Run: 2026-02-19-01KHTXVYA0FQ72WC8FS9DKE5Y2

Run Details

Duration:
1m 48s
Cost:
$0.001320
Provider:
SambaNova

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 the slick pavement, turning the neon lights of Soho into bleeding watercolors. Detective Harlow Quinn’s lungs burned with, a figure darted around a corner, a dark shape made ghostly by the downpour and the steam rising from the grates. . Eighteen years on the force had taught her how a chase felt in her bones—the tightening step into that shadowed warehouse doorway. A feeling of the ground tilting, of the rules she a side alley choked with overflowing bins. Quinn, flickering security light. But the runner was gone. “Damn it,” she hissed, scanning the grimy walls darkness, the air carrying a faint, coppery tang that made the fine hairs on her arms stand up. wasn’t on any city blueprint she’d ever into a blind spot. Morris’s face flashed in her memory ground. She unclipped her radio from her belt. Static crackled in response, a hollow. The signal was dead. It was her choice. Go back, file a report, lose him. Or go down. Quinn descended. The metal steps groaned under her weight. The murmur grew into a cacophony—haggling, laughter, the clink of glass and metal. The air grew thick, smelling of damp stone, exotic spices, and. She emerged into a vast, cavernous space. An abandoned Tube station, its old tiled walls stained with damp and covered in intricate glyphs that shimmered in the lowused platform, lit by flickering lanterns and glowing crystals suspect was already thirty yards ahead, slipping through the crowd with practiced ease. The crowd itself was a shock. A woman with iridescent scales tracing her cheekbones examined a vial of swirling liquid. A tall figure shrouded in shadows seemed to have too many joints in its arms. A vendor with eyes like molten gold gestured to a cage containing something that looked like a winged lizard made of smoke. Quinn’s training screamed at her to observe, to assess threats, to identify exits. There were none she could see. The way she’d come was the only visible entrance. Her sharp jaw was set, her body thrumming with a tension she hadn’t felt since her first tour overseas. This was a different kind of warzone. She moved forward, keeping her head down a lie. She lost sight of him near a stall selling what looked like miniature, mummified hands. Pushing past a h curtained-off alcove near the end of the platform. She closed Saint Christopher medallion gleamed at his throat. He didn’t look like security. He looked worried. “Metropolitan Police,” Quinn said, her voice low, a command. “Release my arm.” He didn’t let go. His eyes darted past her, toward the alcove, then back to her face. His Spanish accent was subtle but distinct. “This is not a place for your jurisdiction.” “I’m pursuing a suspect. That gives me jurisdiction anywhere.” “That might be true up there,” he said, his gaze intense, sincere hurt. Permanently. The man you are following… he is not worth it. Turn around. Cl“My name is Tomás.” He finally released her arm, but his posture remained between her and the alcove. A former paramedic’s bearing, she noted automatically. Used to standing between people and danger. “And I am not obstructing. I am providing medical advice. This past him. The curtain to the alcove was still. Her suspect was in there, maybe listening. This man, Tomás, knew it. He was protecting. “What’s in the alcove, Tomás?” “Trouble you do not need.” “My partner thought that once. He decided to walk away from a lead crossed his face. “I am sorry. Truly. But following this man will not bring your partner back. It will only mean I have another patient I cannot save. Or a body I cannot explain. Walk away, Detective.” The curtain twitched. A sliver of darkness showed from within the the unknown. She could assert the authority of her badge she could listen to the fear in this medic’s eyes, a fear that felt less like a threat and more like a warning. She could feel the weight of hundreds of unseen eyes upon her, the unnatural stillness that had fallen over the nearest stalls. The market was watching. Waiting to see what the surface-worlder would do. Quinn took a half-step back, her own training warring with a new, primal instinct. The instinct that had saved her life in back alleys and dark houses. The instinct that told her when a door was too dangerous to open. She met Tomás’s gaze and gave a single, sharp nod.
created_at"2026-02-19T12:26:47.493Z" 2/19/2026, 12:26:47 PM
model"deepseek/deepseek-chat-v3.1"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency1220
moderation_latency(null)
generation_time96491
tokens_prompt1157
tokens_completion1146
native_tokens_prompt1003
native_tokens_completion1559
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"
usage0.0013197
router(null)
provider_responses
0
endpoint_id"5d5c9c1d-af0d-4e4a-8aee-c5cd2b3883f3"
is_byokfalse
latency11157
model_permaslug"deepseek/deepseek-chat-v3.1"
provider_name"SiliconFlow"
status(null)
1
endpoint_id"1dda4bb9-c626-4816-bb54-99665323fef0"
id"1e8f6be0-9810-445c-9183-eb5893485c65"
is_byokfalse
latency1161
model_permaslug"deepseek/deepseek-chat-v3.1"
provider_name"SambaNova"
status200
api_type"completions"
id"gen-1771504007-PKTNkOuWlcGGyd6SQRGT"
upstream_id"1e8f6be0-9810-445c-9183-eb5893485c65"
total_cost0.0013197
cache_discount(null)
upstream_inference_cost0
provider_name"SambaNova"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags3
adverbTagCount0
adverbTags(empty)
dialogueSentences12
tagDensity0.25
leniency0.5
rawRatio0
effectiveRatio0
93.32% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount749
totalAiIsmAdverbs1
found
0
adverb"truly"
count1
highlights
0"truly"
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)
33.24% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount749
totalAiIsms10
found
0
word"scanning"
count1
1
word"weight"
count2
2
word"cacophony"
count1
3
word"intricate"
count1
4
word"shimmered"
count1
5
word"practiced ease"
count1
6
word"tracing"
count1
7
word"tension"
count1
8
word"could feel"
count1
highlights
0"scanning"
1"weight"
2"cacophony"
3"intricate"
4"shimmered"
5"practiced ease"
6"tracing"
7"tension"
8"could feel"
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
emotionTells1
narrationSentences59
matches
0"looked worried"
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount1
narrationSentences59
filterMatches(empty)
hedgeMatches
0"seemed to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences67
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen42
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords745
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions7
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions14
wordCount615
uniqueNames9
maxNameDensity0.98
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Quinn"
discoveredNames
Soho1
Harlow1
Quinn6
Tube1
Saint1
Christopher1
Spanish1
Permanently1
Tomás1
persons
0"Harlow"
1"Quinn"
2"Saint"
3"Christopher"
4"Tomás"
places
0"Soho"
globalScore1
windowScore1
33.72% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences43
glossingSentenceCount2
matches
0"looked like a winged lizard made of smoke"
1"looked like miniature, mummified hands"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount745
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences67
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs22
mean33.86
std28.01
cv0.827
sampleLengths
041
149
226
318
431
52
631
7106
854
99
1027
1117
1212
1332
149
1593
165
175
1850
1978
2040
2110
99.32% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences59
matches
0"was gone"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs124
matches
0"was watching"
57.57% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount2
semicolonCount0
flaggedSentences2
totalSentences67
ratio0.03
matches
0"Eighteen years on the force had taught her how a chase felt in her bones—the tightening step into that shadowed warehouse doorway."
1"The murmur grew into a cacophony—haggling, laughter, the clink of glass and metal."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount531
adjectiveStacks0
stackExamples(empty)
adverbCount14
adverbRatio0.026365348399246705
lyAdverbCount5
lyAdverbRatio0.009416195856873822
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences67
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences67
mean11.1
std8.48
cv0.764
sampleLengths
014
127
222
317
44
55
626
711
87
97
106
114
124
137
143
152
167
1713
1811
197
2042
216
2214
2315
2422
2513
266
279
2819
297
309
3115
3212
339
345
353
369
373
384
3913
407
418
429
4313
441
455
465
472
4818
497
73.63% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats9
diversityRatio0.5223880597014925
totalSentences67
uniqueOpeners35
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences56
matches(empty)
ratio0
84.29% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount19
totalSentences56
matches
0"she hissed, scanning the grimy"
1"She unclipped her radio from"
2"It was her choice."
3"She emerged into a vast,"
4"Her sharp jaw was set,"
5"She moved forward, keeping her"
6"She lost sight of him"
7"She closed Saint Christopher medallion"
8"He didn’t look like security."
9"He looked worried."
10"He didn’t let go."
11"His eyes darted past her,"
12"His Spanish accent was subtle"
13"he said, his gaze intense,"
14"he is not worth it."
15"He decided to walk away"
16"She could assert the authority"
17"She could feel the weight"
18"She met Tomás’s gaze and"
ratio0.339
49.29% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount46
totalSentences56
matches
0"Detective Harlow Quinn’s lungs burned"
1"A feeling of the ground"
2"Quinn, flickering security light."
3"she hissed, scanning the grimy"
4"Morris’s face flashed in her"
5"She unclipped her radio from"
6"Static crackled in response, a"
7"The signal was dead."
8"It was her choice."
9"The metal steps groaned under"
10"The murmur grew into a"
11"The air grew thick, smelling"
12"She emerged into a vast,"
13"An abandoned Tube station, its"
14"The crowd itself was a"
15"A woman with iridescent scales"
16"A tall figure shrouded in"
17"A vendor with eyes like"
18"Quinn’s training screamed at her"
19"The way she’d come was"
ratio0.821
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences56
matches(empty)
ratio0
53.57% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences24
technicalSentenceCount3
matches
0"An abandoned Tube station, its old tiled walls stained with damp and covered in intricate glyphs that shimmered in the lowused platform, lit by flickering lante…"
1"She could feel the weight of hundreds of unseen eyes upon her, the unnatural stillness that had fallen over the nearest stalls."
2"The instinct that had saved her life in back alleys and dark houses."
0.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags3
uselessAdditionCount2
matches
0"Quinn said, her voice low, a command"
1"he said, his gaze intense, sincere hurt"
66.67% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags3
fancyCount1
fancyTags
0"she hissed (hiss)"
dialogueSentences12
tagDensity0.25
leniency0.5
rawRatio0.333
effectiveRatio0.167
78.1539%