Run: 2026-07-25-01KYCX2B09PAYP201CSJT5WX1V

Run Details

Duration:
8.5s
Cost:
$0.005316
Provider:
BaseTen

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 needles, turning Soho into a smear of neon and blacktop. Detective Harlow Quinn had her collar turned up, her shoes already soaked through, and she was running. Not sprinting—she was forty-one, and her knees had opinions—but running with purpose, with that old familiar burn in her calves that meant she was still in this. The suspect was thirty yards ahead, a hooded figure in dark canvas, weaving through the Thursday-night crowd leaving the clubs and the late restaurants. He was fast. Young. Stupid enough to run, which meant he had something to hide, and smart enough to run away from the police rather than into them. Quinn kept her head up, tracking him past the green neon sign of The Raven’s Nest, past the bouncer with the neck tattoos, past the smell of fryer grease and cigarettes. She knew this ground. She’d walked these beats for eighteen years, and she knew every alley that dead-ended, every courtyard that emptied back into traffic. Then he turned. Not into an alley—into nothing. One second he was there, a blur of black against the wet brick, and the next he’d vanished down a stairwell she hadn’t seen, hidden behind a stack of rotting pallets and a dumpster that smelled like dead fish. Quinn skidded to a stop, her hand going automatically to her hip where her service weapon rested, though she didn’t draw it. Not yet. She listened. The rain masked sounds, but she caught it—footsteps, rapid, echoing downward, into some kind of tunnel or underground access. She approached the stairwell. The concrete steps were slick with moss and grease, descending into darkness that smelled of rust and old electricity. At the bottom, a single bulb flickered orange, illuminating a corridor that definitely was not part of any building she recognized. Quinn pulled out her phone. No signal. Of course. She glanced back up at street level. The rain was still falling. She could call for backup, wait, do this by the book. She’d done that for eighteen years, and it had gotten her partner killed three years ago in some warehouse where the darkness hadn’t been natural, where DS Morris had gone in after her and hadn’t come out, and the report said heart attack, and she knew that was a lie. Quinn started down the stairs. The corridor led to an old service tunnel, then to a gate that was rusted shut—except it wasn’t, not quite, leaving a gap wide enough to squeeze through. On the other side, she entered the abandoned Tube station. Camden. Or beneath it, anyway. The platform was there, crumbling, with tracks that led into blackness in both directions. The air was thick with damp and something else—something chemical, sweet and sharp, like incense burning over a chemical fire. And she was not alone. The market was open. Quinn stepped onto the platform and froze. The space was enormous, vaulted like a cathedral of grime, and it was filled with people. Or things that looked like people. Some wore long coats with hoods pulled low. Others moved with wrong proportions—too tall, too many joints, eyes that reflected the scattered lantern light like cats’. Stalls lined the old tracks, constructed from scrap wood and black iron, displaying goods she couldn’t identify: jars of liquid that glowed with internal colors, bones arranged in geometric patterns, books that seemed to breathe, turning their own pages in some draft she couldn’t feel. This was The Veil Market. She’d heard whispers for years—every detective in the Met heard them, filed them away with the other urban legends—but she’d never believed, not really, not until now. Her suspect was here, she was sure of it. She scanned the crowd, keeping to the wall, trying to make herself small. Then she saw him—fifty yards away, negotiating with a vendor who had no face, just a smooth oval of skin where features should be. The vendor was holding out a small object, bone-white, curved like a tooth but too large. Quinn moved closer, stepping over something that might have been a pool of water or might have been something alive. The crowd parted around her without seeming to notice, though she felt eyes on her, cold and assessing. She was close now, close enough to hear. The suspect—she still didn’t have a name—was speaking in a low voice, trading what looked like a stack of bills, though the paper was black and the denominations made no sense. Quinn reached for her radio. Dead, of course. She had a choice. She could retreat, call in the kind of forces that might actually understand this place—if any existed, if they wouldn’t just cover it up like they covered up Morris. Or she could stay, follow him, learn what he was really buying, who he was working with, and maybe—maybe—get some answers about the world that had taken her partner. The suspect turned, and for a second, she thought he’d seen her. His hood fell back slightly, revealing a face that was human, tired, afraid. Then he was gone, melting into the crowd, and Quinn was left standing alone in the heart of something she didn’t understand, with nothing but her badge, her gun, and a decision that felt like stepping off a cliff. She followed.
created_at"2026-07-25T15:07:30.194Z" 7/25/2026, 3:07:30 PM
model"thinkingmachines/inkling-20260715"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency259
moderation_latency(null)
generation_time8352
tokens_prompt888
tokens_completion1323
native_tokens_prompt816
native_tokens_completion1111
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)
num_fetches(null)
web_search_engine(null)
origin"https://ncbench.com/"
is_byokfalse
finish_reason"stop"
native_finish_reason"stop"
service_tier(null)
usage0.00531555
router(null)
provider_responses
0
endpoint_id"ba2df08c-c0a0-4e0e-9f31-d79a9c475b70"
id"chatcmpl-fd77c6a305d94799bbe45e7156399e6f"
is_byokfalse
latency259
model_permaslug"thinkingmachines/inkling-20260715"
provider_name"BaseTen"
status200
user_agent"langchainjs-openai/1.0.0 ((node/v24.18.0; linux; x64))"
http_referer(null)
request_id"req-1784992050-NaFUtidkzLatjutPp4pk"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1784992050-a7BEdSPgLE8ZagtyYidD"
upstream_id"chatcmpl-fd77c6a305d94799bbe45e7156399e6f"
total_cost0.00531555
cache_discount(null)
upstream_inference_cost0
provider_name"BaseTen"
response_cache_source_id(null)
data_region"global"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags1
adverbTagCount0
adverbTags(empty)
dialogueSentences0
tagDensity1
leniency1
rawRatio0
effectiveRatio0
83.30% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount898
totalAiIsmAdverbs3
found
0
adverb"really"
count2
1
adverb"slightly"
count1
highlights
0"really"
1"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)
66.59% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount898
totalAiIsms6
found
0
word"familiar"
count1
1
word"footsteps"
count1
2
word"echoing"
count1
3
word"flickered"
count1
4
word"constructed"
count1
5
word"scanned"
count1
highlights
0"familiar"
1"footsteps"
2"echoing"
3"flickered"
4"constructed"
5"scanned"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches1
maxInWindow1
found
0
label"air was thick with"
count1
highlights
0"The air was thick with"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences61
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount1
narrationSentences61
filterMatches(empty)
hedgeMatches
0"seemed to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences61
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen50
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords883
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions4
unquotedAttributions0
matches(empty)
98.98% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions21
wordCount882
uniqueNames12
maxNameDensity1.02
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Quinn"
discoveredNames
Soho1
Harlow1
Quinn9
Thursday-night1
Raven1
Nest1
Morris2
Tube1
Stalls1
Veil1
Market1
Met1
persons
0"Harlow"
1"Quinn"
2"Raven"
3"Nest"
4"Morris"
5"Stalls"
places
0"Soho"
1"Thursday-night"
globalScore0.99
windowScore1
0.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences39
glossingSentenceCount6
matches
0"smelled like dead fish"
1"not quite leaving a gap wide enough to squeeze through"
2"looked like people"
3"books that seemed to breathe, turning their own pages in some draft she couldn’t feel"
4"looked like a stack of bills, though the"
5"felt like stepping off a cliff"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount883
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences61
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs25
mean35.32
std24.31
cv0.688
sampleLengths
032
179
256
33
444
545
644
79
873
95
1038
1144
124
137
1493
1532
1622
1740
1838
1939
208
2162
2225
2339
242
88.01% Passive voice overuse
Target: ≤2% passive sentences
passiveCount3
totalSentences61
matches
0"was rusted"
1"was filled"
2"was gone"
3"was left"
0.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount6
totalVerbs164
matches
0"was running"
1"was still falling"
2"was holding"
3"was speaking"
4"was really buying"
5"was working"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount15
semicolonCount0
flaggedSentences11
totalSentences61
ratio0.18
matches
0"Not sprinting—she was forty-one, and her knees had opinions—but running with purpose, with that old familiar burn in her calves that meant she was still in this."
1"Not into an alley—into nothing."
2"The rain masked sounds, but she caught it—footsteps, rapid, echoing downward, into some kind of tunnel or underground access."
3"The corridor led to an old service tunnel, then to a gate that was rusted shut—except it wasn’t, not quite, leaving a gap wide enough to squeeze through."
4"The air was thick with damp and something else—something chemical, sweet and sharp, like incense burning over a chemical fire."
5"Others moved with wrong proportions—too tall, too many joints, eyes that reflected the scattered lantern light like cats’."
6"She’d heard whispers for years—every detective in the Met heard them, filed them away with the other urban legends—but she’d never believed, not really, not until now."
7"Then she saw him—fifty yards away, negotiating with a vendor who had no face, just a smooth oval of skin where features should be."
8"The suspect—she still didn’t have a name—was speaking in a low voice, trading what looked like a stack of bills, though the paper was black and the denominations made no sense."
9"She could retreat, call in the kind of forces that might actually understand this place—if any existed, if they wouldn’t just cover it up like they covered up Morris."
10"Or she could stay, follow him, learn what he was really buying, who he was working with, and maybe—maybe—get some answers about the world that had taken her partner."
99.35% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount540
adjectiveStacks0
stackExamples(empty)
adverbCount22
adverbRatio0.040740740740740744
lyAdverbCount2
lyAdverbRatio0.003703703703703704
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences61
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences61
mean14.48
std11.78
cv0.814
sampleLengths
015
117
227
324
43
51
624
731
84
921
103
115
1239
1322
142
152
1619
174
1819
1921
205
212
222
237
245
2511
2650
275
2828
2910
301
314
3214
3320
345
354
367
3716
386
398
4018
4145
425
4327
449
4513
4624
4716
4820
4918
59.02% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats4
diversityRatio0.39344262295081966
totalSentences61
uniqueOpeners24
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount3
totalSentences54
matches
0"Then he turned."
1"Then she saw him—fifty yards"
2"Then he was gone, melting"
ratio0.056
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount14
totalSentences54
matches
0"He was fast."
1"She knew this ground."
2"She’d walked these beats for"
3"She approached the stairwell."
4"She glanced back up at"
5"She could call for backup,"
6"She’d done that for eighteen"
7"She’d heard whispers for years—every"
8"Her suspect was here, she"
9"She scanned the crowd, keeping"
10"She was close now, close"
11"She had a choice."
12"She could retreat, call in"
13"His hood fell back slightly,"
ratio0.259
80.37% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount41
totalSentences54
matches
0"The rain came down in"
1"Detective Harlow Quinn had her"
2"The suspect was thirty yards"
3"He was fast."
4"Quinn kept her head up,"
5"She knew this ground."
6"She’d walked these beats for"
7"One second he was there,"
8"Quinn skidded to a stop,"
9"The rain masked sounds, but"
10"She approached the stairwell."
11"The concrete steps were slick"
12"Quinn pulled out her phone."
13"She glanced back up at"
14"The rain was still falling."
15"She could call for backup,"
16"She’d done that for eighteen"
17"Quinn started down the stairs."
18"The corridor led to an"
19"The platform was there, crumbling,"
ratio0.759
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences54
matches(empty)
ratio0
0.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences32
technicalSentenceCount9
matches
0"Not sprinting—she was forty-one, and her knees had opinions—but running with purpose, with that old familiar burn in her calves that meant she was still in this…"
1"She’d walked these beats for eighteen years, and she knew every alley that dead-ended, every courtyard that emptied back into traffic."
2"One second he was there, a blur of black against the wet brick, and the next he’d vanished down a stairwell she hadn’t seen, hidden behind a stack of rotting pa…"
3"The concrete steps were slick with moss and grease, descending into darkness that smelled of rust and old electricity."
4"The corridor led to an old service tunnel, then to a gate that was rusted shut—except it wasn’t, not quite, leaving a gap wide enough to squeeze through."
5"The platform was there, crumbling, with tracks that led into blackness in both directions."
6"Stalls lined the old tracks, constructed from scrap wood and black iron, displaying goods she couldn’t identify: jars of liquid that glowed with internal colors…"
7"Quinn moved closer, stepping over something that might have been a pool of water or might have been something alive."
8"Then he was gone, melting into the crowd, and Quinn was left standing alone in the heart of something she didn’t understand, with nothing but her badge, her gun…"
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags1
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags0
79.1871%