Run: 2026-09-23-01M37J032AA6H2XFXD4TJR5EMB

Run Details

Duration:
53.2s
Cost:
$0.029010
Provider:
OpenAI

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
Rain blurred the Camden High Street lights into long, trembling lines. Detective Harlow Quinn kept Tomás Herrera in sight by the pale strip of his shirt beneath his open jacket. He ran well for a man who had just taken a punch to the ribs. He cut between two taxis and vaulted the barrier at a pedestrian crossing. Quinn followed, caught the top rail with a wet palm, and landed hard enough to jar her teeth. A horn sounded behind her. Herrera looked back. That was a mistake. He lost half a step, and she gained it. “Tomás! Stop!” He turned into a side street without answering. An hour ago she had watched him leave the Raven’s Nest in Soho carrying a narrow black case. She had seen him hand it to a man outside a Camden café. The man had opened it, stared at whatever was inside, and collapsed before he could close the lid. Herrera had tried to revive him. When Quinn stepped from her car and identified herself, Herrera took the case and ran. The man was alive when she left him with the ambulance crew. She had made sure of that. His eyes, though, had been open and fixed on her as he whispered, “Don’t let him take it down.” Now Herrera was taking it down. He skidded around a pile of bin bags. Quinn followed into an alley that smelled of stale beer and wet brick. Ahead, the alley ended at a corrugated security gate. She drove harder, certain she had him. Herrera ducked beneath the half-raised gate. Quinn dropped to one knee and slid after him. The metal caught the back of her coat and tore it. By the time she got to her feet, he was twenty yards away, crossing a fenced service yard toward a brick arch. The old Underground roundel above it had been painted over, but rain had loosened the paint. The first three letters showed through: CAM. Her radio crackled at her shoulder. “Quinn? Your location?” “North of Camden High Street. Disused station entrance, east side. Suspect going below.” “Wait for units. Four minutes.” Herrera reached the arch. Beyond him lay a stairwell, unlit except for a faint amber glow far beneath street level. Quinn checked her worn leather watch. Four minutes was plenty of time for a man who knew where he was going. She had spent six weeks building a case around the Raven’s Nest: unexplained injuries, stolen pharmaceuticals, people who entered the bar and later denied having been there. Herrera’s name sat in the middle of it. Former paramedic. Lost his licence. Treated patients whose records either disappeared or never existed. And now a man had fallen unconscious after looking inside Herrera’s case. “I’m maintaining visual,” she said. “Quinn, hold position.” Herrera vanished down the stairs. She went after him. Water streamed down the tiled walls. The staircase turned twice, each landing taking her farther from the noise of traffic. Halfway down, her radio hissed and died. She hit the transmit button. Nothing. At the foot of the stairs stood a turnstile from another decade, its chrome dull with grime. Someone had bolted a heavy iron gate behind it. Herrera stood on the far side, the case tucked under one arm. Between them, seated in a ticket booth with shattered glass, was a woman in a red coat. Quinn had not seen her as she came down. She could not have missed that coat. Herrera held up a small white disc between finger and thumb. Bone, Quinn thought. He slipped it through the booth’s payment slot. The woman took it without looking at him. The iron gate clicked open just long enough for Herrera to pass. “Police,” Quinn said, reaching the turnstile. “Open it.” The woman turned toward her. Her face was ordinary, broad and tired, except that her pupils filled almost all of her eyes. “Token,” she said. Quinn gripped the cold metal bar. On the other side, Herrera glanced back once. He did not look triumphant. He looked frightened. “Open the gate.” “Token.” Herrera moved away. A murmur rose from somewhere beyond the ironwork: dozens of voices, the clatter of wheels over tile, music playing too slowly to be a recording Quinn knew. She reached into her coat pocket and closed her hand around the object she had taken from the man outside the café. A white disc, no bigger than a fifty-pence piece. She had found it pressed into his fist while checking him for identification. A hole pierced its centre, and a hairline crack ran from the hole to the edge. She should have bagged it. She should have stayed upstairs with the ambulance crew, handed over the scene, waited for backup, and come down with enough officers to secure the entrance. Three years earlier, DS Morris had followed a lead into a service tunnel beneath Whitechapel. He had called her from inside it, his voice breaking up through static. She had told him to wait. By the time she reached the tunnel, he was gone. Quinn pushed the disc through the slot. The woman’s fingers closed over it. She studied the crack. “Not yours,” she said. “Neither is the gate.” The woman smiled without warmth and pressed something beneath the counter. The lock gave a hard metallic snap. Quinn stepped through. The tunnel beyond had once been a platform approach. Old advertisements peeled from the walls, their paper softened by decades of damp. But light spilled around the bend ahead, bright and gold, and the noise swelled as she followed it. She emerged onto an abandoned Tube platform crowded with stalls. A butcher under a striped awning packed a dark, pulsing lump into butcher’s paper. Across from him, a woman in surgical gloves sold blue liquid from stoppered vials. Maps hung from wires overhead, none showing streets Quinn recognised. On the tracks, a row of narrow tables displayed keys, jars of teeth, and watches whose hands spun at different speeds. The air smelled of hot oil, wet stone, and something sharp enough to make her eyes water. No one stopped to stare. That unsettled her more than the stalls. Herrera threaded through the crowd toward the far end of the platform. Quinn followed, her shoulders squared, her right hand free. A man brushed past her carrying a cage covered with a velvet cloth. Something inside scraped one long nail against the bars. She checked her watch. The second hand had stopped. A bell rang once. Around her, conversations faltered. The woman selling vials looked up from her table and folded a cloth over her stock. Herrera heard it too. He quickened his pace. Quinn pushed between two customers arguing over a packet of black seeds. She caught sight of his dark curls by a stairway marked EXIT, the letters scorched into the tile. He disappeared through a curtain. She drew the curtain aside and found a cramped storeroom lined with wooden crates. Herrera stood at the far end, trying to open the black case. His left sleeve had ridden up, exposing a long scar along his forearm. A Saint Christopher medallion swung against his chest. “Put it down,” Quinn said. He froze. Then he turned, keeping one hand on the case. “You shouldn’t be here.” “I’ve heard that twice tonight. Put your hands where I can see them.” He obeyed. His breathing was ragged. The front of his shirt bore a smear of blood where she had struck him outside the café. “What did you give that man?” “Nothing. He came to collect this. He opened it before I could stop him.” “And what is it?” Herrera looked at the case. “A record.” “A record of what?” Footsteps passed outside the curtain. Both of them waited until they faded. “People taken from the city,” he said. “Names, dates, places. Someone’s been keeping count.” Quinn felt the storeroom narrow around her. “Who?” “I don’t know.” “Convenient.” “I was bringing it to someone who might.” He nodded at the curtain. “He didn’t show. The other man came instead.” Quinn thought of the patient on the pavement, his fingers locked around the bone disc, his desperate warning. She could not tell whether Herrera was lying. He was certainly keeping something back. “Open it,” she said. “I’m trying.” He turned it so she could see the lock. The small brass catch was bent inward. “He forced the lid. It jammed when I shut it.” A second bell rang, closer this time. A low voice spoke over the market noise. Quinn could not make out the words. Herrera could. The blood drained from his face. “What does that mean?” she asked. “They know you came in on a borrowed token.” Quinn took a step toward the case. Herrera tightened his grip, and she stopped. “Who knows?” The curtain shifted. A shadow fell across the floor beneath it, tall and still. Herrera looked from the shadow to Quinn. In the hard light of the storeroom, his fear was plain. Whatever had brought him here, it had not been the hope of meeting her. “You came through the gate,” he whispered. “Did the woman see the crack?” Quinn did not answer. She heard a soft tap outside, as if someone had put a coin down on a table. Then the curtain began to rise. Quinn seized the nearest crate and shoved it against the doorway. It struck a figure behind the cloth with a muffled grunt. She grabbed Herrera by the jacket and hauled him toward a narrow maintenance door at the back. “Move.” He took the case and stumbled through. Quinn slammed the door behind them and drove the bolt home just as something heavy hit the other side. They stood in a dark passage. Water dripped steadily from a pipe overhead. At the far end, a ladder rose toward a square of black. Herrera stared at her. “You believe me?” “No.” Quinn took out her phone. No signal. She put it away. “But somebody wants that case badly enough to come after us. Climb.” He started up the ladder. Quinn waited beneath it, listening to the bolt strain. Her watch had begun ticking again. She could hear each second as clearly as the blows against the door. At the top, Herrera pushed open a hatch. Rain swept into the passage, cold and clean. Quinn followed him into a fenced lot behind a shuttered shop. Sirens sounded a few streets away. She caught his wrist before he could run. This time he did not pull free. “Open the case,” she said. Herrera set it on an overturned bucket. With the tip of Quinn’s house key, he worked the damaged catch loose. Inside lay a stack of index cards bound with string. The top card bore a name written in neat block capitals. DS ELIAS MORRIS. Quinn stared at it. Beneath his name was the date he had disappeared. Beneath that, in red ink, someone had written: TRANSFERRED. Herrera watched her face. “You know him.” She closed the case before the rain could reach the cards. Beyond the fence, a police car turned into the street, blue lights flashing against the wet brick. “Yes,” she said. “I do.”
created_at"2026-09-23T16:36:19.154Z" 9/23/2026, 4:36:19 PM
model"openai/gpt-6-sol-20260922"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency7131
moderation_latency(null)
generation_time53203
tokens_prompt888
tokens_completion3167
native_tokens_prompt810
native_tokens_completion2739
native_tokens_completion_images0
native_tokens_reasoning394
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"completed"
service_tier"default"
usage0.02901
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.20.0; linux; x64))"
http_referer(null)
request_id"req-1790181379-gI4LwwY7Unm0ZONUpwZC"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1790181379-zHwDEinc8nWkhW7V9C1p"
upstream_id"resp_0f8a4704960fbe7c016ab4000351dc87d1b9efaaa6ddaa151f"
provider_responses
0
endpoint_id"1cfc7d9d-4404-4b8e-9ee4-58ae45c9dcd4"
id"resp_0f8a4704960fbe7c016ab4000351dc87d1b9efaaa6ddaa151f"
is_byokfalse
latency1166
model_permaslug"openai/gpt-6-sol-20260922"
provider_name"OpenAI"
status200
total_cost0.02901
cache_discount(null)
upstream_inference_cost0
provider_name"OpenAI"
response_cache_source_id(null)
data_region"global"
workspace_id"97e315e5-d303-487d-83c1-83180e8a13d4"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags15
adverbTagCount0
adverbTags(empty)
dialogueSentences45
tagDensity0.333
leniency0.667
rawRatio0
effectiveRatio0
97.33% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1870
totalAiIsmAdverbs1
found
0
adverb"slowly"
count1
highlights
0"slowly"
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)
83.96% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1870
totalAiIsms6
found
0
word"shattered"
count1
1
word"warmth"
count1
2
word"unsettled"
count1
3
word"velvet"
count1
4
word"quickened"
count1
5
word"footsteps"
count1
highlights
0"shattered"
1"warmth"
2"unsettled"
3"velvet"
4"quickened"
5"footsteps"
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
narrationSentences185
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount3
hedgeCount2
narrationSentences185
filterMatches
0"watch"
hedgeMatches
0"tried to"
1"began to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences215
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen27
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1870
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions13
unquotedAttributions0
matches(empty)
50.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions75
wordCount1672
uniqueNames16
maxNameDensity1.79
worstName"Quinn"
maxWindowNameDensity3.5
worstWindowName"Quinn"
discoveredNames
Camden2
High1
Street1
Harlow1
Quinn30
Tomás1
Herrera28
Raven2
Nest2
Soho1
Underground1
Morris1
Whitechapel1
Tube1
Saint1
Christopher1
persons
0"Harlow"
1"Quinn"
2"Tomás"
3"Herrera"
4"Raven"
5"Morris"
6"Saint"
7"Christopher"
places
0"Camden"
1"High"
2"Street"
3"Soho"
globalScore0.603
windowScore0.5
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences131
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1870
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences215
matches
0"missed that coat"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs96
mean19.48
std16.87
cv0.866
sampleLengths
045
139
213
32
48
570
637
76
837
96
1065
119
1213
135
1420
1570
1612
175
183
195
204
2133
2238
2333
2442
258
2622
273
2822
293
301
3130
3260
3331
3444
357
3610
374
384
3918
403
4140
4210
4359
4417
4512
4643
479
4832
4935
99.57% Passive voice overuse
Target: ≤2% passive sentences
passiveCount3
totalSentences185
matches
0"been open"
1"been painted"
2"was gone"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount4
totalVerbs305
matches
0"was taking"
1"was going"
2"was lying"
3"was certainly keeping"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences215
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1674
adjectiveStacks0
stackExamples(empty)
adverbCount33
adverbRatio0.01971326164874552
lyAdverbCount4
lyAdverbRatio0.0023894862604540022
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences215
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences215
mean8.7
std5.28
cv0.608
sampleLengths
011
119
215
313
418
55
63
74
89
92
108
1118
1213
1318
146
1515
1612
176
1819
196
208
2113
229
237
246
259
2611
2722
2816
297
306
313
3213
335
344
3516
366
3715
3827
398
402
413
429
4312
445
453
465
474
486
4914
49.61% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats10
diversityRatio0.32558139534883723
totalSentences215
uniqueOpeners70
39.45% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences169
matches
0"Then he turned, keeping one"
1"Then the curtain began to"
ratio0.012
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount48
totalSentences169
matches
0"He ran well for a"
1"He cut between two taxis"
2"He lost half a step,"
3"He turned into a side"
4"She had seen him hand"
5"She had made sure of"
6"His eyes, though, had been"
7"He skidded around a pile"
8"She drove harder, certain she"
9"Her radio crackled at her"
10"She had spent six weeks"
11"She went after him."
12"She hit the transmit button."
13"She could not have missed"
14"He slipped it through the"
15"Her face was ordinary, broad"
16"He did not look triumphant."
17"He looked frightened."
18"She reached into her coat"
19"She had found it pressed"
ratio0.284
54.67% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount137
totalSentences169
matches
0"Rain blurred the Camden High"
1"Detective Harlow Quinn kept Tomás"
2"He ran well for a"
3"He cut between two taxis"
4"Quinn followed, caught the top"
5"A horn sounded behind her."
6"Herrera looked back."
7"That was a mistake."
8"He lost half a step,"
9"He turned into a side"
10"An hour ago she had"
11"She had seen him hand"
12"The man had opened it,"
13"Herrera had tried to revive"
14"The man was alive when"
15"She had made sure of"
16"His eyes, though, had been"
17"He skidded around a pile"
18"Quinn followed into an alley"
19"She drove harder, certain she"
ratio0.811
88.76% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount3
totalSentences169
matches
0"Now Herrera was taking it"
1"By the time she got"
2"By the time she reached"
ratio0.018
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences69
technicalSentenceCount1
matches
0"She had spent six weeks building a case around the Raven’s Nest: unexplained injuries, stolen pharmaceuticals, people who entered the bar and later denied havin…"
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags15
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags12
fancyCount2
fancyTags
0"he whispered (whisper)"
1"he whispered (whisper)"
dialogueSentences45
tagDensity0.267
leniency0.533
rawRatio0.167
effectiveRatio0.089
92.1116%