio0.025044722719141325
lyAdverbCount5
lyAdverbRatio0.008944543828264758
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences46
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences46
mean12.07
std7.82
cv0.648
sampleLengths
023
118
25
322
416
528
620
74
820
92
1012
1128
1213
136
1416
152
1618
1711
1811
1916
205
2111
229
2316
242
2512
263
2711
2818
292
3012
316
326
333
3428
353
3620
375
385
394
4019
415
4219
4313
4424
453
31.16% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats9
diversityRatio0.30434782608695654
totalSentences46
uniqueOpeners14
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences42
matches(empty)
ratio0
58.10% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount17
totalSentences42
matches
0"She didn't have the time."
1"Her boots hit the wet"
2"She scanned the shadows, her"
3"He turned a corner, disappearing"
4"She checked her watch."
5"She gripped the butt of"
6"She wasn't thinking about her"
7"She was thinking about the"
8"She stopped at the wall,"
9"She reached out, her fingers"
10"She looked down."
11"It was bleached white, carved"
12"She reached for it, her"
13"It fit perfectly in her"
14"She had found the body"
15"She stepped through the seam."
16"She was in a cavernous"
ratio0.405
7.62% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount38
totalSentences42
matches
0"The rain came down in"
1"Harlow Quinn felt the cold"
2"She didn't have the time."
3"Her boots hit the wet"
4"She scanned the shadows, her"
5"The suspect was a courier,"
6"He turned a corner, disappearing"
7"She checked her watch."
8"The leather strap on her"
9"The city was a graveyard,"
10"Quinn was forty-one, her salt-and-pepper"
11"She gripped the butt of"
12"She wasn't thinking about her"
13"She was thinking about the"
14"The courier slipped into a"
15"Quinn closed the gap, her"
16"The alley ended abruptly at"
17"She stopped at the wall,"
18"She reached out, her fingers"
19"There, behind a rusted fire"
ratio0.905
100.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount1
totalSentences42
matches
0"Yet, the courier had vanished."
ratio0.024
63.49% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences27
technicalSentenceCount3
matches
0"Quinn was forty-one, her salt-and-pepper hair plastered against her skull, but her bearing remained rigid, a ghost of the discipline that had kept her alive for…"
1"There, behind a rusted fire escape ladder, was a seam in the brickwork that wasn't mortar."
2"There were no streetlamps, only floating orbs of green and violet that drifted through the gloom like trapped fireflies."
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
80.6136%