Run: 2026-09-23-01M37HY7T4P1NZWJBNYPF0QD64

Run Details

Duration:
50.5s
Cost:
$0.026920
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
Tomás Herrera came out of the chemist’s side door with blood on his cuff and a black medical bag under his arm. He saw Quinn across the road and stopped so abruptly that the woman behind him nearly walked into his back. Then he ran. Quinn stepped off the kerb. A bus shouldered past, throwing rainwater over her shoes, and for three seconds she lost him behind its windows. When it cleared, Herrera was already halfway down the next block. “Police! Stop!” He glanced back. He had warm brown eyes and a Saint Christopher medallion that flashed at his throat as he turned. She had watched him for six nights, long enough to know he could slip a crowd without seeming to hurry. Tonight he was hurrying. Quinn ran. The rain made the pavement shine like black glass. Herrera cut between two men outside a kebab shop, vaulted a low chain, and knocked a stack of plastic crates into her path. Quinn clipped one with her shin. It spun away, and she kept going. In her coat pocket, her phone vibrated. Dispatch, probably. She had called in her location before following Herrera into the chemist. She let it ring. By the time someone else reached Camden, he would be gone. The chemist’s owner had reported missing morphine twice in the last month. Herrera had lost his paramedic licence years ago, but people still came to him for treatment. That had been enough to interest Quinn. The photograph on his phone, taken tonight through the chemist’s back window, had made her follow him: a young man lying on the dispensary floor while Herrera pressed a blood-soaked towel to his chest. The man had got up and walked away before Quinn could reach the door. She had watched him go. The blood had remained on the floor. Herrera crossed at the lights against a red signal. A taxi braked hard and sounded its horn. Quinn followed, one hand raised to stop the driver climbing out after them. Beyond the crossing, Herrera turned down a narrow street lined with shuttered shops. Her watch showed eleven forty-eight. He had taken the same route two nights ago, though then he had carried no bag and Quinn had lost him near the canal. She knew where this street ended: a brick wall beneath a railway viaduct. No through road, no entrance to the station. He was running into a trap. Herrera looked over his shoulder once more. He was younger than her by more than a decade, but his face had the drawn, sleepless look she knew from hospital waiting rooms. He saw that she was still coming and reached into his jacket. Quinn slowed and brought her hand to her holster. He pulled out something small and pale. It slipped from his fingers, struck the pavement, and skittered under a parked van. Herrera kept running. Quinn could have left it. Instead she crouched and reached beneath the van’s dripping chassis. Her fingertips found a disc no larger than a pound coin. Bone, polished smooth on one face and scratched on the other. She pocketed it and ran on. At the wall, Herrera had vanished. Quinn stopped beside a boarded-up entrance marked with the faded roundel of a Tube station. The stairs beyond the boards had been closed for decades; she had checked the maps after losing him here the first time. Rain pattered on the plywood. A train rolled somewhere overhead, shaking dust from the viaduct. She caught a scrape from inside. Quinn drew her torch and pushed against the boards. One moved inward. Behind it stood a heavy steel door, open a hand’s width. She slipped through and found stairs descending into darkness. Her phone rang again. She answered. “Quinn.” She kept her voice low. “Where are you?” Sergeant Iqbal asked. “Uniform’s five minutes out.” “Old station entrance off Hawley Street. Herrera’s gone inside.” “Wait for them.” Quinn aimed her torch down the steps. Water trickled along one edge. Halfway down lay a fresh red print, the shape of fingers dragged across a tile. “He may have someone hurt down here.” “Quinn—” The line crackled. From below came a woman’s voice, sharp with anger, then a sound like a shutter slamming. “Send them to the entrance,” Quinn said. “Tell them not to seal it.” She ended the call before Iqbal could answer. The stairwell smelled of wet concrete and hot metal. Quinn descended with her torch in one hand and her other free. At the bottom, a passage opened onto a disused ticket hall. Grimy white tiles reflected a light that had no business being there: amber, blue, a thin violent green. Voices filled the space beyond an archway. A man stood beneath it in a dark wool coat. He was broad enough to block most of the opening. When Quinn approached, he looked at her hand rather than her face. “Token,” he said. Quinn stopped. Through the gap beside him she saw stalls crowded along an old platform. Lamps burned over tables covered in bottles and folded cloth. People moved between them, their heads bent close as they talked. She glimpsed Herrera’s dark curls disappearing into the crowd. She took the bone disc from her pocket. The man held out his palm. “What is this place?” she asked. “Token.” She showed him her warrant card instead. “Metropolitan Police. Move aside.” His gaze stayed on the disc. “You can walk back up the stairs.” Something shifted behind him. Quinn moved her torch and caught a face between two hanging rugs. Its eyes shone silver in the beam. The person turned away too quickly for her to make sense of what she had seen. Herrera was getting farther away. Quinn put the disc in the man’s palm. He turned it over with his thumb. A shallow notch marked the edge. His thumb stopped on the scratched face, and his expression changed. “Who gave you this?” “He dropped it.” The man looked into the market, then back at her. For the first time, he seemed uncertain. He stepped aside. Quinn went through before he could reconsider. The abandoned platform stretched farther than her torch reached. Somebody had strung electrical cable along the ceiling; bare bulbs hung over traders’ tables. An old station sign read CAMDEN TOWN beneath a newer smear of black paint. The tracks were hidden under wooden decking, leaving only a dark trench along the platform’s far edge. Herrera stood at a stall near the middle. He had opened his bag. A woman with a split lip leaned against the counter while he wrapped something around her wrist. Blood dripped steadily from her sleeve onto the wood. Quinn moved toward him. Conversations faltered as she passed. A man selling sealed glass jars pulled a cloth over them. At another table, rows of teeth lay arranged on black velvet, each tied with red thread. Quinn fixed her eyes on Herrera. “Hands where I can see them,” she called. He looked up, finished fastening the bandage, and said something to the injured woman. She disappeared between the stalls. Herrera closed his bag. “You shouldn’t have come down,” he said. “Then you shouldn’t have run.” He glanced past Quinn, toward the entrance. The man in the wool coat remained under the archway, watching them. Quinn took out the disc. “You dropped this.” Herrera’s face tightened. “I know.” “You wanted me to follow.” “I wanted you to find the door. I thought you’d wait for help before you came in.” She looked at the scratches on the bone. Under the market lamps they resolved into letters and numbers: MORRIS, followed by his warrant number. For an instant she heard rain against a different roof. Three years ago, she and DS Morris had entered a boarded-up house on a missing-person call. He had gone ahead of her down a hall. By the time she reached its end, he was gone. There had been no door, no broken window, nothing for the search team to find. His warrant card had still been in his pocket when she last saw him. Quinn closed her fingers around the token. “Where did you get this?” Herrera’s eyes went to the engraved name. “At the Raven’s Nest. Someone brought it to the back room tonight. They said it would get you here.” Quinn pictured the Soho bar: green neon over the entrance, old maps and black-and-white photographs on the walls. She had followed Herrera there the previous week. He had gone behind a bookshelf with two people carrying a wounded man between them. Quinn had waited outside for them to emerge. They never had. “Who brought it?” “I didn’t get a name.” “Describe them.” Herrera shook his head. “We need to leave. That woman was cut getting out of the lower tunnel. Whatever did it followed her.” A metallic bang rang from the far end of the platform. Every head turned. Beyond the last stall, where the tunnel curved away into blackness, a shutter began to rise. It lifted an inch, stopped, and shuddered against its frame. A trader seized the edge of his tablecloth and swept his wares into a box. Others started toward the exit, pressing around Quinn. Herrera caught his bag and pushed through them toward her. Quinn held her ground. “Morris came here?” “I don’t know. The token has his name.” The shutter struck its frame again, harder. Along the platform, bulbs flickered. Quinn could hear a sound beneath the crowd’s footsteps, a slow dragging noise from inside the tunnel. Her phone showed no signal. Uniform was above, five minutes out when Iqbal called. Perhaps three now. Between them and her stood the man at the archway, who had admitted her on a token bearing a dead man’s name. Herrera reached her. “Detective.” She wanted to seize him by the collar and make him tell her everything he knew. Instead she looked down the platform. The shutter rose another inch. In the gap below it, something pale moved across the tiles. The woman with the injured wrist stumbled out from between the stalls. Her bandage had soaked through. Herrera went to her at once, putting her good arm over his shoulders. Quinn backed toward the archway with them, keeping her torch trained on the shutter. The dragging stopped. For one second the market went quiet enough that Quinn heard rain running down the stairwell behind her. Then the shutter buckled outward. Quinn grabbed Herrera’s bag strap and pulled him and the woman toward the exit. “Move.” They reached the ticket hall as the first bulb burst behind them. Quinn did not look back. She could hear people running on the platform, and beneath them, something coming fast along the tiles.
created_at"2026-09-23T16:35:18.476Z" 9/23/2026, 4:35:18 PM
model"openai/gpt-6-sol-20260922"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency7711
moderation_latency(null)
generation_time50454
tokens_prompt888
tokens_completion2683
native_tokens_prompt810
native_tokens_completion2530
native_tokens_completion_images0
native_tokens_reasoning297
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"
usage0.02692
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.20.0; linux; x64))"
http_referer(null)
request_id"req-1790181318-gwmzxjJAnZ3cDbLmsaBw"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1790181318-xynfd3vh0WDQbnqoIm7m"
upstream_id"resp_0faf8fd4df358750016ab3ffc6a11c87d1b11b91169d981002"
provider_responses
0
endpoint_id"1cfc7d9d-4404-4b8e-9ee4-58ae45c9dcd4"
id"resp_0faf8fd4df358750016ab3ffc6a11c87d1b11b91169d981002"
is_byokfalse
latency947
model_permaslug"openai/gpt-6-sol-20260922"
provider_name"OpenAI"
status200
total_cost0.02692
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
totalTags8
adverbTagCount0
adverbTags(empty)
dialogueSentences34
tagDensity0.235
leniency0.471
rawRatio0
effectiveRatio0
97.21% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1790
totalAiIsmAdverbs1
found
0
adverb"quickly"
count1
highlights
0"quickly"
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)
83.24% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1790
totalAiIsms6
found
0
word"vibrated"
count1
1
word"sense of"
count1
2
word"velvet"
count1
3
word"resolved"
count1
4
word"flickered"
count1
5
word"footsteps"
count1
highlights
0"vibrated"
1"sense of"
2"velvet"
3"resolved"
4"flickered"
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
emotionTells1
narrationSentences170
matches
0"p with anger"
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount2
narrationSentences170
filterMatches
0"watch"
hedgeMatches
0"began to"
1"started to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences197
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen35
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1790
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions11
unquotedAttributions0
matches(empty)
44.67% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions68
wordCount1614
uniqueNames10
maxNameDensity2.11
worstName"Quinn"
maxWindowNameDensity3
worstWindowName"Quinn"
discoveredNames
Herrera24
Quinn34
Saint1
Christopher1
Camden1
Tube1
Sergeant1
Iqbal3
Morris1
Soho1
persons
0"Herrera"
1"Quinn"
2"Saint"
3"Christopher"
4"Sergeant"
5"Iqbal"
6"Morris"
places
0"Soho"
globalScore0.447
windowScore0.667
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences122
glossingSentenceCount1
matches
0"seemed uncertain"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1790
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences197
matches
0"saw that she"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs82
mean21.83
std18.93
cv0.867
sampleLengths
042
13
235
32
445
52
645
736
883
912
1043
115
1251
1343
149
1524
1643
176
1852
196
2032
216
226
2310
249
253
2627
277
281
2919
3013
318
3257
3332
343
3545
3614
376
381
3911
4013
4139
4213
4324
444
453
4620
477
4854
4939
99.07% Passive voice overuse
Target: ≤2% passive sentences
passiveCount3
totalSentences170
matches
0"been closed"
1"were hidden"
2"was gone"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount4
totalVerbs288
matches
0"was hurrying"
1"was running"
2"was still coming"
3"was getting"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount2
flaggedSentences2
totalSentences197
ratio0.01
matches
0"The stairs beyond the boards had been closed for decades; she had checked the maps after losing him here the first time."
1"Somebody had strung electrical cable along the ceiling; bare bulbs hung over traders’ tables."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1056
adjectiveStacks0
stackExamples(empty)
adverbCount31
adverbRatio0.029356060606060608
lyAdverbCount4
lyAdverbRatio0.003787878787878788
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences197
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences197
mean9.09
std5.53
cv0.609
sampleLengths
022
120
23
35
419
511
62
73
818
920
104
112
129
1323
146
157
167
172
1812
194
2011
2112
2216
237
2434
2514
265
277
289
298
3013
3113
325
3324
3413
358
366
377
3824
3912
409
417
4214
433
445
4510
4611
4711
486
496
54.99% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats5
diversityRatio0.34517766497461927
totalSentences197
uniqueOpeners68
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount5
totalSentences161
matches
0"Then he ran."
1"Instead she crouched and reached"
2"Perhaps three now."
3"Instead she looked down the"
4"Then the shutter buckled outward."
ratio0.031
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount48
totalSentences161
matches
0"He saw Quinn across the"
1"He glanced back."
2"He had warm brown eyes"
3"She had watched him for"
4"It spun away, and she"
5"She had called in her"
6"She let it ring."
7"She had watched him go."
8"Her watch showed eleven forty-eight."
9"He had taken the same"
10"She knew where this street"
11"He was running into a"
12"He was younger than her"
13"He saw that she was"
14"He pulled out something small"
15"It slipped from his fingers,"
16"Her fingertips found a disc"
17"She pocketed it and ran"
18"She caught a scrape from"
19"She slipped through and found"
ratio0.298
53.17% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount131
totalSentences161
matches
0"Tomás Herrera came out of"
1"He saw Quinn across the"
2"Quinn stepped off the kerb."
3"A bus shouldered past, throwing"
4"He glanced back."
5"He had warm brown eyes"
6"She had watched him for"
7"Tonight he was hurrying."
8"The rain made the pavement"
9"Herrera cut between two men"
10"Quinn clipped one with her"
11"It spun away, and she"
12"She had called in her"
13"She let it ring."
14"The chemist’s owner had reported"
15"Herrera had lost his paramedic"
16"That had been enough to"
17"The photograph on his phone,"
18"The man had got up"
19"She had watched him go."
ratio0.814
62.11% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount2
totalSentences161
matches
0"By the time someone else"
1"By the time she reached"
ratio0.012
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences73
technicalSentenceCount2
matches
0"He had warm brown eyes and a Saint Christopher medallion that flashed at his throat as he turned."
1"Grimy white tiles reflected a light that had no business being there: amber, blue, a thin violent green."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags8
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags6
fancyCount0
fancyTags(empty)
dialogueSentences34
tagDensity0.176
leniency0.353
rawRatio0
effectiveRatio0
93.1487%