Run: 2026-09-10-01M25DWBZBSN93Z9Y04CV3RK89

Run Details

Duration:
37.1s
Cost:
$0.003831
Provider:
DeepSeek

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="4414" name="Eva Kowalski" aliases="Eva, Evie" pronouns="she/her" species="Human" storyRole="Best Friend"> <eyeColor>green</eyeColor> <hair>curly red</hair> <height>5'4"</height> <age>26</age> <glasses>round glasses</glasses> <satchel>worn leather satchel full of books</satchel> <freckles>freckled complexion</freckles> <nervousHabit>tucking hair behind her left ear</nervousHabit> <description>Aurora's childhood friend and an occult researcher.</description> <backstory>Moved to London two years before Aurora and works as a research assistant at the British Museum's restricted archives. She holds a master's degree in Ancient History from Oxford.</backstory> </character> <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> <item id="2004" name="Veil Compass" aliases="the Compass, shadow compass" type="Tool" origin="The Veil Market"> <casingMaterial>small brass compass</casingMaterial> <faceMarkings>face etched with protective sigils</faceMarkings> <needle>points toward the nearest supernatural rift or portal</needle> <patina>casing has a patina of verdigris</patina> <creator>crafted by a Shade artisan</creator> <description>A compass attuned to supernatural energy.</description> </item> </codex> Continue the story using the following instructions: <instructions> Write a fully fleshed out scene of at least 1000 words in the mystery genre. A detective arrives at a crime scene in an unusual location. Something is wrong — the evidence doesn't add up. A colleague is present, offering a different interpretation. Through careful observation and deduction, the detective begins to see what others have missed. 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 service stair behind the shuttered kebab shop on Buck Street dropped further than any Tube station had a right to. Quinn counted the landings. Four. The handrail iced her palm through the glove. Twenty metres down, the air turned mineral and wet, the smell of a well. Scene-of-crime lights threw hard white against oxide walls. Uniforms clustered by the old ticket hall, their radios hissing. A paramedic leaned on a tiled pillar with her arms folded. Nobody needed her. DC Ollie Tanner met Quinn at the foot of the stairs and held out a paper suit. “Morning, guv. Barely.” She pulled the suit over her coat. The cuffs hung past her wrists. “Talk.” “Male, mid-twenties. Two urban explorers found him at half four. Came down through a vent in the men’s toilets. One of them checked for a pulse.” “Name?” “No ID. Nothing in his pockets.” “Then how did he get down here?” Tanner shrugged inside his own suit. “That’s the thing.” He walked her through the ticket hall, past turnstiles seized open, past a window with a hand-painted sign flaking off the glass. The platform stretched long and pale. White tiles, gone the colour of weak tea, from the ceiling to the track. The body lay on the platform, supine, arms at his sides. Charcoal wool suit, cut close, good cloth. No coat. No shoes. His face was turned three degrees to the left, as if he had been about to say something and thought better of it. Quinn crouched. The tiles around him carried no scuff, no drag, no bloom of blood. Ozone hung over the man, sharp and clean, the air after a lightning strike. “Shoes?” Tanner pointed. Two leather brogues sat by the platform edge, toes squared at the tunnel mouth. “Lined up like he was waiting for a train.” “Or like someone wanted us to think that.” She leaned closer. A bone disc rested on his open palm, pale and smooth as a piano key. His fingers lay relaxed beneath it. Not curled. Not gripping. She lifted the disc with a pen. No print oil on the bone. No wear on the edges. Fresh cut. Tanner watched her. “That’s a bone token. Veil Market entry. We’ve had them on the board since the Kingsway job.” “Are they all this clean?” He frowned. “No. The ones we seized were yellow. Greasy. Handled.” “Exactly.” She bagged the token and sealed it. “This one has never been through a pocket in its life.” “You think it’s a plant.” “I think it’s a prop. And I think the man who staged it read about the market in a newspaper.” Tanner stepped back, arms folded, and swept the platform with a slow turn. “You’re not seeing this, guv. Look at the floor.” She followed his arm. A chalk figure sprawled across the tiles in a rough circle, eight arms, hooked and sharp, drawn in fat white strokes. Salt crusted the gaps between the lines. A stub of candle sat at every point. “Ritual,” Tanner said. “Candles, salt, circle. This is the clique. This is their work.” Quinn walked the circumference without stepping inside. The chalk ran thick in places, thin in others, the way a hand draws when it copies a shape from a photograph and understands nothing of its weight. She stopped at the northern arm of the star. “Where’s north?” Tanner checked his phone. “Compass says that way.” He nodded at the tunnel. “Then the circle is rotated ninety degrees.” She pointed to the arm aimed at the exit stair. “That’s the northern point of the figure. Oriented to the stair, not the pole. Whoever drew this put it down to face the way they came in. So they could find it again on the way out.” “That’s a guess.” “It’s geometry.” She skirted the salt and crouched beside the nearest candle. The wax had pooled on the tile and hardened with no wick curl, no blackening at the tip. She snapped a glove off and pressed two fingers to it. Smooth. Cool. She stood and studied the body from the feet up. Lividity had set deep in his back and shoulders, a dark static bloom fixed in place. Fixed meant hours. She lifted a wrist and let it drop. The arm fell heavy. Rigor had come and gone. “He wasn’t killed here.” Tanner pulled his mask down to scratch his jaw. “The skin’s intact.” “Because he wasn’t stabbed here, strangled here, or knocked down here. There’s no pressure mark on the back of his skull, no abrasion on his heels, no blood beneath him.” She opened a hand at the tiles. “A man dies on these stones, the stones tell you. These say he arrived after.” “So the killers brought him down four landings through a vent?” “No.” She turned to the tunnel mouth, black and swallowing, and walked towards it. “They came the other way.” The tunnel curved off into dark. A rusted ladder bolted to the wall climbed towards a maintenance shaft. At its foot, in the dust, lay a thin green ring, verdigris bright against the grime. She photographed it, bagged it, crouched low over the spot. “Brass. Circular. Something sat here and was taken.” She held the bag to the light. Inside, a small compass rested in the plastic. Its casing was a crust of verdigris. Its face carried fine sigils she did not recognise. She turned the bag in her hand. The needle swung, then settled. It pointed down the tunnel. Not north. She tucked the bag into her coat pocket and straightened. “The market moves every full moon. That was three nights ago. So the station has been empty three nights.” “Right.” “Someone knew. Someone knew there would be nobody down here to watch them work, and nobody to argue when we found a body holding a token.” Tanner’s face changed. “You’re saying the scene is a message.” “I’m saying it’s addressed.” She looked back at the chalk star, the dead candles, the shoes lined up like a joke about a train that would never come. “A ritual killing points at the clique. The clique points at the market. The market points at a place we can’t raid because it isn’t there any more.” She looked at the body once more. Young face. Unbruised. At peace. Somebody’s son, staged like furniture. “The people who did this don’t believe any of it. They read about the market the way Tanner read about it. Signs and tokens and salt in a circle. Copies of copies.” “So who?” “Someone who can’t get through the door.” She nodded at the token in its bag. “And wants us to think they can.” Tanner stared at her. “That’s a lot from a compass.” “It’s a lot from a compass that doesn’t point north.” She turned back to the body. “Get the pathologist down. Tell her to bring a thermometer I can read. And find me every copy of that piece the Standard ran last autumn. The one with the drawing.”
created_at"2026-09-10T10:30:12.211Z" 9/10/2026, 10:30:12 AM
model"deepseek/deepseek-v4.1-flash-20260910"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency740
moderation_latency(null)
generation_time37020
tokens_prompt1112
tokens_completion6937
native_tokens_prompt1059
native_tokens_completion6340
native_tokens_completion_images(null)
native_tokens_reasoning4765
native_tokens_cached896
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)
usage0.003831138
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.20.0; linux; x64))"
http_referer(null)
request_id"req-1789036212-eC8oGoLxDUzrVzbulJdt"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1789036212-epa463sriKBPhhJzzTHJ"
upstream_id"8b96b979-ca79-4e19-8b84-dbbfe8326ce3"
provider_responses
0
endpoint_id"fd104bc8-535a-4d31-a847-a8e402e86a02"
id"8b96b979-ca79-4e19-8b84-dbbfe8326ce3"
is_byokfalse
latency343
model_permaslug"deepseek/deepseek-v4.1-flash-20260910"
provider_name"DeepSeek"
status200
total_cost0.003831138
cache_discount0.000131712
upstream_inference_cost0
provider_name"DeepSeek"
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
totalTags11
adverbTagCount1
adverbTags
0"She turned back [back]"
dialogueSentences47
tagDensity0.234
leniency0.468
rawRatio0.091
effectiveRatio0.043
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1174
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)
87.22% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1174
totalAiIsms3
found
0
word"pulse"
count1
1
word"weight"
count1
2
word"standard"
count1
highlights
0"pulse"
1"weight"
2"standard"
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
narrationSentences87
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences87
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences123
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen37
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1174
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions7
unquotedAttributions0
matches(empty)
79.38% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions18
wordCount708
uniqueNames6
maxNameDensity1.41
worstName"Tanner"
maxWindowNameDensity2.5
worstWindowName"Tanner"
discoveredNames
Buck1
Street1
Tube1
Ollie1
Tanner10
Quinn4
persons
0"Buck"
1"Street"
2"Ollie"
3"Tanner"
4"Quinn"
places(empty)
globalScore0.794
windowScore0.833
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences55
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1174
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences123
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs52
mean22.58
std17.28
cv0.765
sampleLengths
048
132
217
33
414
526
61
76
87
99
1042
1145
1229
131
1425
158
1648
1720
185
1911
2019
215
2220
2322
2440
2514
2644
272
2813
2954
303
3143
3246
334
3412
3552
3611
3719
3844
3958
4010
4119
421
4326
4410
4556
4617
4732
482
4922
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences87
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs119
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences123
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount711
adjectiveStacks0
stackExamples(empty)
adverbCount11
adverbRatio0.015471167369901548
lyAdverbCount0
lyAdverbRatio0
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences123
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences123
mean9.54
std7.94
cv0.832
sampleLengths
021
14
21
38
414
58
610
711
83
917
103
117
126
131
1426
151
166
177
186
193
2022
216
2214
2311
247
252
262
2723
282
2913
3014
311
322
3314
349
358
363
3715
386
392
402
417
426
435
442
453
4617
475
482
499
75.88% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats5
diversityRatio0.4796747967479675
totalSentences123
uniqueOpeners59
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences71
matches(empty)
ratio0
62.25% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount28
totalSentences71
matches
0"She pulled the suit over"
1"He walked her through the"
2"His face was turned three"
3"She leaned closer."
4"His fingers lay relaxed beneath"
5"She lifted the disc with"
6"She bagged the token and"
7"She followed his arm."
8"She stopped at the northern"
9"He nodded at the tunnel"
10"She pointed to the arm"
11"She skirted the salt and"
12"She snapped a glove off"
13"She stood and studied the"
14"She lifted a wrist and"
15"She opened a hand at"
16"She turned to the tunnel"
17"She photographed it, bagged it,"
18"She held the bag to"
19"Its casing was a crust"
ratio0.394
16.34% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount63
totalSentences71
matches
0"The service stair behind the"
1"Quinn counted the landings."
2"The handrail iced her palm"
3"Scene-of-crime lights threw hard white"
4"Uniforms clustered by the old"
5"A paramedic leaned on a"
6"Nobody needed her."
7"DC Ollie Tanner met Quinn"
8"She pulled the suit over"
9"The cuffs hung past her"
10"Tanner shrugged inside his own"
11"He walked her through the"
12"The platform stretched long and"
13"The body lay on the"
14"Charcoal wool suit, cut close,"
15"His face was turned three"
16"The tiles around him carried"
17"Ozone hung over the man,"
18"She leaned closer."
19"A bone disc rested on"
ratio0.887
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences71
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences25
technicalSentenceCount0
matches(empty)
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags11
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags1
fancyCount0
fancyTags(empty)
dialogueSentences47
tagDensity0.021
leniency0.043
rawRatio0
effectiveRatio0
87.3691%