Run: 2026-09-03-01M1KRQ33J7N5AM6PY7HAQ9VEF

Run Details

Duration:
20.3s
Cost:
$0.003388
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 hammered Soho and turned the pavement into a black mirror. The green neon sign above The Raven’s Nest sputtered, threw sick light across Detective Harlow Quinn’s face, and caught in her closely cropped salt-and-pepper hair. Water ran off the brim of her coat. The worn leather watch on her left wrist ticked against her knuckles. The suspect had not gone for the door. Quinn watched the figure slip past the bar, past the walls papered with old maps and black-and-white photographs, and push at the row of books behind the last table. The shelf gave. A narrow gap opened, smelling of damp plaster and cold air. The figure ducked through. Quinn moved. Military precision governed her steps. She crossed the floor in three strides, shouldered the shelf aside before the wood could settle back, and followed the figure down a stairwell that had no sign and no light except what bled from above. The air turned colder with each step. The stairs ended at a service alley behind Frith Street. Rain hit harder there, no awnings, no cover. A man in a dark coat broke into a run. Short curly dark brown hair flattened against his forehead. Olive skin darkened by wet. A Saint Christopher medallion caught the streetlamp and flashed once before disappearing under his collar. A scar ran along his left forearm, pale against his skin. Tomás Herrera. Quinn knew the name from files she was not supposed to have opened. Former paramedic. Lost his license. Off-the-books medical care for people who did not exist on paper. The clique. “Tomás Herrera.” He did not break stride. He vaulted a skip, landed hard on the far side, and kept going toward Camden. Quinn chased. Her boots struck puddles and sent up spray. The rain stung her eyes. Brown eyes narrowed against it. Sharp jaw set. She kept Herrera in sight between delivery vans and shuttered shops. He knew the streets. He cut through a service yard, slipped under a low gate, emerged on a side street where a crowd had gathered around a busker with a battered guitar. Herrera disappeared into the crowd. Quinn pushed through. A shoulder checked her. A bottle knocked from a hand. She kept moving. He was not in the crowd. A service hatch stood open at the end of the block, metal propped against a brick wall. The hatch led down. Cold air rose from it, carrying the smell of earth and old iron. A thin line of people moved down the steps, single file, each holding something in their palm. Bone. Quinn could see it in the lamplight. Small tokens, carved, yellowed. She caught a glimpse of Herrera’s coat disappearing down the stairwell. The Veil Market. She had read the reports. An abandoned Tube station beneath Camden. Moved locations every full moon. Entry required a bone token. Enchanted goods, banned alchemical substances, information sold by people who did not want police. The kind of place that ate detectives who went in alone. Three years ago she had lost DS Morris in a case that never made sense. The official report said overdose. The unofficial notes said something else. Something she could not name. She had not told anyone about the smell in the stairwell that night. It was the same smell rising now. A man in a long coat waited at the top of the stairs. He did not look at Quinn. He looked at her hands. “Token.” Quinn’s coat pockets were empty. She had come for a bar fight, for a tip about stolen artifacts, for a suspect who moved too easily through the night. She had no token. She had no backup. The rain hammered the street above and turned the stairwell into a throat. Herrera was down there. The clique was down there. Whatever she had been chasing since Morris died was down there. She took a step forward. The man did not move. The line of people continued down, their tokens catching the faint light. Someone laughed low in the dark. Water dripped from the ceiling far below. Quinn’s watch ticked. Her left wrist ached where the leather had worn thin. She thought of the shelf in the Raven’s Nest, the hidden back room used for clandestine meetings. She thought of the files on Herrera, the scar on his forearm, the medallion he never took off. “Detective Quinn,” the man said. “You don’t belong here.” She stared at the dark mouth of the stairs. Rain ran down her face. She could go back to the street, call it in, wait for a warrant she would never get for a place that moved every full moon. She could leave Herrera to vanish into a market that traded in things the Metropolitan Police could not name. Her hand found the edge of the hatch. Cold metal bit her palm. She stood at the top of the stairs, coat heavy with water, and did not move.
created_at"2026-09-03T13:53:13.851Z" 9/3/2026, 1:53:13 PM
model"meta/muse-glimmer-30b-20260810"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency528
moderation_latency(null)
generation_time20207
tokens_prompt1104
tokens_completion2472
native_tokens_prompt1005
native_tokens_completion2087
native_tokens_completion_images(null)
native_tokens_reasoning1284
native_tokens_cached304
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"
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usage0.00338801
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.19.0; linux; x64))"
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request_id"req-1788443593-IXH1br5PBilbjPOvs4ha"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1788443593-X2s6lLvjQTb8CgzaMuvJ"
upstream_id"oyodu36-7ArivV-a3553d4dbfd06798"
provider_responses
0
endpoint_id"e6c588fa-aef0-4e1b-bb7f-78185dd5b6da"
id"oyodu36-7ArivV-a3553d4dbfd06798"
is_byokfalse
latency172
model_permaslug"meta/muse-glimmer-30b-20260810"
provider_name"Together"
status200
total_cost0.00338801
cache_discount0.00009424
upstream_inference_cost0
provider_name"Together"
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
totalTags1
adverbTagCount0
adverbTags(empty)
dialogueSentences4
tagDensity0.25
leniency0.5
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount825
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)
93.94% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount825
totalAiIsms1
found
0
word"clandestine"
count1
highlights
0"clandestine"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches1
maxInWindow1
found
0
label"eyes widened/narrowed"
count1
highlights
0"eyes narrowed"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences91
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount0
narrationSentences91
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences94
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen36
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords825
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions2
unquotedAttributions0
matches(empty)
88.73% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions40
wordCount816
uniqueNames19
maxNameDensity1.23
worstName"Quinn"
maxWindowNameDensity2
worstWindowName"Quinn"
discoveredNames
Soho1
Raven2
Nest2
Detective1
Harlow1
Quinn10
Frith1
Street1
Saint1
Christopher1
Herrera7
Camden2
Veil1
Market1
Tube1
Morris2
Metropolitan1
Police1
Rain3
persons
0"Raven"
1"Nest"
2"Harlow"
3"Quinn"
4"Saint"
5"Christopher"
6"Herrera"
7"Morris"
8"Police"
9"Rain"
places
0"Soho"
1"Detective"
2"Frith"
3"Street"
4"Camden"
5"Market"
globalScore0.887
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences51
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount825
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences94
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs28
mean29.46
std22.26
cv0.755
sampleLengths
056
18
247
32
466
550
62
731
82
920
1066
1121
126
1363
1411
153
1646
1751
1824
191
2049
2120
2235
2348
249
2559
2613
2716
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences91
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs137
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences94
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount822
adjectiveStacks0
stackExamples(empty)
adverbCount10
adverbRatio0.012165450121654502
lyAdverbCount3
lyAdverbRatio0.0036496350364963502
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences94
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences94
mean8.78
std6.68
cv0.761
sampleLengths
011
125
28
312
48
529
63
711
84
92
105
1136
127
1310
148
1510
169
175
1815
1911
202
2113
222
233
2411
252
262
275
2815
292
308
315
325
333
3411
354
3628
375
383
394
406
413
426
4317
444
4513
4617
471
487
494
44.68% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats13
diversityRatio0.35106382978723405
totalSentences94
uniqueOpeners33
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences85
matches(empty)
ratio0
92.94% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount27
totalSentences85
matches
0"She crossed the floor in"
1"He did not break stride."
2"He vaulted a skip, landed"
3"Her boots struck puddles and"
4"She kept Herrera in sight"
5"He knew the streets."
6"He cut through a service"
7"She kept moving."
8"He was not in the"
9"She caught a glimpse of"
10"She had read the reports."
11"She had not told anyone"
12"It was the same smell"
13"He did not look at"
14"He looked at her hands."
15"She had come for a"
16"She had no token."
17"She had no backup."
18"She took a step forward."
19"Her left wrist ached where"
ratio0.318
30.59% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount73
totalSentences85
matches
0"The green neon sign above"
1"Water ran off the brim"
2"The worn leather watch on"
3"The suspect had not gone"
4"Quinn watched the figure slip"
5"The shelf gave."
6"A narrow gap opened, smelling"
7"The figure ducked through."
8"Military precision governed her steps."
9"She crossed the floor in"
10"The air turned colder with"
11"The stairs ended at a"
12"A man in a dark"
13"Olive skin darkened by wet."
14"A Saint Christopher medallion caught"
15"A scar ran along his"
16"Quinn knew the name from"
17"He did not break stride."
18"He vaulted a skip, landed"
19"Her boots struck puddles and"
ratio0.859
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences85
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences34
technicalSentenceCount2
matches
0"Off-the-books medical care for people who did not exist on paper."
1"Enchanted goods, banned alchemical substances, information sold by people who did not want police."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags1
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags1
fancyCount0
fancyTags(empty)
dialogueSentences4
tagDensity0.25
leniency0.5
rawRatio0
effectiveRatio0
88.3625%