Run: 2026-09-02-01M1HK28Y9TPGE3C44PX68KFVE

Run Details

Duration:
1m 56s
Cost:
$0.004743
Provider:
Alibaba

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 ran down the green glass of the Raven's Nest sign and fractured into needles on the street. Inside, the air smelled of malt, wet wool, and the iron tang old photographs had when damp crept through the walls. Detective Harlow Quinn sat with her back to the maps, watching the bookshelf by the bar. The volumes looked solid, encyclopaedias, shipping logs, atlases with cracked spines. They were a wall. The wall had moved twice in the past hour. Tomás Herrera had gone through it once. He came out with a black medical bag that had not been on his hip when he entered. Quinn's left thumb brushed the worn leather of her watch. Eighteen years. Enough years to learn that a door in a bar meant a second business. He noticed her at the table. His warm brown eyes narrowed, and his hand closed around the bag's strap. "You were in that room." The old barman polished a glass and did not look up. The photographs on the wall, faces from another century, a river frozen before the embankment was built, held their stillness. Herrera turned for the side passage. Quinn moved before the chair could complain. Her knee caught the table edge; the glass of water tipped and spilled across maps. Herrera had already hit the door with his shoulder. The hinges shrieked. She was three steps behind when he burst into the night. The alley was narrow and slick. A green bin stood against the brickwork, its lid open. Herrera went over it without slowing. His boots splashed through a shallow river of rainwater. A medallion bounced at his throat. St Christopher. Patron of travellers, useless at a run. Quinn took the bin's metal lid and dropped onto it. Her right calf pulled, a hot wire under skin, but her balance held. She came out into Charing Cross Road with the door at her back, the bar's sign bleeding neon onto the wet tarmac. Herrera was fifty yards ahead, heading west with the medical bag swinging. His curly hair was dark and flat. The scar on his forearm showed white when his cuff rode up. She did not shout. Shouts made people faster. She stepped into the flow of the street, counting the distance. Nine. Ten. Twelve. Rain needled her scalp. The closely cropped hair had grown since Morris died; it used to be nearly white at the temples, now it had grey threading through black. Eighteen years and a dead partner had left few places for vanity. Her jaw tightened, sharp as a blade in the light. Herrera turned into a side street lined with shuttered shops. A taxi driver swerved as he appeared from the kerb. The tyres hissed. The taxi horn bled into the rain. Herrera kept going, cutting across a forecourt where a black cab stood with its meter blinking, empty. He pulled a set of keys from his pocket and jabbed the fob. The cab's doors unlocked. Quinn reached the forecourt edge as the cab shot forward. Something slid from the medical bag and hit the wet tarmac. Herrera did not look back. She crouched. The object was small, pale, no bigger than a coin, carved with a mark that had been worn smooth by a thumb. Bone. Her unmarked Vauxhall sat two streets away, its roof black with rain. She got in, started the engine, and pulled into the lane the taxi had taken. Camden's arches opened like wet mouths. The taxi stopped by a closed station entrance with the roundel cracked and a boarded notice across the stairs. Quinn cut her headlights and pulled opposite. She watched Herrera pay through the window, then get out. He had the medical bag. He looked back once. She ducked beneath the dashboard. When she rose, he was gone down the stairs. Rain hammered the windscreen. The wipers made no sense to the street. No sign on the stairs, no platform notice, no guard. Only a metal door set into the brickwork, half-hidden behind a shuttered newsagent's. The door was new enough for the screws to be bright. A woman sat inside the entrance on a folding chair, visible through a gap. Her hands rested on a wooden board. On the board lay more tokens, strings of them, each cut with a different mark. Quinn approached. The woman looked up. Her eyes were brown and tired. "Market's for clients." The voice was flat, not hostile, not welcoming. Quinn held up the bone. "Found this." The woman did not move. "Found things get spent here." "Herrera went through." "Then he belongs inside." "He's carrying evidence." "The market carries what people need." The woman nodded at the token. "Hand it over." Quinn tightened her fingers. The rain ran into the gutter and blacked out the edge of the stairs. Through the open door's lower seam, she smelled damp, candles, something metallic, and another smell like a hospital corridor cleaned too hard. She looked down. A single light burned at the bottom. The walls were tiled with old cream squares and newer black paint. A discarded poster showed a Tube map with Camden Town crossed out. The platform beyond had been walled in long before London had admitted it was dead. "If I go through, what happens to the man with the medical bag?" "He answers for the room he enters." "He answers for me?" The woman's mouth shifted. "You want to see him in a market where your badge is paper? You follow a man into a place built on debt and secret, that's your choice. The door doesn't make it safe." Quinn's shoulders had stopped shaking, but the cold had moved into her bones. Eighteen years of procedure. Crime scene first. Backup. Warrant. Chain of custody. The city had soaked those things through, and the rain would take what remained. Morris's final words sat folded in her coat pocket. The paper had worn at the crease from her thumb. The woman lifted the board from her lap. "Token." Quinn dropped the bone into the hand. The gatekeeper turned it against the light, inspected the mark, then reached beneath her coat and produced another, longer, yellowed, tied with black cord. She handed it across. "Hold that at your throat. Walk two minutes down the first corridor. Do not touch goods. Do not take photographs. If the men at the stalls know you are police, they will not care." Quinn took the cord. "If they don't know I'm police?" "Then learn what you can before they do." The light at the bottom of the stairs blinked. Somewhere beyond it, Herrera's voice carried, too low to make words, followed by the hiss of rain dropping onto an iron grating. "Full moon," the woman said, closing the door against the street. "So we're under Camden. Last month it was Whitechapel." Quinn climbed the steps. Each one rang under her heel. The street noise faded, sirens, wet tyres, the cough of a motorcycle. The air thickened with incense, sweat, and cold stone. The token's cord rubbed the skin of her neck. At the bottom, the old platform had been broken into stalls. Green cloth hung over tiles. Glass jars sat on wooden crates. A man with gloves sold folded maps from a tray. A woman arranged vials in a case lined with dark velvet. A table held animal teeth drilled through with wire, and beside them, cards with prices written in a hand Quinn did not recognise. Herrera stood twenty feet ahead, talking to a figure behind a counter. The medical bag rested on the counter. He touched his forearm, the scar pulling under the cuff. The figure answered by pushing the bag toward him. A vendor beside him glanced toward the door, then at Quinn. His gaze snagged on the black cord, then slid to Herrera. Herrera lifted his chin. His warm brown eyes found her across the stalls. Quinn did not slow.
created_at"2026-09-02T17:35:59.952Z" 9/2/2026, 5:35:59 PM
model"qwen/qwen3.8-flash-20260826"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency840
moderation_latency(null)
generation_time116357
tokens_prompt1104
tokens_completion10983
native_tokens_prompt1103
native_tokens_completion10032
native_tokens_completion_images(null)
native_tokens_reasoning8300
native_tokens_cached1024
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)
usage0.004743274
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.19.0; linux; x64))"
http_referer(null)
request_id"req-1788370559-GSb6F4y0GcPmTtcaBIPv"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1788370559-2LKKNQP1vxIrmfY7OBmb"
upstream_id"chatcmpl-1d25d639-7933-9123-9753-964f3a65cba3"
provider_responses
0
endpoint_id"84b1e4a7-5aed-4464-818f-1994f0b4ee18"
id"chatcmpl-1d25d639-7933-9123-9753-964f3a65cba3"
is_byokfalse
latency824
model_permaslug"qwen/qwen3.8-flash-20260826"
provider_name"Alibaba"
status200
total_cost0.004743274
cache_discount0.000137216
upstream_inference_cost0
provider_name"Alibaba"
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
totalTags1
adverbTagCount0
adverbTags(empty)
dialogueSentences19
tagDensity0.053
leniency0.105
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1324
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)
92.45% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1324
totalAiIsms2
found
0
word"fractured"
count1
1
word"velvet"
count1
highlights
0"fractured"
1"velvet"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches1
maxInWindow1
found
0
label"eyes widened/narrowed"
count1
highlights
0"eyes narrowed"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences128
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount0
narrationSentences128
filterMatches
0"watch"
1"notice"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences146
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen34
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1324
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions3
unquotedAttributions0
matches(empty)
81.74% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions50
wordCount1172
uniqueNames17
maxNameDensity1.37
worstName"Quinn"
maxWindowNameDensity2.5
worstWindowName"Quinn"
discoveredNames
Raven1
Nest1
Harlow1
Quinn16
Herrera13
Christopher1
Charing1
Cross1
Road1
Morris2
Vauxhall1
Tube1
Camden2
Town1
London1
Rain3
Eighteen3
persons
0"Raven"
1"Harlow"
2"Quinn"
3"Herrera"
4"Morris"
5"Rain"
places
0"Christopher"
1"Charing"
2"Cross"
3"Road"
4"Vauxhall"
5"Camden"
6"Town"
7"London"
globalScore0.817
windowScore0.833
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences91
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1324
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences146
matches
0"learn that a"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs58
mean22.83
std19.69
cv0.863
sampleLengths
079
151
219
35
431
56
645
746
845
931
1073
1147
1217
1326
1425
1527
1651
1714
1846
1936
2012
213
228
235
242
255
265
273
284
293
306
316
323
3340
3449
3513
367
374
384
3934
4039
4119
428
431
447
4528
4634
474
486
498
88.82% Passive voice overuse
Target: ≤2% passive sentences
passiveCount6
totalSentences128
matches
0"was built"
1"been worn"
2"was gone"
3"were tiled"
4"been walled"
5"been broken"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs198
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount2
flaggedSentences2
totalSentences146
ratio0.014
matches
0"Her knee caught the table edge; the glass of water tipped and spilled across maps."
1"The closely cropped hair had grown since Morris died; it used to be nearly white at the temples, now it had grey threading through black."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1173
adjectiveStacks0
stackExamples(empty)
adverbCount19
adverbRatio0.01619778346121057
lyAdverbCount4
lyAdverbRatio0.0034100596760443308
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences146
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences146
mean9.07
std6.12
cv0.674
sampleLengths
018
121
216
311
44
59
67
718
810
92
1014
116
1213
135
1411
1520
166
177
1815
199
203
2111
226
2310
246
259
266
272
287
2910
3013
3122
3212
337
3412
354
364
3711
381
391
401
414
4225
4312
4410
4510
4610
473
487
4917
49.09% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats10
diversityRatio0.3356164383561644
totalSentences146
uniqueOpeners49
56.50% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences118
matches
0"Only a metal door set"
1"Somewhere beyond it, Herrera's voice"
ratio0.017
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount27
totalSentences118
matches
0"They were a wall."
1"He came out with a"
2"He noticed her at the"
3"His warm brown eyes narrowed,"
4"Her knee caught the table"
5"She was three steps behind"
6"His boots splashed through a"
7"Her right calf pulled, a"
8"She came out into Charing"
9"His curly hair was dark"
10"She did not shout."
11"She stepped into the flow"
12"Her jaw tightened, sharp as"
13"He pulled a set of"
14"Her unmarked Vauxhall sat two"
15"She got in, started the"
16"She watched Herrera pay through"
17"He had the medical bag."
18"He looked back once."
19"She ducked beneath the dashboard."
ratio0.229
19.32% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount104
totalSentences118
matches
0"Rain ran down the green"
1"Detective Harlow Quinn sat with"
2"The volumes looked solid, encyclopaedias,"
3"They were a wall."
4"The wall had moved twice"
5"Tomás Herrera had gone through"
6"He came out with a"
7"Quinn's left thumb brushed the"
8"He noticed her at the"
9"His warm brown eyes narrowed,"
10"The old barman polished a"
11"The photographs on the wall,"
12"Herrera turned for the side"
13"Quinn moved before the chair"
14"Her knee caught the table"
15"Herrera had already hit the"
16"The hinges shrieked."
17"She was three steps behind"
18"The alley was narrow and"
19"A green bin stood against"
ratio0.881
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences118
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences57
technicalSentenceCount1
matches
0"Herrera kept going, cutting across a forecourt where a black cab stood with its meter blinking, empty."
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
totalTags1
fancyCount0
fancyTags(empty)
dialogueSentences19
tagDensity0.053
leniency0.105
rawRatio0
effectiveRatio0
89.5970%