Run: 2026-09-01-01M1EMC7TRT0XYJSRZ5FEG4XHP

Run Details

Duration:
23.2s
Cost:
$0.004156
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
The rain turned Soho into a black mirror. Detective Harlow Quinn’s leather soles slapped wet cobblestones, each strike echoing off the brick canyon. Salt-and-pepper hair clung to her sharp jawline. Her brown eyes tracked the figure three blocks ahead—Tomás Herrera, olive skin gleaming under streetlamps, curly dark hair plastered flat, Saint Christopher medallion bouncing against his chest. "Quinn!" Tomás’s voice cracked over the downpour. "You want me dead out here?" Quinn tightened her grip on the worn leather watch on her left wrist. The face was cracked but ticking. "Move." Tomás darted left, past the green neon sign of The Raven’s Nest—Silas’ bar—where the bulb flickered like a dying pulse above the entrance. Quinn followed. The door groaned; she shoved inside. The walls were covered with old maps and black-and-white photographs. Dim light hung in the corners like smoke. Tomás vanished behind a rack of dusty bottles. Quinn pushed through the crowd of shadowed patrons. Her military precision showed in the way she moved—not rushing, never rushing, just closing the gap. Tomás spoke from the dark: "You lost your partner to this, Harlow. You think I don’t know?" Quinn’s jaw tightened. "Shut up." She found the bookshelf. Tomás had pulled a volume; the shelf swung open, revealing a hidden back room accessible through paper and wood. No doors. Just an invitation. "You coming?" Tomás asked. Quinn stepped through. The passage smelled of mildew and old paper. Stairs cut into the earth, slick with condensation. She followed the scar running along Tomás’s left forearm as he climbed down, the Saint Christopher medallion swinging against his shirt. "We’re not in London anymore," Tomás said. Quinn glanced at her watch. "We never were." The air thickened. At the base of the stairs, a vast chamber yawned open—the abandoned Tube station beneath Camden. The Veil Market. Twisted rails rusted like old bones. Stalls lined the walls, selling enchanted goods, banned alchemical substances, information that could burn a career down to ash. Tomás pulled a bone token from his pocket. He held it up to a dark archway. "You need this," he said. Quinn looked at the token. She looked at him. "You never understood DS Morris," Tomás said. "He knew the cost." Quinn’s shoe scraped the stone. "The cost is your feet if you run." Tomás’s warm brown eyes met hers. "Then come down with me." Quinn stepped over the threshold. The market moved every full moon; tonight was the moon’s heart. She smelled iron and ozone, secrets and blood. The detective did not hesitate. She followed the suspect into unfamiliar and potentially dangerous territory, her hand on her watch, her jaw set, her eyes fixed on the man who knew her grief better than she wanted. "Keep walking," she said. The rain was gone. The dark was absolute.
created_at"2026-09-01T14:01:11.791Z" 9/1/2026, 2:01:11 PM
model"thinkingmachines/inkling-small-20260730"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency186
moderation_latency(null)
generation_time22930
tokens_prompt1104
tokens_completion3715
native_tokens_prompt991
native_tokens_completion3166
native_tokens_completion_images(null)
native_tokens_reasoning2976
native_tokens_cached256
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)
usage0.00415555
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.19.0; linux; x64))"
http_referer(null)
request_id"req-1788271271-5S7qdsoBEvw1GYtbW3M2"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1788271271-hOxDpLKrJSfMRdl9PiJG"
upstream_id"chatcmpl-RMql7tsluw00RPRovLBhV6e3"
provider_responses
0
endpoint_id"faa5ad38-86c9-427e-b1cb-99c43bc4827b"
is_byokfalse
latency78
model_permaslug"thinkingmachines/inkling-small-20260730"
provider_name"BaseTen"
status429
1
endpoint_id"d1d9657b-3c3a-49b5-8f81-33204e7c71c3"
id"chatcmpl-RMql7tsluw00RPRovLBhV6e3"
is_byokfalse
latency92
model_permaslug"thinkingmachines/inkling-small-20260730"
provider_name"DeepInfra"
status200
total_cost0.00415555
cache_discount0.0000896
upstream_inference_cost0
provider_name"DeepInfra"
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
totalTags8
adverbTagCount0
adverbTags(empty)
dialogueSentences14
tagDensity0.571
leniency1
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount470
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)
57.45% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount470
totalAiIsms4
found
0
word"echoing"
count1
1
word"gleaming"
count1
2
word"flickered"
count1
3
word"pulse"
count1
highlights
0"echoing"
1"gleaming"
2"flickered"
3"pulse"
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
narrationSentences49
matches(empty)
55.39% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount3
hedgeCount0
narrationSentences49
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences56
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen32
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords465
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions5
unquotedAttributions0
matches(empty)
0.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions35
wordCount395
uniqueNames13
maxNameDensity3.04
worstName"Tomás"
maxWindowNameDensity4.5
worstWindowName"Tomás"
discoveredNames
Soho1
Harlow1
Quinn10
Tomás12
Herrera1
Saint2
Christopher2
Raven1
Nest1
Tube1
Camden1
Veil1
Market1
persons
0"Harlow"
1"Quinn"
2"Tomás"
3"Herrera"
4"Saint"
5"Christopher"
6"Raven"
7"Nest"
8"Market"
places
0"Soho"
globalScore0
windowScore0.167
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences27
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount465
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences56
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs25
mean18.6
std16.41
cv0.882
sampleLengths
057
113
219
31
431
526
624
717
85
928
104
113
1237
137
148
1547
1616
175
189
1911
2013
2111
2261
234
248
98.10% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences49
matches
0"was gone"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs66
matches(empty)
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount4
semicolonCount3
flaggedSentences7
totalSentences56
ratio0.125
matches
0"Her brown eyes tracked the figure three blocks ahead—Tomás Herrera, olive skin gleaming under streetlamps, curly dark hair plastered flat, Saint Christopher medallion bouncing against his chest."
1"Tomás darted left, past the green neon sign of The Raven’s Nest—Silas’ bar—where the bulb flickered like a dying pulse above the entrance."
2"The door groaned; she shoved inside."
3"Her military precision showed in the way she moved—not rushing, never rushing, just closing the gap."
4"Tomás had pulled a volume; the shelf swung open, revealing a hidden back room accessible through paper and wood."
5"At the base of the stairs, a vast chamber yawned open—the abandoned Tube station beneath Camden."
6"The market moved every full moon; tonight was the moon’s heart."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount126
adjectiveStacks0
stackExamples(empty)
adverbCount0
adverbRatio0
lyAdverbCount1
lyAdverbRatio0.007936507936507936
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences56
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences56
mean8.3
std6.51
cv0.783
sampleLengths
08
115
27
327
47
56
613
76
81
923
102
116
1210
138
148
158
1616
1717
183
192
204
2119
222
233
244
253
268
278
2821
297
305
313
323
3316
343
356
3619
378
388
395
405
414
427
434
445
458
466
475
485
4911
58.93% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats2
diversityRatio0.375
totalSentences56
uniqueOpeners21
79.37% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences42
matches
0"Just an invitation."
ratio0.024
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount8
totalSentences42
matches
0"Her brown eyes tracked the"
1"Her military precision showed in"
2"She found the bookshelf."
3"She followed the scar running"
4"He held it up to"
5"She looked at him."
6"She smelled iron and ozone,"
7"She followed the suspect into"
ratio0.19
7.62% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount38
totalSentences42
matches
0"The rain turned Soho into"
1"Detective Harlow Quinn’s leather soles"
2"Salt-and-pepper hair clung to her"
3"Her brown eyes tracked the"
4"Tomás’s voice cracked over the"
5"Quinn tightened her grip on"
6"The face was cracked but"
7"Tomás darted left, past the"
8"The door groaned; she shoved"
9"The walls were covered with"
10"Tomás vanished behind a rack"
11"Quinn pushed through the crowd"
12"Her military precision showed in"
13"Tomás spoke from the dark:"
14"Quinn’s jaw tightened."
15"She found the bookshelf."
16"Tomás had pulled a volume;"
17"Quinn stepped through."
18"The passage smelled of mildew"
19"Stairs cut into the earth,"
ratio0.905
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences42
matches(empty)
ratio0
12.99% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences11
technicalSentenceCount2
matches
0"Stalls lined the walls, selling enchanted goods, banned alchemical substances, information that could burn a career down to ash."
1"She followed the suspect into unfamiliar and potentially dangerous territory, her hand on her watch, her jaw set, her eyes fixed on the man who knew her grief b…"
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags8
uselessAdditionCount0
matches(empty)
78.57% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags6
fancyCount1
fancyTags
0"Tomás spoke (speak)"
dialogueSentences14
tagDensity0.429
leniency0.857
rawRatio0.167
effectiveRatio0.143
78.2805%