Run: 2026-07-25-01KYCVHS5RC9G6RXK83CBQ4NPJ

Run Details

Duration:
1m 37s
Cost:
$0.003609
Provider:
DeepInfra

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 plastered Quinn’s hair flat against her skull. Her boots struck asphalt with a rhythm that matched her pulse. The suspect turned onto Carnaby, then vanished through an iron gate marked with rust and no sign. Quinn followed. The alley stank of wet cardboard and exhaust. Her left wrist bore the worn leather watch, its face cracked but ticking. She pressed against brick and peered around the corner. The gate led downward. Concrete steps, slick with grime and rainwater, descended into darkness. Green light flickered at the bottom, not neon exactly, but something older and sicklier. Quinn’s sharp jaw tightened. She had lost DS Morris three years ago in darkness like this. Unexplained. Supernatural, though she had not admitted the word until now. She placed her boot on the first step. The air changed immediately—warmer, carrying scents of iron and burned sugar. She descended. Her hand rested near her hip, near the service piece she no longer trusted in places like this. The steps ended in a chamber that used to be part of the Tube network. Abandoned tracks curved into shadow. Stalls lined the walls, selling things that glowed without batteries. The Veil Market. She knew the rumors. Entry required a bone token. She had none. That did not stop her. She moved between vendors. One sold alchemical dust in vials. Another traded in whispers and banned information. The walls wept moisture that smelled of copper. Quinn spotted the suspect. He wore an olive coat, dark hair plastered to his forehead. Tomás Herrera. Not her target tonight, but the man who knew where bodies disappeared. He turned, saw her brown eyes, and ran. Quinn pursued. Her military precision guided her through the crowd. She did not shout. Shouting ended chases in places like this. Herrera ducked behind a stall selling enchanted goods. Quinn cut left, blocked his path. He stopped, chest heaving. His Saint Christopher medallion swung against his throat. “You do not belong here, Detective,” he said. His Seville accent thickened with fear. Quinn’s jaw worked. “You led me here.” “I led you away from them.” She glanced past him. Figures moved near the old tracks, carrying bone tokens that clicked like teeth. “You administer treatments,” Quinn said. “Unauthorized. Supernatural patients.” Herrera’s hand went to his left forearm, to the scar running there from a knife attack. “I heal what the NHS refuses.” “Where is the clique?” Herrera shook his head. “Not a clique. A wound.” Quinn stepped closer. Rain dripped from her salt-and-pepper hair onto the concrete. The worn leather watch on her left wrist ticked loudly in the hollow space. “I lost Morris,” she said. “Here, or somewhere like here.” Herrera’s warm brown eyes softened. “Then you already know the price of following.” She looked past him, into the deeper darkness where the tracks disappeared. “I have to decide,” Quinn said, “whether this territory kills me or answers for him.” Herrera touched his medallion. “Choose quickly. The market moves every full moon. Tonight is the full moon.” Quinn stepped forward, past him, into the unfamiliar dark. Her breath came steady. Her sharp jaw set. She followed.<|end_message|>
created_at"2026-07-25T14:40:59.075Z" 7/25/2026, 2:40:59 PM
model"thinkingmachines/inkling-20260715"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency806
moderation_latency(null)
generation_time96887
tokens_prompt1104
tokens_completion848
native_tokens_prompt992
native_tokens_completion692
native_tokens_completion_images(null)
native_tokens_reasoning0
native_tokens_cached224
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)
usage0.00360868
router(null)
provider_responses
0
endpoint_id"d6b9d68e-115a-49d7-81cd-1ad1c6942b0e"
id"chatcmpl-R4MLM0rtmniPqUonBH9V4YhP"
is_byokfalse
latency73
model_permaslug"thinkingmachines/inkling-20260715"
provider_name"DeepInfra"
status200
user_agent"langchainjs-openai/1.0.0 ((node/v24.18.0; linux; x64))"
http_referer(null)
request_id"req-1784990459-keoIKStUoOdJfqI9E01F"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1784990459-TaK33v8af44ChdKZDAQq"
upstream_id"chatcmpl-R4MLM0rtmniPqUonBH9V4YhP"
total_cost0.00360868
cache_discount0.00018592
upstream_inference_cost0
provider_name"DeepInfra"
response_cache_source_id(null)
data_region"global"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags4
adverbTagCount0
adverbTags(empty)
dialogueSentences13
tagDensity0.308
leniency0.615
rawRatio0
effectiveRatio0
90.40% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount521
totalAiIsmAdverbs1
found
0
adverb"quickly"
count1
highlights
0"quickly"
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)
80.81% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount521
totalAiIsms2
found
0
word"pulse"
count1
1
word"flickered"
count1
highlights
0"pulse"
1"flickered"
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
emotionTells1
narrationSentences63
matches
0"d with fear"
97.51% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount0
narrationSentences63
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences72
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen18
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords520
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions4
unquotedAttributions0
matches(empty)
25.28% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions25
wordCount441
uniqueNames10
maxNameDensity2.49
worstName"Quinn"
maxWindowNameDensity4
worstWindowName"Quinn"
discoveredNames
Quinn11
Carnaby1
Morris1
Tube1
Veil1
Market1
Herrera6
Saint1
Christopher1
Seville1
persons
0"Quinn"
1"Carnaby"
2"Morris"
3"Herrera"
4"Saint"
5"Christopher"
places
0"Seville"
globalScore0.253
windowScore0.333
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences33
glossingSentenceCount0
matches(empty)
0.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches2
per1kWords3.846
wordCount520
matches
0"not neon exactly, but something older"
1"Not her target tonight, but the man who knew"
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences72
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs38
mean13.68
std7.81
cv0.571
sampleLengths
019
117
22
330
44
524
627
78
811
92
1018
1130
127
1313
1425
1515
1614
178
182
1919
2014
2112
2214
237
246
2517
268
2722
284
299
3026
3110
3213
3312
3415
3517
3617
372
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences63
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs88
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount1
semicolonCount0
flaggedSentences1
totalSentences72
ratio0.014
matches
0"The air changed immediately—warmer, carrying scents of iron and burned sugar."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount444
adjectiveStacks0
stackExamples(empty)
adverbCount10
adverbRatio0.02252252252252252
lyAdverbCount3
lyAdverbRatio0.006756756756756757
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences72
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences72
mean7.22
std4.1
cv0.567
sampleLengths
08
111
217
32
48
513
69
74
810
914
104
1112
121
1310
148
1511
162
1718
1815
195
2010
213
224
235
243
255
264
276
287
298
304
3111
322
3312
348
352
368
374
387
398
406
414
428
438
446
453
464
476
484
4913
65.74% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats1
diversityRatio0.4027777777777778
totalSentences72
uniqueOpeners29
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences53
matches(empty)
ratio0
69.06% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount20
totalSentences53
matches
0"Her boots struck asphalt with"
1"Her left wrist bore the"
2"She pressed against brick and"
3"She had lost DS Morris"
4"She placed her boot on"
5"Her hand rested near her"
6"She knew the rumors."
7"She had none."
8"She moved between vendors."
9"He wore an olive coat,"
10"He turned, saw her brown"
11"Her military precision guided her"
12"She did not shout."
13"He stopped, chest heaving."
14"His Saint Christopher medallion swung"
15"His Seville accent thickened with"
16"She glanced past him."
17"She looked past him, into"
18"Her breath came steady."
19"Her sharp jaw set."
ratio0.377
16.60% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount47
totalSentences53
matches
0"Rain plastered Quinn’s hair flat"
1"Her boots struck asphalt with"
2"The suspect turned onto Carnaby,"
3"The alley stank of wet"
4"Her left wrist bore the"
5"She pressed against brick and"
6"The gate led downward."
7"Quinn’s sharp jaw tightened."
8"She had lost DS Morris"
9"She placed her boot on"
10"The air changed immediately—warmer, carrying"
11"Her hand rested near her"
12"The steps ended in a"
13"Stalls lined the walls, selling"
14"The Veil Market."
15"She knew the rumors."
16"Entry required a bone token."
17"She had none."
18"That did not stop her."
19"She moved between vendors."
ratio0.887
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences53
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences18
technicalSentenceCount1
matches
0"Her boots struck asphalt with a rhythm that matched her pulse."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags4
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags4
fancyCount0
fancyTags(empty)
dialogueSentences13
tagDensity0.308
leniency0.615
rawRatio0
effectiveRatio0
81.5133%