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Run: 2026-11-11-01M7543TNE6QY641BXJX9DC8S7
Run Details
Model:
Muse Ember (Reasoning, Max)
Test:
Dialogue tags
Scenario:
Write 200 words with 90% dialogue
Duration:
10.2s
Cost:
$0.003203
Provider:
Meta
Evaluation summary
Score
Evaluator
Details Link
100.00%
Matches word count
(details)
100.00%
Dialogue to Total Word Ratio
(details)
100.0000%
Overall
Model input
User/Human
You are a author of comedic heist books. Please write a 200 word scene that has 90% 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 whispered, pointing at the bouncy castle. "Okay Bill, plan is simple: wait for the clown to distract everyone with balloon animals. Then we sneak past the juice boxes and grab every present under that enormous pink table. I will wear the hat, you carry the sack, and we will not giggle." Bill nodded, clutching an empty toy sack. "Molly, what about the parents? They have eyes like hungry party mascots watching us constantly. Also, that little guard is eating cake and staring directly at our suspicious matching raincoats. And the pinata is filled with candy, which might cause total chaos during our escape." "Relax, Bill, I have disguises: we pretend we are triplets hired to entertain the birthday twins. You do the funny walk, I play kazoo, and everyone will overlook the sack completely. Then we load the stroller, roll calmly out, and sell everything online by midnight." "Genius, Molly! But what if the birthday girl demands a magic trick and discovers our sack? We could saw invisible presents in half, then claim we only steal the dramatic halves. Honestly, I will risk it for one extremely shiny, plastic dinosaur treasure today, please." They celebrated, already planning the escape.
created_at
"2026-11-11T10:58:00.132Z"
11/11/2026, 10:58:00 AM
model
"meta/muse-ember-20261109"
app_id
182717
external_user
(null)
streamed
true
cancelled
false
latency
1832
moderation_latency
(null)
generation_time
9926
tokens_prompt
75
tokens_completion
455
native_tokens_prompt
69
native_tokens_completion
6392
native_tokens_completion_images
0
native_tokens_reasoning
6005
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
0.0032029
router
(null)
user_agent
"langchainjs-openai/1.0.0 ((node/v24.22.2; linux; x64))"
http_referer
(null)
request_id
"req-1794394680-Lg5TfubKoJ4pxkIUn496"
session_id
(null)
preset_id
(null)
api_type
"completions"
id
"gen-1794394680-7ZfMCq3h2StqHwxPZ22K"
upstream_id
"resp_6af44a39be92152812da495f"
provider_responses
0
endpoint_id
"ea8469c2-ddf8-4985-8cf2-f5dc7e63e14c"
id
"resp_6af44a39be92152812da495f"
is_byok
false
latency
773
model_permaslug
"meta/muse-ember-20261109"
provider_name
"Meta"
status
200
total_cost
0.0032029
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
200
100.00%
Dialogue to Total Word Ratio
Ratio: 90.00%, Deviation: 0.00%
neededClean
false
wordsTotal
200
wordsDialogue
180
100.0000%