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Run: 2026-11-11-01M753RMS9VYH6JSGF6A33WDN7
Run Details
Model:
Muse Ember (Reasoning, Max)
Test:
Dialogue tags
Scenario:
Write 500 words with 70% dialogue
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
Messages
Meta
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_id
182717
external_user
(null)
streamed
true
cancelled
false
latency
2017
moderation_latency
(null)
generation_time
13677
tokens_prompt
75
tokens_completion
946
native_tokens_prompt
69
native_tokens_completion
8033
native_tokens_completion_images
0
native_tokens_reasoning
7158
native_tokens_cached
0
num_media_prompt
(null)
num_input_audio_prompt
(null)
num_media_completion
0
num_search_results
(null)
num_fetches
(null)
web_search_engine
(null)
origin
"https://ncbench.com/"
is_byok
false
finish_reason
"stop"
native_finish_reason
"completed"
service_tier
"default"
usage
0.0040234
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_byok
false
latency
822
model_permaslug
"meta/muse-ember-20261109"
provider_name
"Meta"
status
200
total_cost
0.0040234
cache_discount
(null)
upstream_inference_cost
0
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
neededClean
false
words
500
99.56%
Dialogue to Total Word Ratio
Ratio: 70.30%, Deviation: 0.30%
neededClean
false
wordsTotal
505
wordsDialogue
355
99.7799%