Run: 2026-02-19-01KHTRGS0NA2M32FTMARWAP9VK

Run Details

Duration:
13.2s
Cost:
$0.001447
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
Detective Harlow Quinn charged through the rain-soaked streets, her strides measured and precise despite the slick cobblestones underfoot. The relentless downpour plastered her short salt-and-pepper hair to her scalp, and her worn leather watch on her left wrist caught the dim light with each urgent step. Up ahead, a figure darted through the shadows, his olive skin and short curly dark brown hair intermittently visible under the flickering streetlights. Tomás Herrera, a former paramedic with ties to the city's underbelly. Quinn had tailed him from The Raven's Nest, a dimly lit bar in Soho, hoping he would lead her to the truth behind her partner's death three years ago. The suspect veered sharply into an alleyway, and Quinn followed, her heart pounding against her ribs. The alley was narrow, the old brick walls closing in on either side. Herrera's Saint Christopher medallion glinted as it bounced against his chest, his breath coming in short gasps as he ran. "Metropolitan Police! Stop where you are!" Quinn shouted, her voice cutting through the rain. Herrera glanced back, his warm brown eyes wide, then he ducked into a boarded-up entrance to an abandoned Tube station beneath Camden. Quinn hesitated for a moment. This was unfamiliar territory, potentially dangerous. But she had come too far to back down now. She followed him inside, her footsteps echoing in the damp tunnel. The air was heavy with the scent of mildew and decay, and the only light came from a faint green glow up ahead. Quinn moved cautiously, keeping to the shadows. As she approached, the tunnel opened into a hidden market. Enchanted goods and banned alchemical substances were displayed on rickety tables, and cloaked figures haggled over prices. The Veil Market, a supernatural black market that moved every full moon. Quinn had only heard rumors of its existence until now. She scanned the crowd, searching for Herrera. He had to be here somewhere, likely using the bone token required for entry. A hooded figure bumped into her, and she caught a glimpse of a scar running along the left forearm beneath the cloak. Herrera.
created_at"2026-02-19T10:53:18.746Z" 2/19/2026, 10:53:18 AM
model"nousresearch/hermes-3-llama-3.1-405b"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency374
moderation_latency(null)
generation_time13109
tokens_prompt1175
tokens_completion543
native_tokens_prompt996
native_tokens_completion451
native_tokens_completion_images(null)
native_tokens_reasoning0
native_tokens_cached0
num_media_prompt(null)
num_input_audio_prompt(null)
num_media_completion0
num_search_results(null)
origin"https://ncbench.com/"
is_byokfalse
finish_reason"stop"
native_finish_reason"stop"
usage0.001447
router(null)
provider_responses
0
endpoint_id"3280e8f8-0960-400c-8284-2e03a0bcb446"
id"cmpl-aa36ea174125409bad69e5bac5aa8aa3"
is_byokfalse
latency67
model_permaslug"nousresearch/hermes-3-llama-3.1-405b"
provider_name"DeepInfra"
status200
api_type"completions"
id"gen-1771498398-LmaudSsVzBb92xWYI4uZ"
upstream_id"cmpl-aa36ea174125409bad69e5bac5aa8aa3"
total_cost0.001447
cache_discount(null)
upstream_inference_cost0
provider_name"DeepInfra"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags1
adverbTagCount0
adverbTags(empty)
dialogueSentences1
tagDensity1
leniency1
rawRatio0
effectiveRatio0
71.67% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount353
totalAiIsmAdverbs2
found
0
adverb"sharply"
count1
1
adverb"cautiously"
count1
highlights
0"sharply"
1"cautiously"
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)
15.01% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount353
totalAiIsms6
found
0
word"charged"
count1
1
word"measured"
count1
2
word"pounding"
count1
3
word"footsteps"
count1
4
word"echoing"
count1
5
word"scanned"
count1
highlights
0"charged"
1"measured"
2"pounding"
3"footsteps"
4"echoing"
5"scanned"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches1
maxInWindow1
found
0
label"air was thick with"
count1
highlights
0"The air was heavy with"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences24
matches(empty)
83.33% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences24
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences24
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen30
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords349
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions1
unquotedAttributions0
matches(empty)
47.96% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions22
wordCount343
uniqueNames12
maxNameDensity2.04
worstName"Quinn"
maxWindowNameDensity2.5
worstWindowName"Quinn"
discoveredNames
Harlow1
Quinn7
Herrera5
Raven1
Nest1
Soho1
Saint1
Christopher1
Tube1
Camden1
Veil1
Market1
persons
0"Harlow"
1"Quinn"
2"Herrera"
3"Saint"
4"Christopher"
places
0"Raven"
1"Soho"
globalScore0.48
windowScore0.833
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences22
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount349
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences24
matches(empty)
41.39% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs8
mean43.63
std12.86
cv0.295
sampleLengths
046
163
249
314
443
541
649
744
90.64% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences24
matches
0"were displayed"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs50
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences24
ratio0
matches(empty)
92.65% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount347
adjectiveStacks0
stackExamples(empty)
adverbCount12
adverbRatio0.0345821325648415
lyAdverbCount10
lyAdverbRatio0.02881844380403458
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences24
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences24
mean14.54
std7.22
cv0.497
sampleLengths
018
128
223
311
429
516
613
720
814
922
105
116
1210
1311
1423
157
1610
1717
1812
1910
207
2114
2222
231
93.06% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats1
diversityRatio0.5833333333333334
totalSentences24
uniqueOpeners14
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences23
matches(empty)
ratio0
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount3
totalSentences23
matches
0"She followed him inside, her"
1"She scanned the crowd, searching"
2"He had to be here"
ratio0.13
46.96% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount19
totalSentences23
matches
0"Detective Harlow Quinn charged through"
1"The relentless downpour plastered her"
2"Tomás Herrera, a former paramedic"
3"Quinn had tailed him from"
4"The suspect veered sharply into"
5"The alley was narrow, the"
6"Herrera's Saint Christopher medallion glinted"
7"Quinn shouted, her voice cutting"
8"Herrera glanced back, his warm"
9"Quinn hesitated for a moment."
10"This was unfamiliar territory, potentially"
11"She followed him inside, her"
12"The air was heavy with"
13"Quinn moved cautiously, keeping to"
14"The Veil Market, a supernatural"
15"Quinn had only heard rumors"
16"She scanned the crowd, searching"
17"He had to be here"
18"A hooded figure bumped into"
ratio0.826
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences23
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences18
technicalSentenceCount1
matches
0"The Veil Market, a supernatural black market that moved every full moon."
0.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags1
uselessAdditionCount1
matches
0"Quinn shouted, her voice cutting through the rain"
0.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags1
fancyCount1
fancyTags
0"Quinn shouted (shout)"
dialogueSentences1
tagDensity1
leniency1
rawRatio1
effectiveRatio1
76.0893%