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Run: 2026-11-11-01M753ANGMFPJ92Q2FF0HSZ4G4
Run Details
Model:
Muse Ember (Reasoning, Max)
Test:
Dialogue tags
Scenario:
Write 200 words with 10% dialogue
Duration:
6.1s
Cost:
$0.001666
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 10% 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 crouched behind a hedge wearing a clown wig while Bill balanced a ladder against the fence, both pretending they belonged at a birthday party for a seven year old expecting a pony. Their master plan depended on juice boxes, party hats, and shameless confidence. Molly said, "We sneak in through the bounce house." Bill replied, "That is genius disguised as a felony." Molly whispered, "Grab balloons for camouflage." Bill added, "And cake." Inside, children screamed with joy while a magician attempted card tricks and failed spectacularly with every single card. A mountain of presents sat beside the cake, wrapped in glittering paper that reflected the disco ball above. Molly rehearsed her distraction involving a runaway ferret, although they owned no ferret and had never met one. Bill planned to swap the birthday child's whistle for a kazoo, hoping nobody would notice until the getaway. They argued briefly about whether stealing presents counted as villainy or merely aggressive party crashing with excellent timing. Finally they agreed that cake first, presents second, and running third would guarantee either success or a memorable sugar rush. Then Molly checked her watch, nodded and prepared to ruin the party with theatrical professionalism and snacks.
created_at
"2026-11-11T10:44:15.688Z"
11/11/2026, 10:44:15 AM
model
"meta/muse-ember-20261109"
app_id
182717
external_user
(null)
streamed
true
cancelled
false
latency
2837
moderation_latency
(null)
generation_time
5842
tokens_prompt
75
tokens_completion
411
native_tokens_prompt
69
native_tokens_completion
3318
native_tokens_completion_images
0
native_tokens_reasoning
2985
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
0.0016659
router
(null)
user_agent
"langchainjs-openai/1.0.0 ((node/v24.22.2; linux; x64))"
http_referer
(null)
request_id
"req-1794393855-fZnu8z5fdNdlyeQ77Oa2"
session_id
(null)
preset_id
(null)
api_type
"completions"
id
"gen-1794393855-ys8KtJd8IrIiuRc51mSL"
upstream_id
"resp_6af44701b947e6eef89744a2"
provider_responses
0
endpoint_id
"ea8469c2-ddf8-4985-8cf2-f5dc7e63e14c"
id
"resp_6af44701b947e6eef89744a2"
is_byok
false
latency
2508
model_permaslug
"meta/muse-ember-20261109"
provider_name
"Meta"
status
200
total_cost
0.0016659
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: 10.00%, Deviation: 0.00%
neededClean
false
wordsTotal
200
wordsDialogue
20
100.0000%