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Run: 2026-03-18-01KM0AQ51CVMD14K2HEFHAGZPH
Run Details
Model:
GPT-5.4 Nano (Reasoning, Low)
Test:
Dialogue tags
Scenario:
Write 500 words with 50% dialogue
Duration:
6.5s
Cost:
$0.001597
Provider:
OpenAI
Evaluation summary
Score
Evaluator
Details Link
0.00%
Matches word count
(details)
0.00%
Dialogue to Total Word Ratio
(details)
0.0000%
Overall
Model input
User/Human
You are a author of comedic heist books. Please write a 500 word scene that has 50% 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 the hedge like it owed her money. “Okay, Bill, eyes up. Tell me you parked the getaway car somewhere heroic.” Bill whispered, “I parked it heroically. In the driveway. Next to a minivan with stickers that say ‘TRAIN YOUR DRAGON’ and I’m pretty sure that’s a threat.” Molly leaned closer. “Great. That means we’re dealing with a family that owns dragons. Or at least emotionally. How’s the grappling line?” Bill held up a coil of rope. “It’s… optimistic.” Molly squinted. “Optimistic doesn’t bind doors, it binds hope. Do you have the suction cups?” Bill patted his bag. “Yes, and also candy. Don’t ask.” Molly hissed, “We’re stealing presents, not starting a sugar-based friendship. We need to move like ghosts.” Bill nodded. “Ghosts with snacks.” Molly pointed toward the house. “Listen. I can hear the music. That’s a playlist. That means guardians. That means armed with juice boxes.” Bill stared through the window. “I see a clown. A real one. Not like the terrifying inflatable kind. Like… paperwork clown.” Molly sighed. “Why is every party staffed? Where are their union negotiations? Focus.” Bill adjusted his headlamp. “Plan is simple. We go in, we locate the gift pile, we take everything shiny, and we leave before anyone says, ‘Who wants cake?’” Molly lifted a finger. “We take the presents quietly.” Bill said, “Quietly like we’re in a library.” Molly frowned. “Libraries are quiet because people are scared. We’re going to be quiet because we’re excellent.” Bill grinned. “I can do excellent. I can also do sneaky. I just need the correct ingredient.” Molly whispered, “Ingredient?” Bill pulled out a tiny bag. “Balloons. Inflatable decoys.” Molly’s eyes widened. “We’re using balloons as camouflage?” Bill nodded. “We float into the party like innocent party infrastructure.” Molly stared. “Bill. That is the dumbest genius idea I’ve ever heard.” Bill beamed. “Thank you. I polished it myself.” Molly took the rope from his hands. “Okay. I’m going to climb in through the cat window. You stay here and… what do you do again?” Bill pointed at his smartwatch. “I monitor the security system.” Molly blinked. “We’re breaking into a children’s party. Why do you have a security system app?” Bill shrugged. “The previous owner installed smart locks and then forgot to turn them off. I downloaded the app for ‘burglar preparedness’ and now it sends me notifications when someone opens the fridge.” Molly paused. “So the fridge is the real enemy.” Bill whispered, “The fridge is always watching.” Molly lifted a finger again. “If it sends you an alert mid-heist, you don’t panic.” Bill nodded seriously. “I will panic politely. Into my sleeve.” Molly crawled toward the hedge. “Fine. Cat window. Bag of suction cups. Stealth shoes.” Bill whispered, “Do you have stealth shoes?” Molly looked down at her sneakers. “They’re not loud.” Bill whispered, “All sneakers are loud.” Molly shot him a look. “Be quiet, Bill. We’re almost at the window.” Bill leaned in. “I can’t believe you stole this many lists from the internet.” Molly smiled without looking. “It wasn’t stealing. It was research. Also, I made a checklist.” Bill’s voice sharpened. “A checklist? For stealing presents?” Molly nodded. “Priority one: locate present mountain. Priority two: avoid stepping on the board game. Priority three: do not get emotionally blackmailed by the teddy bear.” Bill said, “The teddy bear will guilt-trip us?” Molly hissed, “It’s wearing a little vest that says ‘BIG HUGS.’ That’s manipulative.” Bill muttered, “I’m already being manipulated.” Molly slid closer to the cat window. “On my signal, Bill, hit the doorbell.” Bill blinked. “Wait, why the doorbell?” Molly whispered, “Because when adults hear a doorbell, they assume something normal is happening. Like pizza. Or a missed package. Or—” Bill finished, “Or an incredibly suspicious sound coming from a shrub.” Molly grinned. “Exactly.” Bill swallowed. “What if they check the window?” Molly tapped the glass. “Then we pretend to be… delivery people.” Bill asked, “Delivery people for whom?” Molly said, “For joy. For joy delivery. We’re Joycouriers.” Bill whispered, “I hate that. I love it.” Molly counted under her breath. “Three… two…” Bill raised his eyebrows. “If we do this, can we at least steal the LEGO set first?” Molly paused. “Yes. But only if it’s still warm from the box.” Bill whispered, “Warm LEGO. That’s horrifying.” Molly gripped the latch. “Signal. Doorbell now.” Bill pressed the doorbell like he was defusing a bomb shaped like happiness. “Okay. Done. If anyone asks, I’m here for the—” Molly cut him off. “Go, go, go.” Bill hissed, “I’m going! I’m going to be the world’s worst ghost!” Molly slipped into the window frame. “Welcome to the children’s party, Bill. Try not to squeak.” Bill whispered from outside, “I’m not squeaking. I’m… resonating.”
created_at
"2026-03-18T11:18:57.849Z"
3/18/2026, 11:18:57 AM
model
"openai/gpt-5.4-nano-20260317"
app_id
182717
external_user
(null)
streamed
true
cancelled
false
latency
3400
moderation_latency
(null)
generation_time
6082
tokens_prompt
77
tokens_completion
1569
native_tokens_prompt
66
native_tokens_completion
1267
native_tokens_completion_images
0
native_tokens_reasoning
72
native_tokens_cached
0
num_media_prompt
(null)
num_input_audio_prompt
(null)
num_media_completion
0
num_search_results
(null)
origin
"https://ncbench.com/"
is_byok
false
finish_reason
"stop"
native_finish_reason
"completed"
usage
0.00159695
router
(null)
provider_responses
0
endpoint_id
"0c835f2e-c18d-4e8c-b245-e1e3bd08b97f"
id
"resp_0b62304c5375ec6a0169ba8a21e7f08196b448ded0b13e90cb"
is_byok
false
latency
165
model_permaslug
"openai/gpt-5.4-nano-20260317"
provider_name
"OpenAI"
status
200
user_agent
"langchainjs-openai/1.0.0 ((node/v24.14.0; linux; x64))"
http_referer
(null)
api_type
"completions"
id
"gen-1773832737-EDFHuCPxoweWlJkzs8Bl"
upstream_id
"resp_0b62304c5375ec6a0169ba8a21e7f08196b448ded0b13e90cb"
total_cost
0.00159695
cache_discount
(null)
upstream_inference_cost
0
provider_name
"OpenAI"
Evaluation details
Result
Evaluator
Details
Meta Data
0.00%
Matches word count
n/a
neededClean
false
words
791
0.00%
Dialogue to Total Word Ratio
Ratio: 73.05%, Deviation: 23.05%
neededClean
false
wordsTotal
794
wordsDialogue
580
0.0000%