Run: 2026-10-20-01M5C6D6FX1PHW0EGEBTE8JZDT

Run Details

Duration:
1m 16s
Cost:
$0.006641
Provider:
InferenceNet

Evaluation summary

Score Evaluator
Details Link
100.00% Matches word count (details)
65.78% Dialogue to Total Word Ratio (details)
82.8910% Overall

Model input

User/Human
You are a author of comedic heist books. Please write a 500 word scene that has 50% words of dialogue between two thieves named Molly and Bill who are planning to break into a children's party to steal all the presents. Only include the prose of the scene without any markdown or commentary.

Model response

AI
Molly crouched behind the van's steering wheel, peering through a crack in the windshield at the bouncing castle. "The clown's our ticket in. He's got a whistle and zero peripheral vision." Bill adjusted his fake mustache, which had slipped sideways during the drive, and checked the duffel bag of decoy bricks. "Those bricks better look like Lego sets. Last time you wrapped them in newspaper." Molly rolled her eyes and pointed at a cluster of parents hovering near the cake table like secret service agents. "That aunt's a drill sergeant. She pats down every gift bag for contraband juice boxes." Bill groaned and pulled a dog toy from his pocket, the squeaker already loose and rattling. "Swap the whistle with this. The clown will honk, not tweet. Parents will think it's comedy." Molly nodded and sketched a rough timeline on the dashboard with a grease pencil. "Two-fifteen, cake cutting. All eyes on frosting. We slide under the bounce house and puncture it slow." Bill stared at a toddler waddling past with a juice box and a suspiciously red stain on his shirt. "That kid's a biter. I still have scars from the last party. We need backup." Molly tossed him a juice box from the glove compartment, its straw still wrapped in plastic. "Backup is juice boxes. Toddler currency. Now focus on the gift table." Bill squinted through binoculars at a stack of presents wobbling beside the bouncy castle's inflation fan. "Lego sets, scooters, that fancy doll with the plastic crown. We swap them for bricks." Molly checked her watch, a cheap digital thing that beeped every hour like a tiny alarm. "Three minutes. Trip the clown after he drops a club. I'll grab the presents and run." Bill adjusted his magician's cape and practiced pulling a rabbit from a hat, but the rabbit was a stuffed sock. "If this goes wrong, I'm blaming the clown. He's got a suspicious nose for rubber chickens." Molly started the engine, which coughed twice before settling into a nervous idle. "Put on your mask. We're entertainers. Smile like you mean it. No biting." Bill pulled the ski mask over his face, leaving only his eyes and a twitching mustache visible. "What about the party bags? Every kid gets one. We can't take those." Molly grinned and revved the engine, scattering a flock of pigeons from the curb. "We don't. We take the big presents. Juice boxes for the crying kids. Done." Molly slowly pulled the old van into a side alley and killed the lights, leaving the tiny party glowing in the rearview mirror. "Just remember, no running. Little kids are short but surprisingly fast. And they bite hard. Always aim for their ankles first, okay please." Bill cracked nervously the van door and slipped onto the old pavement, then his magician's cape snagging on a loose rusty bolt again. "If anyone asks, we're the official entertainment. You're the magician. I'm the assistant. And the squeaky dog toy is very much a prop."
created_at"2026-10-20T08:21:21.542Z" 10/20/2026, 8:21:21 AM
model"deepseek/deepseek-v4.1-flash-20261017"
app_id182717
external_user(null)
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cancelledfalse
latency164
moderation_latency(null)
generation_time76374
tokens_prompt75
tokens_completion11874
native_tokens_prompt67
native_tokens_completion13272
native_tokens_completion_images(null)
native_tokens_reasoning12606
native_tokens_cached0
num_media_prompt(null)
num_input_audio_prompt(null)
num_media_completion0
num_search_results(null)
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origin"https://ncbench.com/"
is_byokfalse
finish_reason"stop"
native_finish_reason"stop"
service_tier(null)
usage0.00664136
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1792484481-sFGXbtvn2Ek3D7U5UOdB"
session_id(null)
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api_type"completions"
id"gen-1792484481-sbcgcBOdPYI8MSpbAVd4"
upstream_id"gv2_prDC84PTrvwmodgJGIXHV"
provider_responses
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endpoint_id"4231aea9-73af-4a2f-bd14-660b1d8b13b2"
id"gv2_prDC84PTrvwmodgJGIXHV"
is_byokfalse
latency164
model_permaslug"deepseek/deepseek-v4.1-flash-20261017"
provider_name"InferenceNet"
status200
total_cost0.00664136
cache_discount(null)
upstream_inference_cost0
provider_name"InferenceNet"
response_cache_source_id(null)
data_region"global"
workspace_id"97e315e5-d303-487d-83c1-83180e8a13d4"

Evaluation details

Result Evaluator Details Meta Data
100.00% Matches word count n/a
neededCleanfalse
words500
65.78% Dialogue to Total Word Ratio
Ratio: 47.11%, Deviation: 2.89%
neededCleanfalse
wordsTotal501
wordsDialogue236
82.8910%