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Run: 2026-11-11-01M7545WHZMZF7SVWQT2Q7PVZM
Run Details
Model:
Muse Ember (Reasoning, Max)
Test:
Dialogue tags
Scenario:
Write 200 words with 90% dialogue
Duration:
5.9s
Cost:
$0.001898
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 spread blueprints across the kitchen table as Bill sharpened a candy cane. "Okay, Bill, the plan is simple: we enter the birthday party, distract the clown, and hoover up every present like very polite vacuum cleaners." "But the party is for kids. What if they ask us to play pin the tail on the donkey?" "Then we play. We win the prize, because losing to a child looks suspicious, and suspicious people get no cake." "What about the bouncy castle guard? He has a whistle, a clipboard, and the unshakeable confidence of a man named Chad." "We bribe Chad with juice boxes. Every adult folds instantly when offered apple juice and a paper umbrella." "And the parents? They will definitely notice two adults absconding with a mountain of wrapped bicycles and shouting, mine!" "That is why we wear disguises. I bought us matching tiger costumes, which are both adorable and legally distracting." Bill adjusted his tiny tiger hat proudly. "Tigers cannot open doors. Their paws are terrible. We tested this morning, and now the doorknob is glittery." "Excellent. Glitter means we were never here. Now, Bill, hand me the trolley, and try not to cry again." "Too late, Molly!"
created_at
"2026-11-11T10:59:07.467Z"
11/11/2026, 10:59:07 AM
model
"meta/muse-ember-20261109"
app_id
182717
external_user
(null)
streamed
true
cancelled
false
latency
1475
moderation_latency
(null)
generation_time
5827
tokens_prompt
75
tokens_completion
310
native_tokens_prompt
69
native_tokens_completion
3782
native_tokens_completion_images
0
native_tokens_reasoning
3492
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
0.0018979
router
(null)
user_agent
"langchainjs-openai/1.0.0 ((node/v24.22.2; linux; x64))"
http_referer
(null)
request_id
"req-1794394747-gQHPMNOgYS8VC3JkLfA7"
session_id
(null)
preset_id
(null)
api_type
"completions"
id
"gen-1794394747-YzlO5ny4poE0cUkf432F"
upstream_id
"resp_6af44a7caee41965555b43bc"
provider_responses
0
endpoint_id
"ea8469c2-ddf8-4985-8cf2-f5dc7e63e14c"
id
"resp_6af44a7caee41965555b43bc"
is_byok
false
latency
722
model_permaslug
"meta/muse-ember-20261109"
provider_name
"Meta"
status
200
total_cost
0.0018979
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%