Run: 2026-04-25-01KQ2EZ733V36F3GP25H1BGQQ0

Run Details

Duration:
6m 15s
Cost:
$0.015144
Provider:
DeepSeek

Evaluation summary

Score Evaluator
Details Link
0.00% Matches word count (details)
99.94% Dialogue to Total Word Ratio (details)
49.9698% Overall

Model input

User/Human
You are a author of comedic heist books. Please write a 500 word scene that has 30% 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 spread the blueprints across the sticky diner table, beside a syrup-smeared placemat decorated with smiling pancakes. The blueprints were actually a hand-drawn map of a suburban backyard, complete with a bouncy castle labeled “Fort Knox” and a gift table marked “LOOT.” Bill squinted at the cartoon unicorns and the crayon arrows indicating wind direction. A half-eaten stack of pancakes sat between them, growing cold. Bill tapped a glitter-pen marking. “You want us to dress as clowns.” Molly beamed, producing two rainbow wigs from a duffel bag that smelled faintly of old balloons. “Not just clowns, Bill. Sparkles and Giggles, from the Joyful Jingles Agency. I’ve already booked us.” Bill picked up a wig with two fingers, as if it might trigger an allergy. The synthetic fibers crackled with static. He remembered a childhood incident involving a unicycle and a llama, and shuddered. “The shoes are a dealbreaker.” Molly ignored him. She unfolded a secondary diagram illustrating the trajectory of glitter-filled hamsters. The hamsters were drawn with tiny capes. She had spent six weeks training them to scatter on command, using a Pavlovian method involving carrot sticks and a kazoo. “Diversion. You’ll do a magic trick where I vanish into the gift pile.” Bill stared at the drawing of a stick figure disappearing behind a three-tiered cake. The cake had googly eyes. He wondered if any actual children’s parties had this level of planning. “I can’t even find the hope in a Kinder Egg.” Molly slid a pair of floppy shoes across the table. They were size fourteen, and one squeaked involuntarily. “You’ll just honk and wave a wand. The hamsters handle the chaos.” Bill raised an eyebrow. “Where do you get glitter-covered hamsters on short notice?” “A man called Nibbles. He owes me a favor.” Bill sighed, fogging the sugar pourer. He thought about the moral implications, but the thought was interrupted by a waitress refilling their coffee. The waitress had a name tag that read “Nibbles,” which felt like an omen. Bill leaned in. “What’s the getaway?” “Ice-cream truck. Plays the Baby Shark remix. No pursuit.” “And the moral quandary of stealing from tiny humans?” “We’re not stealing joy, just relocating it. We’ll mail each child a handcrafted apology note. On glitter paper.” Bill nodded slowly, a man accepting a ridiculous fate. “Fine. But if I get a honk-related injury, I’m billing you.” Outside the diner window, a pigeon fought a discarded balloon animal. The scene felt like a metaphor. Bill imagined the presents: LEGO sets, dolls that peed, video games. He could already hear the sirens, or maybe just the endless loop of Baby Shark. He took a deep breath and grabbed the wig.
created_at"2026-04-25T13:58:50.728Z" 4/25/2026, 1:58:50 PM
model"deepseek/deepseek-v4-pro-20260423"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency881
moderation_latency(null)
generation_time374642
tokens_prompt77
tokens_completion15815
native_tokens_prompt67
native_tokens_completion17373
native_tokens_completion_images(null)
native_tokens_reasoning16737
native_tokens_cached0
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"
usage0.015143655
router(null)
provider_responses
0
endpoint_id"d084170b-dd99-4e53-9ae1-b63881f3cfbf"
id"7dba962e-a7fe-4c59-8c7f-4baf2e55db57"
is_byokfalse
latency289
model_permaslug"deepseek/deepseek-v4-pro-20260423"
provider_name"DeepSeek"
status200
user_agent"langchainjs-openai/1.0.0 ((node/v24.14.1; linux; x64))"
http_referer(null)
request_id"req-1777125530-NKCxxQr7ZSWbKVIH96Uq"
session_id(null)
api_type"completions"
id"gen-1777125530-dQDaG67yakXTaxjNHjvb"
upstream_id"7dba962e-a7fe-4c59-8c7f-4baf2e55db57"
total_cost0.015143655
cache_discount(null)
upstream_inference_cost0
provider_name"DeepSeek"
response_cache_source_id(null)

Evaluation details

Result Evaluator Details Meta Data
0.00% Matches word count n/a
neededCleanfalse
words447
99.94% Dialogue to Total Word Ratio
Ratio: 30.11%, Deviation: 0.11%
neededCleanfalse
wordsTotal455
wordsDialogue137
49.9698%