Run: 2026-10-20-01M5C6H0D4V7DPZJSTCM35MN5Y

Run Details

Duration:
25.9s
Cost:
$0.001028
Provider:
Wafer

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
The rain hit the pavement in sheets, turning the neon reflections of Soho into a liquid smear of colour. Harlow Quinn’s shoes slapped through puddles, water soaking through the leather and chilling her ankles. Her coat was a dead weight on her shoulders. Her breath came in controlled, even bursts—years of discipline keeping panic at bay. Ahead, a shadow darted left, down an alley so narrow it was barely more than a drain between buildings. Tomás Herrera. She’d had him dead to rights outside the club, a twenty-minute tail that ended with him spotting her. He was fast, younger, but she was relentless. Eighteen years on the force didn’t build speed; it built stubbornness. She skidded around the corner, one hand slamming against the wet brick to steady herself. Her watch caught the light—the worn leather strap, the plain face. A gift from Morris. She pushed harder. The alley stank of rotting produce and wet cardboard. Her feet splashed through debris. Her quarry vaulted a stack of pallets, and she heard the clatter of his Saint Christopher medallion against a fire escape. He moved like a man who knew the terrain. She noted that. He wasn’t just running; he was navigating. Then he vanished. Harlow stopped dead. The alley ended in a chain-link fence, rusted and sagging, but there was no sign of him. She pulled her torch, the beam cutting through the downpour. A grate, set into the ground, was ajar, the dark maw of a maintenance shaft yawning beneath it. The metal edges were wet, not with rain—with the grease from a recent, hasty grip. She crouched. A low hum vibrated up from the tunnel, a sound out of place with the city’s rhythm. Something electrical, maybe. Or something else. She listened. Past the rain, past her own pulse. A distant, rhythmic thud. Footsteps. Two, maybe three sets. Not running. Walking. Purposeful. Her radio crackled. She thumbed the switch. “Control, this is Quinn. Suspect entered a subterranean maintenance shaft. Alley off Sherwood Street. No backup available. I’m going in.” A burst of static. “Copy, Quinn. No units in the vicinity. Exercise caution.” She clipped the radio back. Caution. Morris had been cautious. Then he’d been gone. She swung her legs into the shaft, finding the ladder rungs with her toes. The descent swallowed the noise of the rain, replacing it with a muffled, underground silence. The air grew thick, stale, cut with a metallic tang that coated her tongue. Her torch beam found brickwork, old, Victorian, then a modern concrete panel, then a thick cable running along the wall. The hum was louder here. Her feet hit solid ground. She killed the torch, letting her eyes adjust. The tunnel stretched left and right, but the footsteps had gone left. She followed. This wasn’t a simple drain. The floor was swept. The walls, covered in a century of grime, bore faint scuff marks—furniture moved, maybe. The occasional flicker of dim, hidden light source pulsed ahead, a sickly orange glow from low-watt bulbs strung along a wire. The tunnel opened into a wider space. She pressed herself against the wall, peering around a corner. It was a concourse—a dead, abandoned Tube station, tiled in that iconic, grimy white brickwork. An old ticket booth, its glass shattered, stood sentinel at the entrance to a platform that had long since seen its last train. But it was alive. Stalls and carts were clustered along the platform edge, their wares lit by the orange bulbs and a few hissing gas lamps. The air was a thick soup of smells: frying onions, incense, damp earth, and something sharper, like ozone and old blood. People moved among the stalls—a strange, quiet crowd in hooded coats, haggling in low murmurs. Not human, she realised with a cold, gut-level shift. A woman with skin the texture of bark. A man whose shadow moved independently of his body. The Veil Market. It was real. The whispers in the MIT canteen, the cases that went nowhere, the files stamped and locked away. A black market for the impossible, run beneath the city. And there was Herrera, on the far side of the concourse. He wasn’t running anymore. He was talking to a figure in a long, dark coat, their face obscured by a wide-brimmed hat. Herrera handed something over—a small, glinting object. The figure nodded, then melted into the crowd. Herrera turned, his gaze sweeping the market. He was on edge, looking for a tail. Harlow’s hand went to her radio. The signal would be dead down here. She knew it before she raised it. She was on her own. Three years ago, Morris had followed a lead into a place like this, a place that didn’t appear on the maps. He had gone in cautious and come out in a body bag with no explanation. The official report said ‘exsanguination by unknown means.’ The file was sealed. She could wait. Call it in when she surfaced, get a warrant, and hope the market was still there. It moves every full moon, the whispers said. That gave her a week, maybe less. Or she could follow. Now. Before Herrera slipped away into a crowd that would close ranks against her in a heartbeat. She looked at the strange, silent figures. She looked at the stall selling things that pulsed with their own light, things in jars that shouldn’t be alive. She looked at Tomás Herrera’s back as he moved toward a corridor leading deeper into the station’s bowels. She unclipped her radio and left it on a ledge by the tunnel mouth. No point carrying a dead weight. She straightened her coat, checked the grip of her service pistol at her hip, and stepped out into the concourse. The detective from the Metropolitan Police, with her sharp jaw and her old watch, walked into the Veil Market. The noise of the city above was gone. The only sound was the scuff of her shoes on the grimy tiles and the steady, purposeful thud of her own heart. She was in. Now she would see what Herrera was buying.
created_at"2026-10-20T08:23:26.378Z" 10/20/2026, 8:23:26 AM
model"deepseek/deepseek-v4.1-flash-20261017"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency356
moderation_latency(null)
generation_time25919
tokens_prompt1104
tokens_completion1953
native_tokens_prompt1029
native_tokens_completion1726
native_tokens_completion_images(null)
native_tokens_reasoning259
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)
usage0.00102764
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1792484606-ruVEk5O6EYWx7sruKHpE"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1792484606-OgjNM9QtCAnEpLYYcS1q"
upstream_id"chatcmpl-56f0267c1eb843b9889e62e660ea0ead"
provider_responses
0
endpoint_id"66d4851d-7738-4948-9d40-2ab6dc54f895"
id"chatcmpl-56f0267c1eb843b9889e62e660ea0ead"
is_byokfalse
latency356
model_permaslug"deepseek/deepseek-v4.1-flash-20261017"
provider_name"Wafer"
status200
total_cost0.00102764
cache_discount(null)
upstream_inference_cost0
provider_name"Wafer"
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)
dialogueSentences2
tagDensity0.5
leniency1
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1026
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)
17.15% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1026
totalAiIsms17
found
0
word"chilling"
count1
1
word"weight"
count2
2
word"navigating"
count1
3
word"maw"
count1
4
word"vibrated"
count1
5
word"pulse"
count1
6
word"rhythmic"
count1
7
word"footsteps"
count2
8
word"silence"
count1
9
word"flicker"
count1
10
word"pulsed"
count2
11
word"shattered"
count1
12
word"sentinel"
count1
13
word"glinting"
count1
highlights
0"chilling"
1"weight"
2"navigating"
3"maw"
4"vibrated"
5"pulse"
6"rhythmic"
7"footsteps"
8"silence"
9"flicker"
10"pulsed"
11"shattered"
12"sentinel"
13"glinting"
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
narrationSentences104
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences104
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences106
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen23
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1019
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions1
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions27
wordCount982
uniqueNames14
maxNameDensity0.71
worstName"Herrera"
maxWindowNameDensity2
worstWindowName"Herrera"
discoveredNames
Soho1
Quinn1
Herrera7
Morris3
Saint1
Christopher1
Victorian1
Tube1
Veil2
Market2
Tomás2
Metropolitan1
Police1
Harlow3
persons
0"Quinn"
1"Herrera"
2"Morris"
3"Saint"
4"Christopher"
5"Tomás"
6"Harlow"
places
0"Soho"
1"Veil"
2"Metropolitan"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences65
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1019
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences106
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs22
mean46.32
std34.95
cv0.755
sampleLengths
056
158
233
354
43
563
625
722
87
920
1013
1114
1268
1327
1444
1517
16159
1763
1873
1934
2066
21100
91.77% Passive voice overuse
Target: ≤2% passive sentences
passiveCount4
totalSentences104
matches
0"been gone"
1"was swept"
2"were clustered"
3"was gone"
0.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount5
totalVerbs156
matches
0"was navigating"
1"wasn’t just running"
2"wasn’t running"
3"was talking"
4"was buying"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount7
semicolonCount2
flaggedSentences9
totalSentences106
ratio0.085
matches
0"Her breath came in controlled, even bursts—years of discipline keeping panic at bay."
1"Eighteen years on the force didn’t build speed; it built stubbornness."
2"Her watch caught the light—the worn leather strap, the plain face."
3"He wasn’t just running; he was navigating."
4"The metal edges were wet, not with rain—with the grease from a recent, hasty grip."
5"The walls, covered in a century of grime, bore faint scuff marks—furniture moved, maybe."
6"It was a concourse—a dead, abandoned Tube station, tiled in that iconic, grimy white brickwork."
7"People moved among the stalls—a strange, quiet crowd in hooded coats, haggling in low murmurs."
8"Herrera handed something over—a small, glinting object."
94.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount794
adjectiveStacks1
stackExamples
0"iconic, grimy white brickwork."
adverbCount21
adverbRatio0.02644836272040302
lyAdverbCount3
lyAdverbRatio0.003778337531486146
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences106
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences106
mean9.61
std6.34
cv0.66
sampleLengths
019
115
29
313
419
52
618
78
811
915
1011
114
123
139
145
1521
169
173
187
193
203
2117
2210
2318
2415
252
2617
273
283
292
307
314
321
334
342
351
361
373
384
3920
404
419
425
431
444
454
4614
4715
4814
4920
43.40% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats14
diversityRatio0.33962264150943394
totalSentences106
uniqueOpeners36
70.92% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences94
matches
0"Then he vanished."
1"Then he’d been gone."
ratio0.021
62.55% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount37
totalSentences94
matches
0"Her coat was a dead"
1"Her breath came in controlled,"
2"She’d had him dead to"
3"He was fast, younger, but"
4"She skidded around the corner,"
5"Her watch caught the light—the"
6"She pushed harder."
7"Her feet splashed through debris."
8"Her quarry vaulted a stack"
9"He moved like a man"
10"She noted that."
11"He wasn’t just running; he"
12"She pulled her torch, the"
13"Her radio crackled."
14"She thumbed the switch."
15"She clipped the radio back."
16"She swung her legs into"
17"Her torch beam found brickwork,"
18"Her feet hit solid ground."
19"She killed the torch, letting"
ratio0.394
45.11% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount78
totalSentences94
matches
0"The rain hit the pavement"
1"Harlow Quinn’s shoes slapped through"
2"Her coat was a dead"
3"Her breath came in controlled,"
4"She’d had him dead to"
5"He was fast, younger, but"
6"She skidded around the corner,"
7"Her watch caught the light—the"
8"A gift from Morris."
9"She pushed harder."
10"The alley stank of rotting"
11"Her feet splashed through debris."
12"Her quarry vaulted a stack"
13"He moved like a man"
14"She noted that."
15"He wasn’t just running; he"
16"Harlow stopped dead."
17"The alley ended in a"
18"She pulled her torch, the"
19"A grate, set into the"
ratio0.83
100.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount2
totalSentences94
matches
0"Before Herrera slipped away into"
1"Now she would see what"
ratio0.021
91.84% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences42
technicalSentenceCount3
matches
0"She’d had him dead to rights outside the club, a twenty-minute tail that ended with him spotting her."
1"An old ticket booth, its glass shattered, stood sentinel at the entrance to a platform that had long since seen its last train."
2"She looked at the stall selling things that pulsed with their own light, things in jars that shouldn’t be alive."
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)
dialogueSentences2
tagDensity0.5
leniency1
rawRatio0
effectiveRatio0
83.8912%