Run: 2026-09-02-01M1HHVPYBDYWR27J3JY3AV9Z8

Run Details

Duration:
3m 5s
Cost:
$0.000000
Provider:
Alibaba

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 from the river and moved through Soho with a schedule no one had sanctioned. It filled gutter seams and lifted the green neon of The Raven's Nest above the entrance into wet threads across tarmac. Detective Harlow Quinn stood under the awning of a closed newsagent, her weight forward, watching the side panel beside the pub's main door. Her watch showed 02:18. The public door stayed shut. A side panel, cut to match the bookshelf inside, had no bell, no lock plate, and no customer's reason to use it. At 02:21, the panel swung out. A man in a dark coat stepped into the rain and pulled it shut. His sleeve moved and exposed a gold medallion at his throat, Saint Christopher small enough to miss unless someone held a photograph in the mind. Quinn's fingers tightened on the handle of her umbrella. "Tomás Herrera." He turned toward the canal. He listened the way a man does when footsteps sound wrong. Quinn stepped out. "Herr
created_at"2026-09-02T17:15:43.634Z" 9/2/2026, 5:15:43 PM
model"qwen/qwen3.8-flash-20260826"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency765
moderation_latency(null)
generation_time137061
tokens_prompt1104
tokens_completion14680
native_tokens_prompt1104
native_tokens_completion14680
native_tokens_completion_images(null)
native_tokens_reasoning14439
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(null)
native_finish_reason(null)
service_tier(null)
usage0
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.19.0; linux; x64))"
http_referer(null)
request_id"req-1788369343-dk1ufjDCMo5vHxQ03Rjz"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1788369343-pn8t2VxDrMlrZjgB2p9C"
upstream_id"chatcmpl-71483140-08d9-91ad-9dd1-3a8dd102c2fd"
provider_responses
0
endpoint_id"84b1e4a7-5aed-4464-818f-1994f0b4ee18"
id"chatcmpl-71483140-08d9-91ad-9dd1-3a8dd102c2fd"
is_byokfalse
latency765
model_permaslug"qwen/qwen3.8-flash-20260826"
provider_name"Alibaba"
status200
total_cost0
cache_discount(null)
upstream_inference_cost0
provider_name"Alibaba"
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
totalTags0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount167
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)
40.12% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount167
totalAiIsms2
found
0
word"weight"
count1
1
word"footsteps"
count1
highlights
0"weight"
1"footsteps"
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
narrationSentences13
matches(empty)
32.97% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences13
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences15
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen25
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords167
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions0
unquotedAttributions0
matches(empty)
58.54% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions9
wordCount164
uniqueNames7
maxNameDensity1.83
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Quinn"
discoveredNames
Soho1
Raven1
Nest1
Harlow1
Quinn3
Saint1
Christopher1
persons
0"Raven"
1"Harlow"
2"Quinn"
3"Saint"
4"Christopher"
places
0"Soho"
globalScore0.585
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences9
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount167
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences15
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs8
mean20.88
std22.22
cv1.064
sampleLengths
064
127
26
348
42
516
63
71
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences13
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs27
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences15
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount164
adjectiveStacks0
stackExamples(empty)
adverbCount1
adverbRatio0.006097560975609756
lyAdverbCount0
lyAdverbRatio0
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences15
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences15
mean11.13
std8.12
cv0.73
sampleLengths
016
121
223
34
45
522
66
714
825
99
102
115
1211
133
141
93.33% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats1
diversityRatio0.8
totalSentences15
uniqueOpeners12
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences13
matches(empty)
ratio0
66.15% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount5
totalSentences13
matches
0"It filled gutter seams and"
1"Her watch showed 02:18."
2"His sleeve moved and exposed"
3"He turned toward the canal."
4"He listened the way a"
ratio0.385
0.00% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount12
totalSentences13
matches
0"Rain came from the river"
1"It filled gutter seams and"
2"Detective Harlow Quinn stood under"
3"Her watch showed 02:18."
4"The public door stayed shut."
5"A side panel, cut to"
6"A man in a dark"
7"His sleeve moved and exposed"
8"Quinn's fingers tightened on the"
9"He turned toward the canal."
10"He listened the way a"
11"Quinn stepped out."
ratio0.923
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences13
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences7
technicalSentenceCount0
matches(empty)
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags0
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags0
83.0370%