Run: 2026-03-11-01KKFCZQR02FHAXACNXKXST7PB

Run Details

Duration:
1m 18s
Cost:
$0.000902
Provider:
Together

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 slicked the cobblestones of Soho. It fell in sheets, drumming against the pavement. Detective Harlow Quinn ran. Her boots slapped the ground, heavy, determined. Water soaked through her trousers. She ignored the cold. Her leather watch on her left wrist clogged the rainwater. She checked it once every hundred steps. Three years. Too long since she checked her partner's time. Too long since the phone stopped ringing. Ahead, the suspect turned into a narrow alley. The green neon of the Raven's Nest flickered. It buzzed behind glass like a dying insect. Harlow didn't hesitate. She pushed through the doorframe. The smell hit her instantly. Stale tobacco. Wet wool. Ozone. She searched the shadows. The rain from outside stopped dead at the threshold. A velvet curtain hung from the ceiling. It pulled tight against the wind. "Tomás!" Harlow shouted. Her throat burned. No answer. She found him near the back. He huddled behind a rack of books. The obscure titles lined the shelves. He looked small. He wore a cream shirt. A stain on his collar. He didn't look like a paramedic. He looked like a man who had taken too many risks. "Where is he?" Harlow stepped closer. "I don't know." Tomás turned. His brown eyes darted around. The Saint Christopher medallion sat heavy on his chest. "You lied." "I hid." "Why?" "Because they want it." "What?" "The token." Tom reached up. He moved his left arm over his head. The scar on his forearm didn't show from the front. The skin had healed over, pale against olive. Harlow knew the knife attack meant he had carried something sharp once. Now he held a different shape. Harlow raised her weapon. The steel caught the light. "Don't touch that." Tomás stepped back. "Hand it over." "If I move, the room breaks." "What?" "If I break the token, the Market shifts." "You're lying." "I saved that clinic because the men didn't want patients. They want the goods." "The Veil Market?" "It's below." "I need the token." "I have to go down there." "Not alone." "Yes." "We won't find it." "I'll bring it." "You'll lose it again." "No." "Yes." "I said I won't." "I said you will." "No." "Yes." "I said no." "I said yes." "Wait." "Wait." "I'm coming." "I'm waiting." "Now." "Go." "I go." "Stop." "Stop." She turned. The shutter closed. The door locked. The shutter rattled. It slammed shut. The light faded. She looked at Tomás. He stood in front of the door. He held the token. The token glowed. It pushed against the glass. It broke the glass. The glass shattered. Harlow moved.
created_at"2026-03-11T21:31:30.958Z" 3/11/2026, 9:31:30 PM
model"qwen/qwen3.5-9b-20260310"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency149
moderation_latency(null)
generation_time78359
tokens_prompt1140
tokens_completion5521
native_tokens_prompt1065
native_tokens_completion5303
native_tokens_completion_images(null)
native_tokens_reasoning4796
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"
usage0.00090195
router(null)
provider_responses
0
endpoint_id"00d506d9-a5e9-4382-a2b9-d54732365594"
id"oagL42Q-6Ng1vN-9dadaa9e914d69fb"
is_byokfalse
latency149
model_permaslug"qwen/qwen3.5-9b-20260310"
provider_name"Together"
status200
user_agent"langchainjs-openai/1.0.0 ((node/v24.14.0; linux; x64))"
http_referer(null)
api_type"completions"
id"gen-1773264690-jc55UCrEHA77iK95lYbA"
upstream_id"oagL42Q-6Ng1vN-9dadaa9e914d69fb"
total_cost0.00090195
cache_discount(null)
upstream_inference_cost0
provider_name"Together"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags4
adverbTagCount1
adverbTags
0"Tomás stepped back [back]"
dialogueSentences42
tagDensity0.095
leniency0.19
rawRatio0.25
effectiveRatio0.048
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount434
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)
53.92% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount434
totalAiIsms4
found
0
word"determined"
count1
1
word"flickered"
count1
2
word"velvet"
count1
3
word"shattered"
count1
highlights
0"determined"
1"flickered"
2"velvet"
3"shattered"
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
narrationSentences62
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences62
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences100
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen14
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords434
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions7
unquotedAttributions0
matches(empty)
40.28% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions16
wordCount319
uniqueNames8
maxNameDensity2.19
worstName"Harlow"
maxWindowNameDensity2.5
worstWindowName"Harlow"
discoveredNames
Soho1
Harlow7
Quinn1
Raven1
Nest1
Tomás3
Saint1
Christopher1
persons
0"Harlow"
1"Quinn"
2"Raven"
3"Nest"
4"Tomás"
5"Saint"
6"Christopher"
places
0"Soho"
globalScore0.403
windowScore0.833
50.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences25
glossingSentenceCount1
matches
0"looked like a man who had taken too many"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount434
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences100
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs54
mean8.04
std13.71
cv1.706
sampleLengths
068
142
226
36
42
549
66
719
82
92
101
114
121
132
1447
159
166
173
186
191
208
212
2214
233
242
254
266
272
281
294
303
314
321
331
344
354
361
371
383
393
401
411
422
432
441
451
462
471
481
498
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences62
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs64
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences100
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount319
adjectiveStacks0
stackExamples(empty)
adverbCount11
adverbRatio0.034482758620689655
lyAdverbCount1
lyAdverbRatio0.003134796238244514
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences100
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences100
mean4.34
std2.77
cv0.638
sampleLengths
06
18
24
37
45
54
610
77
82
98
107
118
128
138
143
155
165
172
182
191
204
219
227
236
243
253
262
276
287
296
303
315
325
336
3411
356
365
375
389
392
402
411
424
431
442
453
468
4710
488
4912
44.33% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats14
diversityRatio0.35
totalSentences100
uniqueOpeners35
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences53
matches
0"Too long since she checked"
1"Too long since the phone"
ratio0.038
38.87% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount24
totalSentences53
matches
0"It fell in sheets, drumming"
1"Her boots slapped the ground,"
2"She ignored the cold."
3"Her leather watch on her"
4"She checked it once every"
5"It buzzed behind glass like"
6"She pushed through the doorframe."
7"She searched the shadows."
8"It pulled tight against the"
9"Her throat burned."
10"She found him near the"
11"He huddled behind a rack"
12"He looked small."
13"He wore a cream shirt."
14"He didn't look like a"
15"He looked like a man"
16"His brown eyes darted around."
17"He moved his left arm"
18"It slammed shut."
19"She looked at Tomás."
ratio0.453
0.00% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount49
totalSentences53
matches
0"Rain slicked the cobblestones of"
1"It fell in sheets, drumming"
2"Detective Harlow Quinn ran."
3"Her boots slapped the ground,"
4"Water soaked through her trousers."
5"She ignored the cold."
6"Her leather watch on her"
7"She checked it once every"
8"The green neon of the"
9"It buzzed behind glass like"
10"Harlow didn't hesitate."
11"She pushed through the doorframe."
12"The smell hit her instantly."
13"She searched the shadows."
14"The rain from outside stopped"
15"A velvet curtain hung from"
16"It pulled tight against the"
17"Her throat burned."
18"She found him near the"
19"He huddled behind a rack"
ratio0.925
94.34% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount1
totalSentences53
matches
0"Now he held a different"
ratio0.019
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences4
technicalSentenceCount0
matches(empty)
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
totalTags1
fancyCount1
fancyTags
0"Harlow shouted (shout)"
dialogueSentences42
tagDensity0.024
leniency0.048
rawRatio1
effectiveRatio0.048
87.3913%