Run: 2026-09-03-01M1KS3QE9K3WFNH59F2GQABWD

Run Details

Duration:
23.0s
Cost:
$0.003613
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 of the Raven’s Nest buzzed above the doorway and threw a sickly light across the puddles. Harlow Quinn stood under the awning with the collar of her coat up and her leather watch catching the glare. She watched the door. A bell jangled. Tomás Herrera stepped out into the wet. He wore a dark jacket, his short curly hair plastered to his forehead. The Saint Christopher medallion at his throat glinted once and vanished against his olive skin. The scar on his left forearm showed where his sleeve rode up. Quinn moved. “Tomás.” He turned his head. Warm brown eyes found her. He did not run at first. He lifted his hands, palms out. “Detective.” She closed the distance between them in three strides. The rain slicked her cropped salt-and-pepper hair to her scalp. Her jaw set. “You work for the Nest. You patch up people who shouldn’t be patched up. That’s a crime.” “You know I lost my license.” “I know you lost your license for treating patients the NHS doesn’t recognize.” He shifted his weight. The medallion swung. “Then you know I don’t have a clinic.” “You have a back room.” The bell above the Nest jangled again as someone left. Tomás glanced toward the street. A black cab rolled past, lights smeared by rain. “Come inside. It’s raining.” “I’m done asking.” He smiled without warmth. He took a step back toward the curb. Quinn blocked him. “You’re not going to Camden with that.” He blinked. “You followed me from the bar.” “I followed you from the file.” He exhaled through his nose. He looked past her shoulder at the bar’s entrance, then down the street. “Don’t.” She grabbed his sleeve. He jerked free. The movement was clean, trained. He broke toward the left, into the alley between the shops. Quinn went after him. The alley narrowed and smelled of garbage and wet brick. Neon bled off the walls. He moved fast for a man with a medical bag slung over his shoulder. He cut left, then right, through a service gate that stood ajar. Quinn vaulted it. Rain needled her face. They hit the main drag again. He crossed at the light, rain whipping his jacket. She ran the line, boots striking pavement. A bus hissed past. She kept him in sight between the crowd. He ducked under a market stall awning, slipped through a gap in the stalls, emerged on the other side. He was not trying to lose her. He was leading her. She caught up on the corner of Great Portland Street. He stopped. He pressed his back to a brick wall and stared at her, breathing hard. “You have a warrant?” “I have a question about DS Morris.” His eyes flickered. The medallion caught the streetlight. “You don’t want an answer.” “I want a name.” He shook his head. “You don’t know what you’re chasing.” She stepped in. He stepped back. The wall ended at a stairwell that dropped down. A metal door at the bottom, propped open by a brick. Cold air rose from it. He moved first. “Tomás.” He didn’t look back. Quinn followed the sound of his boots on the metal stairs. The stairwell smelled of damp concrete and old oil. The rain faded to a distant roar above. At the bottom, a tunnel stretched out, lit by bare bulbs strung on wire. The walls bore graffiti and water stains. A ticket booth stood crooked, its glass shattered. Tomás stopped at a chained gate. A sign above it read CAMDEN TOWN in faded paint. Beyond the gate, a platform lay empty except for a few figures in dark coats. One of them held a small bone token in his palm. He pressed it to a slot in the gate. It clicked. The gate rolled up with a shriek. The market opened onto a cavern that had once been a Tube station. Stalls lined the platform edge under strings of warm light. Enchanted goods sat in glass cases. Alchemical vials caught the light. The air thrummed with voices speaking in low tones. A man bartered for a jar of what looked like teeth. A woman examined a ring that pulsed faintly. Tomás turned to Quinn at the top of the stairs. “You can’t come in here.” She stood in the mouth of the stairwell with rain still dripping from her coat. Her watch read 02:14. The gate was open. The bone token requirement hung in the air between them. She had no token. She had a badge, a gun, eighteen years of service, and a partner who died three years ago under circumstances she still could not name. He held his bag tighter. “If you go down, you can’t come back the same.” She looked at the market. She looked at the figures watching from the stalls. She looked at the gate that would close again when the moon moved. “Who ordered the hit on Morris?” He said nothing. She stepped forward, one boot on the first concrete step.
created_at"2026-09-03T14:00:07.894Z" 9/3/2026, 2:00:07 PM
model"meta/muse-glimmer-30b-20260810"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency144
moderation_latency(null)
generation_time22873
tokens_prompt1104
tokens_completion2781
native_tokens_prompt1005
native_tokens_completion2379
native_tokens_completion_images(null)
native_tokens_reasoning1533
native_tokens_cached992
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"
service_tier(null)
usage0.00361273
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.19.0; linux; x64))"
http_referer(null)
request_id"req-1788444007-mzwl1NNtvZQLz4u3OVBO"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1788444007-IdMVxJPrwhstvW4BlZgi"
upstream_id"oyog27r-7ArivV-a35547698f3c0c97"
provider_responses
0
endpoint_id"e6c588fa-aef0-4e1b-bb7f-78185dd5b6da"
id"oyog27r-7ArivV-a35547698f3c0c97"
is_byokfalse
latency120
model_permaslug"meta/muse-glimmer-30b-20260810"
provider_name"Together"
status200
total_cost0.00361273
cache_discount0.00030752
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
totalTags0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount852
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)
70.66% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount852
totalAiIsms5
found
0
word"weight"
count1
1
word"warmth"
count1
2
word"flickered"
count1
3
word"shattered"
count1
4
word"pulsed"
count1
highlights
0"weight"
1"warmth"
2"flickered"
3"shattered"
4"pulsed"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches1
maxInWindow1
found
0
label"hung in the air"
count1
highlights
0"hung in the air"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences93
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount0
narrationSentences93
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences115
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
totalWords852
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions1
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions22
wordCount726
uniqueNames12
maxNameDensity0.96
worstName"Quinn"
maxWindowNameDensity2
worstWindowName"Quinn"
discoveredNames
Soho1
Raven1
Nest2
Quinn7
Herrera1
Saint1
Christopher1
Great1
Portland1
Street1
Tube1
Tomás4
persons
0"Raven"
1"Nest"
2"Quinn"
3"Herrera"
4"Saint"
5"Christopher"
6"Tomás"
places
0"Soho"
1"Portland"
2"Street"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences54
glossingSentenceCount1
matches
0"looked like teeth"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount852
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences115
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs51
mean16.71
std18.54
cv1.11
sampleLengths
054
150
22
31
421
51
622
717
86
913
107
118
125
1324
144
153
1615
177
182
196
206
2118
221
2327
2448
2553
2611
2726
284
297
308
315
324
334
346
3531
363
371
384
3957
4060
4162
4210
435
4462
455
4610
4727
486
493
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences93
matches(empty)
98.22% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount2
totalVerbs131
matches
0"was not trying"
1"was leading"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences115
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount728
adjectiveStacks0
stackExamples(empty)
adverbCount14
adverbRatio0.019230769230769232
lyAdverbCount3
lyAdverbRatio0.004120879120879121
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences115
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences115
mean7.41
std4.48
cv0.604
sampleLengths
011
119
220
34
43
57
613
715
812
92
101
114
125
136
146
151
169
1710
183
1917
206
2113
224
233
248
255
2610
275
289
294
303
314
328
333
347
352
366
376
385
3913
401
414
423
435
4411
454
4610
475
4814
4912
36.09% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats16
diversityRatio0.25217391304347825
totalSentences115
uniqueOpeners29
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences89
matches(empty)
ratio0
26.74% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount43
totalSentences89
matches
0"She watched the door."
1"He wore a dark jacket,"
2"He turned his head."
3"He did not run at"
4"He lifted his hands, palms"
5"She closed the distance between"
6"Her jaw set."
7"He shifted his weight."
8"He smiled without warmth."
9"He took a step back"
10"He exhaled through his nose."
11"He looked past her shoulder"
12"She grabbed his sleeve."
13"He jerked free."
14"He broke toward the left,"
15"He moved fast for a"
16"He cut left, then right,"
17"They hit the main drag"
18"He crossed at the light,"
19"She ran the line, boots"
ratio0.483
4.94% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount81
totalSentences89
matches
0"The green neon of the"
1"Harlow Quinn stood under the"
2"She watched the door."
3"A bell jangled."
4"Tomás Herrera stepped out into"
5"He wore a dark jacket,"
6"The Saint Christopher medallion at"
7"The scar on his left"
8"He turned his head."
9"He did not run at"
10"He lifted his hands, palms"
11"She closed the distance between"
12"The rain slicked her cropped"
13"Her jaw set."
14"He shifted his weight."
15"The medallion swung."
16"The bell above the Nest"
17"Tomás glanced toward the street."
18"A black cab rolled past,"
19"He smiled without warmth."
ratio0.91
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences89
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences31
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
84.5549%