Run: 2026-09-23-01M37HVDMM8R90W1KYMRWBK9EJ

Run Details

Duration:
41.1s
Cost:
$0.021658
Provider:
OpenAI

Model input

System
You are an expert fiction writer. Always keep the following rules in mind: - Write in past tense and use General English spelling, grammar, and colloquialisms/slang. - Write in active voice - Always follow the "show, don't tell" principle. - Avoid adverbs and cliches and overused/commonly used phrases. Aim for fresh and original descriptions. - Convey events and story through dialogue. - Mix short, punchy sentences with long, descriptive ones. Drop fill words to add variety. - Skip "he/she said said" dialogue tags and convey people's actions or face expressions through their speech - Avoid mushy dialog and descriptions, have dialogue always continue the action, never stall or add unnecessary fluff. Vary the descriptions to not repeat yourself. - Put dialogue on its own paragraph to separate scene and action. - Reduce indicators of uncertainty like "trying" or "maybe" When writing text: - NEVER conclude the scene on your own, follow the beat instructions very closely. - NEVER end with foreshadowing. - NEVER write further than what I prompt you with. - AVOID imagining possible endings, NEVER deviate from the instructions. - STOP EARLY if the continuation contains what was required in the instructions. You do not need to fill out the full amount of words possible.
User/Human
Take into account the following glossary of characters/locations/items/lore... when writing your response: <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> Continue the story using the following instructions: <instructions> 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. </instructions>

Model response

AI
Rain struck the bonnet of the parked car hard enough to drown out the radio. Detective Harlow Quinn wiped a clear patch in the steamed windscreen and watched Tomás Herrera leave the chemist’s on Camden High Street. He carried a black medical bag in one hand. His other hand gripped the elbow of a woman in a grey coat. The woman walked with her head down, her weight tipping against him each time her right foot met the pavement. Quinn lifted her radio. “Herrera’s out. One woman with him, injured. East side of the High Street, heading north.” Static crackled, then a voice from the control room. “Nearest unit is eight minutes away.” “Tell them to come through the market.” The woman stumbled. Herrera caught her before she hit a shutter, and her coat fell open. A dark stain covered her shirt from ribs to hip. She pushed his hand away and lurched on. Quinn opened the car door. Herrera looked across the road. His warm brown eyes met hers through the rain. He let go of the woman and ran. “Police. Stop.” He ducked between a bus and a delivery van. Quinn crossed after him, her shoes slapping through water that reached her ankles. A horn blared. The van’s wing mirror passed close enough to tug her sleeve. On the far pavement, the woman in grey clung to a drainpipe. “Stay there.” Quinn pointed to the chemist’s lit doorway. “Get inside and ask them to call an ambulance.” “No hospital.” The woman’s lips had lost their colour. “He’s got what I need.” Herrera turned a corner beside a kebab shop. Quinn looked once at the blood running from beneath the woman’s coat, then followed him. She had watched Herrera leave the Raven’s Nest two hours earlier, under its green neon sign, with the same medical bag. She had followed him out of Soho and lost him twice among the crowds on the Tube. In the chemist’s, he had bought gauze, saline and a packet of children’s sweets. Quinn had seen the sweets through the window when he set them on the counter. She hadn’t seen a weapon. His name sat in three files on her desk. In each one, someone had arrived at a hospital with an injury no one could explain and refused to name the person who treated it. A fourth file held a photograph of Herrera outside the Nest beside a man DS Morris had questioned three days before he died. Herrera cut behind a row of market stalls. Their canvas roofs sagged under the rain. Quinn saw his dark curls above a crate of oranges, then lost him behind a stack of folded tables. “Tomás.” A table scraped against brick. Quinn rounded it and found the passage empty. She slowed. Water ran down the wall in thin streams. At the far end, a metal gate swung inward, then bounced back against its frame. Quinn caught it before it shut. Beyond lay a service yard packed with bins and broken pallets. Herrera crossed it at a run. He dropped the medical bag to wrestle with a padlock on a narrow door in the wall. “Leave it.” He looked back. Rain ran from his hair down his face. The sleeve of his jacket rode up, showing a long scar on his left forearm. “There’s a woman bleeding on the High Street,” Quinn called. “If you’ve got supplies, take them to her.” “I haven’t got what she needs.” “Then tell me where you’re going.” The lock gave. Herrera caught up his bag and pulled the door open. “Back to your car, Detective.” He slipped inside. Quinn crossed the yard. A white chip lay near the padlock, small enough to fit beneath her thumb. She picked it up. A hole pierced one end, and scratches marked the other. It felt like bone. A voice came through her radio. “Detective Quinn? Unit’s on Parkway now. Where are you?” “Service yard behind the north end of the market. I’ve got an open door leading into a building. Herrera went through.” “Hold for the unit.” Quinn put the chip in her pocket and stepped inside. A stairwell descended beneath the street. Old cream tiles lined the walls, their edges black with grime. At the first landing, a sign pointed left towards a sealed platform. Herrera’s footsteps rang below her. “Tomás, I need you to stop.” “You saw her wound.” His voice rose through the stairwell. “You know I can’t stop.” Quinn moved down. Her watch strap stuck to her wet wrist. At the bottom of the next flight, she found a wire gate set into the wall, a space beneath it wide enough for water to drain through. Voices carried from beyond it. Herrera stood in front of the gate. On the other side, a broad man in a railway coat held a lantern. He looked at Herrera’s bag. “You’re late.” “Open it. She’s on the street.” “Token.” Herrera pulled a white disc from the chain around his neck. As he held it up, his Saint Christopher medallion slid against it. The man inspected the disc, then lifted a latch. Quinn took out her radio. Only a hiss answered. Herrera passed through. The man began to close the gate, then caught sight of Quinn halfway down the stairs. He waited with one hand on the bars. “Police,” Quinn called. “Keep it open.” Herrera stopped beyond him. “She followed me from Soho.” The man’s gaze dropped to Quinn’s hand, her coat, the radio. “Not my concern. Token.” “I’m pursuing a suspect.” “Then you can pursue him with a token.” Herrera took a step down the passage. Quinn drew the white chip from her pocket. “You dropped this.” Herrera glanced at it. “Keep it.” “What is it?” “Your way in.” The man’s fingers tightened around the gate. Quinn could see past him now. Strings of bare bulbs ran along a platform where a train should have stood. Stalls filled the tracks and spilled across the platform edge. A woman behind a counter poured something black from a flask into stoppered glass bottles. Across the aisle, a customer held out a palm while a vendor turned a silver ring over it without touching skin. A child in a yellow raincoat sat beneath a table, sorting teeth into small paper envelopes. Farther down, a sign painted on the tiled wall read VEIL MARKET. Under it, someone had pinned a notice announcing the next full moon. Quinn held the radio higher. “Control, do you copy?” The hiss broke around two words she couldn’t make out. The man at the gate raised his lantern. “In or out?” From above came the clatter of the service-yard door. The responding officers had reached it. Quinn could wait for them. She looked past the gate again and saw Herrera moving through the crowd, medical bag pressed against his side. He stopped at a stall and leaned across the counter, his hands cutting sharp shapes through the air. The vendor shook her head. Quinn pushed the bone chip through the bars. The man took it and turned it once beneath the lantern. He stood aside. “Keep hold of that,” he told her, returning it. Quinn crossed the threshold. Noise closed around her: bargaining, glass knocking against glass, a handcart rattling over the sleepers. She passed the man with the lantern and stepped down from the old platform, her eyes fixed on Herrera’s dark jacket as it vanished behind a hanging sheet of blue plastic. “Tomás.” Her voice carried above the crowd. “Move away from the stall and show me your hands.” The sheet snapped aside. Herrera looked back at her, clutching a small parcel wrapped in brown paper. He held it up so she could see it, then pushed through the people between them.
created_at"2026-09-23T16:33:46.14Z" 9/23/2026, 4:33:46 PM
model"openai/gpt-6-sol-20260922"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency10689
moderation_latency(null)
generation_time41115
tokens_prompt1104
tokens_completion1934
native_tokens_prompt984
native_tokens_completion1969
native_tokens_completion_images0
native_tokens_reasoning337
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"
usage0.021658
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.20.0; linux; x64))"
http_referer(null)
request_id"req-1790181226-BH1uIuyOC72tqWwmtbaI"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1790181226-2bYUTo2lUHYlT5HUEv4r"
upstream_id"resp_089bcdb8b939d3f2016ab3ff6a429c87d180cfbcf4e069242d"
provider_responses
0
endpoint_id"1cfc7d9d-4404-4b8e-9ee4-58ae45c9dcd4"
id"resp_089bcdb8b939d3f2016ab3ff6a429c87d180cfbcf4e069242d"
is_byokfalse
latency771
model_permaslug"openai/gpt-6-sol-20260922"
provider_name"OpenAI"
status200
total_cost0.021658
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
totalTags6
adverbTagCount0
adverbTags(empty)
dialogueSentences39
tagDensity0.154
leniency0.308
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1302
totalAiIsmAdverbs0
found(empty)
highlights(empty)
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)
88.48% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1302
totalAiIsms3
found
0
word"weight"
count1
1
word"lurched"
count1
2
word"footsteps"
count1
highlights
0"weight"
1"lurched"
2"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
narrationSentences113
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount1
narrationSentences113
filterMatches
0"watch"
hedgeMatches
0"began to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences145
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen31
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1302
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions4
unquotedAttributions0
matches(empty)
33.33% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions57
wordCount1102
uniqueNames14
maxNameDensity2.18
worstName"Quinn"
maxWindowNameDensity4
worstWindowName"Herrera"
discoveredNames
Harlow1
Quinn24
Tomás1
Herrera20
Camden1
High1
Street1
Raven1
Nest2
Soho1
Tube1
Morris1
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"Nest"
4"Soho"
globalScore0.411
windowScore0.333
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences81
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1302
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences145
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs67
mean19.43
std17.85
cv0.919
sampleLengths
037
142
24
315
415
57
634
75
822
92
1036
1112
1218
1314
1423
1572
1657
1734
181
1913
2031
2134
222
2326
2418
256
266
2713
285
293
3036
3115
3221
334
3410
3534
366
3715
3843
3926
402
416
421
4332
449
4527
466
479
4815
494
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences113
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs180
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences145
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1103
adjectiveStacks0
stackExamples(empty)
adverbCount27
adverbRatio0.024478694469628286
lyAdverbCount1
lyAdverbRatio0.0009066183136899365
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences145
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences145
mean8.98
std5.66
cv0.63
sampleLengths
015
122
29
313
420
54
615
79
86
97
103
1113
1210
138
145
155
169
178
182
199
2013
213
2211
2312
249
259
269
275
288
2915
3021
3117
3214
3315
345
359
3625
3723
388
397
4019
411
425
438
442
458
4615
476
4811
496
58.85% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats3
diversityRatio0.36551724137931035
totalSentences145
uniqueOpeners53
30.30% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences110
matches
0"Only a hiss answered."
ratio0.009
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount27
totalSentences110
matches
0"He carried a black medical"
1"His other hand gripped the"
2"She pushed his hand away"
3"His warm brown eyes met"
4"He let go of the"
5"He ducked between a bus"
6"She had watched Herrera leave"
7"She had followed him out"
8"She hadn’t seen a weapon."
9"His name sat in three"
10"Their canvas roofs sagged under"
11"He dropped the medical bag"
12"He looked back."
13"He slipped inside."
14"She picked it up."
15"It felt like bone."
16"His voice rose through the"
17"Her watch strap stuck to"
18"He looked at Herrera’s bag."
19"He waited with one hand"
ratio0.245
28.18% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount95
totalSentences110
matches
0"Rain struck the bonnet of"
1"Detective Harlow Quinn wiped a"
2"He carried a black medical"
3"His other hand gripped the"
4"The woman walked with her"
5"Quinn lifted her radio."
6"Static crackled, then a voice"
7"The woman stumbled."
8"Herrera caught her before she"
9"A dark stain covered her"
10"She pushed his hand away"
11"Quinn opened the car door."
12"Herrera looked across the road."
13"His warm brown eyes met"
14"He let go of the"
15"He ducked between a bus"
16"Quinn crossed after him, her"
17"A horn blared."
18"The van’s wing mirror passed"
19"Quinn pointed to the chemist’s"
ratio0.864
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences110
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences49
technicalSentenceCount1
matches
0"Quinn crossed after him, her shoes slapping through water that reached her ankles."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags6
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags3
fancyCount0
fancyTags(empty)
dialogueSentences39
tagDensity0.077
leniency0.154
rawRatio0
effectiveRatio0
87.9716%