Run: 2026-09-23-01M36WYW5RWW241FM6D4WYTXZQ

Run Details

Duration:
58.4s
Cost:
$0.001722
Provider:
Xiaomi

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 came sideways off the Thames wind and slapped the pavement in sheets. Quinn ran. The suspect had forty metres on her and the shape of a grey hood cutting through the market stalls on Inverness Street. She'd clocked him outside the shuttered chip shop on Camden High Street — the same lean build, the same left-handed fumble for his phone that DS Morris's old snitch had described three years ago in a report nobody had signed. Then he'd seen her face and the whole thing had gone loud. Her shoes hit puddles and the water climbed her shins. Leather watch soaked against her wrist. She keyed the radio without breaking stride. "Control, this is Quinn. I'm southbound on Inverness, single male, grey hoodie, heading for the Lock. I need units at the bridge." The reply broke apart in static. She thumbed the button again and got a burst of white noise shaped like a voice, then nothing. He vaulted a barrier stacked with unsold scarves and knocked a crate of bruised plums into the road. Fruit rolled under the parked vans. Quinn planted a hand on the barrier and swung over, landed wrong on a plum, twisted her ankle and kept moving because the alternative was losing him at the bend. "You're making this worse!" She pitched her voice down the street. He didn't look back. He cut right, down a side street that smelled of wet cardboard and hot fryer oil, and she followed him past a club queue where bodies turned and phones came up. "Police! Move!" The queue folded. A bouncer in a short-sleeve shirt reached for him and caught a fist in the throat for the trouble. Quinn hurdled the bouncer's knees and kept the grey hood in her eye-line. He was good. He didn't sprint in straight lines. He angled for bins, for lamp posts, for the corners of buildings where the light broke and her eye lost the edge of him. Twice he feinted toward traffic and she heard brakes howl and horns start up behind her. The streets began to tilt downward. Warehouse fronts gave way to hoardings covered in flyers and paint. She knew the shape of Camden in her legs after eighteen years and this was the ugly seam between the canal and the railway arches, where the streetlights thinned and the graffiti got older. He dropped through a hole in a fence. Quinn hit the fence with both hands and the wire bit her palms. The gap yawned at the bottom where someone had peeled the metal back from a concrete post. Beyond it, a service road ran down toward the canal, and past that, the mouth of the old Northern line entrance — closed in the nineties, sealed with steel shutters since before she'd made sergeant. The shutters stood open. A wedge of warm light spilled out across the wet tarmac and with it came a smell that didn't belong to any station she'd ever worked. Dried herbs and hot metal and something sweet underneath, like flowers left too long in a jar. Quinn keyed the radio again. "Control, suspect has entered the abandoned station under Camden. I am at the entrance. Confirm units." Static. Then a woman's voice, thin and far away, asking if anyone wanted to place a bet on the darts. The channel had been opened by somebody else entirely and had forgotten to close. She stared at the shutters. The light moved like something liquid behind them, throwing shapes she couldn't parse onto the brickwork opposite. A figure passed inside and for a heartbeat blocked the light — tall, thin, wearing a coat too fine for the weather. Then the figure moved on and the gap in the shutters grinned at her. Quinn drew her warrant card out of habit and put it back. She unbuttoned her coat and checked the kit on her belt. Torch. Baton. Cuffs. No vest, because she'd been eating noodles two streets away when the call had come through, off duty, off book, chasing a ghost from a file the department kept in a locked drawer. Her phone found no signal. She held it up and turned in a slow arc and the bars never climbed. Down the tunnel, a sound rose. Not voices. Voices would have been easier. It was the particular hum a crowd makes when it wants things — low, layered, shifting — and underneath it the clink of coins on stone and someone singing a tune in a language that sat wrong in her ear. Quinn thought about Morris. She thought about the three questions the post-mortem hadn't answered and the fourth one that nobody had dared to ask. She thought about the folder in her flat, the maps, the transit routes, the two previous locations where unlicensed trade had bloomed and vanished inside a lunar cycle. The paperwork had called it an unsanctioned market. Her gut had called it something else. She thought about radio silence and the distance to the nearest marked unit and the fact that if she walked in there, the Metropolitan Police would have no record of where Detective Harlow Quinn had gone. The bouncer's bruised throat would give a statement. The club queue had phone footage. The suspect would keep running and take his grey hood and his file and his secrets into whatever lived under this city, and the gap in the shutters would close behind him like it had never been there. Warm air breathed out of the tunnel and touched her wet face. The singing stopped. In the quiet, someone laughed, and the laugh echoed twice when it should have echoed once. Quinn pulled her coat straight. She switched the torch on and held it low so it wouldn't blind her and shut off the useless radio so it wouldn't crackle at the wrong moment. She stepped through the shutters and the market noise closed over her head like water.
created_at"2026-09-23T10:28:39.23Z" 9/23/2026, 10:28:39 AM
model"xiaomi/mimo-v2.6-pro-20260921"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency2681
moderation_latency(null)
generation_time58130
tokens_prompt1104
tokens_completion1715
native_tokens_prompt1005
native_tokens_completion1477
native_tokens_completion_images(null)
native_tokens_reasoning278
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)
usage0.001722165
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.20.0; linux; x64))"
http_referer(null)
request_id"req-1790159319-wz9fvIvIg9Hers0dfP8t"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1790159319-7GtEFzehilM5MqWrZDVI"
upstream_id"5702db17-6322-48c9-844e-c318322a6efd_70502812a76f432d810b8e459f90bec3"
provider_responses
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endpoint_id"4354fc36-b8ab-4ced-aa8c-0efb6b4523f1"
id"5702db17-6322-48c9-844e-c318322a6efd_70502812a76f432d810b8e459f90bec3"
is_byokfalse
latency2580
model_permaslug"xiaomi/mimo-v2.6-pro-20260921"
provider_name"Xiaomi"
status200
total_cost0.001722165
cache_discount(null)
upstream_inference_cost0
provider_name"Xiaomi"
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)
dialogueSentences4
tagDensity0.25
leniency0.5
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount989
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)
84.83% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount989
totalAiIsms3
found
0
word"silence"
count1
1
word"echoed"
count2
highlights
0"silence"
1"echoed"
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
narrationSentences68
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount1
narrationSentences68
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)
analyzedSentences71
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen40
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords994
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions4
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions23
wordCount950
uniqueNames12
maxNameDensity0.95
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Quinn"
discoveredNames
Thames1
Inverness1
Street2
Camden2
High1
Morris2
Northern1
Metropolitan1
Police1
Detective1
Harlow1
Quinn9
persons
0"Morris"
1"Police"
2"Harlow"
3"Quinn"
places
0"Thames"
1"Inverness"
2"Street"
3"Camden"
4"High"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences51
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount994
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences71
matches
0"past that, the"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs27
mean36.81
std19.34
cv0.525
sampleLengths
015
174
223
322
424
554
611
735
82
935
1049
1151
128
1365
1447
1521
1634
1758
1859
1920
2053
2167
2236
2352
2431
2533
2615
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences68
matches
0"been opened"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs149
matches
0"was losing"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount5
semicolonCount0
flaggedSentences4
totalSentences71
ratio0.056
matches
0"She'd clocked him outside the shuttered chip shop on Camden High Street — the same lean build, the same left-handed fumble for his phone that DS Morris's old snitch had described three years ago in a report nobody had signed."
1"Beyond it, a service road ran down toward the canal, and past that, the mouth of the old Northern line entrance — closed in the nineties, sealed with steel shutters since before she'd made sergeant."
2"A figure passed inside and for a heartbeat blocked the light — tall, thin, wearing a coat too fine for the weather."
3"It was the particular hum a crowd makes when it wants things — low, layered, shifting — and underneath it the clink of coins on stone and someone singing a tune in a language that sat wrong in her ear."
94.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount949
adjectiveStacks1
stackExamples
0"same left-handed fumble"
adverbCount23
adverbRatio0.02423603793466807
lyAdverbCount3
lyAdverbRatio0.003161222339304531
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences71
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences71
mean14
std10.64
cv0.76
sampleLengths
013
12
222
340
412
510
66
77
822
96
1018
1118
126
1330
1411
154
1631
172
183
1919
2013
213
226
2324
2416
256
2611
2734
288
2913
3017
3135
324
3326
3417
355
3616
371
3819
3914
405
4117
4222
4314
4412
4511
461
471
481
4933
55.87% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats7
diversityRatio0.39436619718309857
totalSentences71
uniqueOpeners28
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount4
totalSentences62
matches
0"Then he'd seen her face"
1"Twice he feinted toward traffic"
2"Then a woman's voice, thin"
3"Then the figure moved on"
ratio0.065
65.16% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount24
totalSentences62
matches
0"She'd clocked him outside the"
1"Her shoes hit puddles and"
2"She keyed the radio without"
3"She thumbed the button again"
4"He vaulted a barrier stacked"
5"She pitched her voice down"
6"He didn't look back."
7"He cut right, down a"
8"He was good."
9"He didn't sprint in straight"
10"He angled for bins, for"
11"She knew the shape of"
12"He dropped through a hole"
13"She stared at the shutters."
14"She unbuttoned her coat and"
15"Her phone found no signal."
16"She held it up and"
17"It was the particular hum"
18"She thought about the three"
19"She thought about the folder"
ratio0.387
40.65% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount52
totalSentences62
matches
0"Rain came sideways off the"
1"The suspect had forty metres"
2"She'd clocked him outside the"
3"Her shoes hit puddles and"
4"Leather watch soaked against her"
5"She keyed the radio without"
6"The reply broke apart in"
7"She thumbed the button again"
8"He vaulted a barrier stacked"
9"Fruit rolled under the parked"
10"Quinn planted a hand on"
11"She pitched her voice down"
12"He didn't look back."
13"He cut right, down a"
14"The queue folded."
15"A bouncer in a short-sleeve"
16"Quinn hurdled the bouncer's knees"
17"He was good."
18"He didn't sprint in straight"
19"He angled for bins, for"
ratio0.839
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences62
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences38
technicalSentenceCount1
matches
0"It was the particular hum a crowd makes when it wants things — low, layered, shifting — and underneath it the clink of coins on stone and someone singing a tune…"
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
totalTags0
88.0169%