Run: 2026-11-11-01M753RMS9VYH6JSGF6A33WDN7

Run Details

Duration:
13.7s
Cost:
$0.004023
Provider:
Meta

Evaluation summary

Score Evaluator
Details Link
100.00% Matches word count (details)
99.56% Dialogue to Total Word Ratio (details)
99.7799% Overall

Model input

User/Human
You are a author of comedic heist books. Please write a 500 word scene that has 70% 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 peered through the binoculars at the brightly decorated garden, where balloons bobbed above a table overflowing with wrapped presents. Bill crouched beside her behind a hedge, wearing a fake mustache and holding a net normally used for butterflies. "Thirty-seven presents," Molly whispered. "That is our retirement fund." "We will need a bigger bag," Bill said. "And maybe a distraction." "I have a plan," she said. "We wait until the clown finishes his balloon animals." "That clown once turned my cousin into a poodle," Bill said. "It was not pleasant." "Focus, Bill," Molly said. "We go in wearing party hats, carrying a cake, and looking generally harmless." "I cannot look harmless," Bill said. "I have a criminal face. The mustache makes it worse." A child screamed happily next door, and both thieves froze as if a museum guard had spotted them. "See?" she hissed. "They are too busy with cake and games. Ninjas wish they had our cover." "What about the bouncy castle?" he asked. "It is guarded by a seven-year-old named Chad." "I will bribe Chad with the secret decoder ring from my cereal box," Molly replied. "It works every time." "Why do you still have that?" he asked. "Sentimental value," she said. "Also, it decodes nothing, which makes it perfect for bluffing." Bill nodded solemnly, as though they had discussed actual spy equipment, and adjusted his ridiculous mustache with grave concentration. "And the piƱata?" he whispered. "If that breaks, we will have a stampede of sugar-crazed toddlers." "Then we become the stampede," she said. "We grab presents while screaming about candy. No one suspects thieves." "We need a getaway vehicle," Bill said. "That tricycle by the gate looks fast." "It has a bell and a basket," Molly said. "We can put at least nine presents in the basket and pedal away majestically." "Nine is not all of them," he pointed out. "We make multiple trips," she said. "Slowly. While waving. Like cheerful, slightly sweaty delivery people." "What if they sing Happy Birthday?" he asked. "I always cry during that song." "Then we cry too," Molly said. "Tears are excellent camouflage. No one questions a sobbing getaway driver." Somewhere inside the house, a mother began tuning a ukulele, and Bill paled beneath his mustache. "Not the ukulele," he groaned. "That means singing is imminent. We must move now." "On three," Molly said. "One, two, three, charge!" "Wait!" he shouted. "I forgot my party hat!" "Bill, it is on your head," she said, sighing loudly. "Oh good," he said. "Then let us go. For the presents. For glory. For cake later." "For cake later," she agreed. "And maybe a tricycle with a very loud bell." "Then we will ring the bell all the way home, wear our party hats proudly, and pretend Chad's decoder ring is actual government spy equipment every single time," he replied cheerfully. "And I will carry the cake while you pedal, waving cheerfully at thirty-seven confused parents who definitely think we are professionals now," Molly said.
created_at"2026-11-11T10:51:53.518Z" 11/11/2026, 10:51:53 AM
model"meta/muse-ember-20261109"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency2017
moderation_latency(null)
generation_time13677
tokens_prompt75
tokens_completion946
native_tokens_prompt69
native_tokens_completion8033
native_tokens_completion_images0
native_tokens_reasoning7158
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"completed"
service_tier"default"
usage0.0040234
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.22.2; linux; x64))"
http_referer(null)
request_id"req-1794394313-TF5ugwtPoPMnWSEPMKw7"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1794394313-1k4uXisnLu7LcLVipwhb"
upstream_id"resp_6af448ca7033afebf80445ae"
provider_responses
0
endpoint_id"ea8469c2-ddf8-4985-8cf2-f5dc7e63e14c"
id"resp_6af448ca7033afebf80445ae"
is_byokfalse
latency822
model_permaslug"meta/muse-ember-20261109"
provider_name"Meta"
status200
total_cost0.0040234
cache_discount(null)
upstream_inference_cost0
provider_name"Meta"
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
99.56% Dialogue to Total Word Ratio
Ratio: 70.30%, Deviation: 0.30%
neededCleanfalse
wordsTotal505
wordsDialogue355
99.7799%