Run: 2026-02-17-01KHMMG1MHTBA03HPVBWX2R3AN

Run Details

Duration:
1.9s
Cost:
$0.000662
Provider:
Novita

Model input

System
You are an expert novelist with a gift for vivid, immersive prose. You write in tight third-person limited point of view with a strong narrative voice. Your prose is clean, precise, and engaging — you favor strong verbs, concrete details, and natural-sounding dialogue. You will be given a story bible (codex) containing canonical details about characters, locations, items, and lore. Use these details to ground your writing in the established world. Stay consistent with the codex. Write only the prose of the scene. Do not include titles, chapter headings, markdown formatting, or any commentary before or after the scene.
User/Human
<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> 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.

Model response

AI
A slow drizzle begins to fall on the wet cobblestones, the detective’s gloved hand clutching the back of her neck. the early evening light casting an eerire glow from a neon sign across the street. <backstory>Born in Seville, Spain, Detectives find themselves in a maze of mannequins and shadowy figures
created_at"2026-02-17T01:47:36.245527+00:00" 2/17/2026, 1:47:36 AM
model"microsoft/wizardlm-2-8x22b"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency683
moderation_latency(null)
generation_time1807
tokens_prompt917
tokens_completion79
native_tokens_prompt988
native_tokens_completion80
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"
usage0.00066216
router(null)
provider_responses
0
id"3251cbe1-e06b-47d0-8db2-91bb8781b272"
status200
is_byokfalse
latency683
endpoint_id"92d0658f-b48a-4746-96e7-75ea71d5e060"
provider_name"Novita"
model_permaslug"microsoft/wizardlm-2-8x22b"
api_type"completions"
id"gen-1771292853-akByqQnDsRU4bvN29yYt"
upstream_id"3251cbe1-e06b-47d0-8db2-91bb8781b272"
total_cost0.00066216
cache_discount(null)
upstream_inference_cost0
provider_name"Novita"

Evaluation details

Result Evaluator Details Meta Data
100.0000% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags0
adverbTagCount0
adverbTags(empty)
100.0000% AI-ism adverb frequency
Target: <1% AI-ism adverbs (57 tracked)
wordCount50
totalAiIsmAdverbs0
found(empty)
highlights(empty)
100.0000% AI-ism character names
Target: 0 AI-default names (17 tracked, −20% each)
codexExemptions(empty)
found(empty)
100.0000% AI-ism location names
Target: 0 AI-default location names (33 tracked, −20% each)
codexExemptions(empty)
found(empty)
100.0000% AI-ism word frequency
Target: <1% AI-ism words (140 tracked)
wordCount50
totalAiIsms0
found(empty)
highlights(empty)
100.0000% Cliche density
Target: ≤1 cliche(s) per 800-word window
totalCliches0
maxInWindow0
found(empty)
highlights(empty)
100.0000% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences3
matches(empty)
7.2464% Filter word density
Target: ≤12% sentences with filter/hedge words
filterCount0
hedgeCount1
narrationSentences3
filterMatches(empty)
hedgeMatches
0"begins to"
100.0000% Overuse of "that" (subordinate clause padding)
Target: ≤10% sentences with "that" clauses
thatCount0
totalSentences3
matches(empty)
100.0000% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs2
mean0
std0
cv0
sampleLengths
035
115
100.0000% Passive voice overuse
Target: ≤5% passive sentences
passiveCount0
totalSentences3
matches(empty)
100.0000% Past progressive (was/were + -ing) overuse
Target: ≤10% past progressive verbs
pastProgressiveCount0
totalVerbs5
matches(empty)
100.0000% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount50
adjectiveStacks0
stackExamples(empty)
adverbCount0
adverbRatio0
lyAdverbCount1
lyAdverbRatio0.02
100.0000% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences3
echoCount0
echoWords(empty)
100.0000% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences3
mean0
std0
cv0
sampleLengths
020
115
215
100.0000% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats0
diversityRatio1
totalSentences3
100.0000% Dialogue tag variety (said vs. fancy)
Target: ≤30% fancy dialogue tags
totalTags0
fancyCount0
fancyTags(empty)
94.5439%