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Start with DeepSeek V4 Pro 0813 and Muse Wonder, or choose any two current ranked language models. Compare capability evidence, price, context, provider availability, and weight access without pretending one field decides every use case.

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Current public data

DeepSeek V4 Pro 0813 vs Muse Wonder

Live dataset updated 10/12/2026, 5:49:31 PM UTC

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Factual comparison of DeepSeek V4 Pro 0813 and Muse Wonder
Field	DeepSeek V4 Pro 0813 DeepSeek	Muse Wonder Meta
ModelCap position	#28	#6
Index score	76.1	83.0
Evidence	Measured3 public benchmark observations across 3 boards	Estimated1 reported rows against measured corpus ladders (69%); publisher-corpus-prior over 211 held-out anchors (31%); shrunk 0.2 up toward the measured corpus
Input / 1M	$0.66	$4.00
Output / 1M	$1.98	$20.00
Pricing status	fresh	fresh
Context	1M	1M
Providers	20	1
Weight access	Open weights	No linked weights

Decision facts

DeepSeek V4 Pro 0813 is #28; Muse Wonder is #6 on the same current language board.
Index scores are 76.1 for DeepSeek V4 Pro 0813 and 83.0 for Muse Wonder. Their published uncertainty intervals overlap, so the rank difference alone does not establish a reliable capability advantage for your workload.
Evidence differs: DeepSeek V4 Pro 0813 is Measured; Muse Wonder is Estimated.
Listed output price per 1M tokens is $1.98 for DeepSeek V4 Pro 0813 and $20.00 for Muse Wonder. For 1,000 requests with 2,000 input and 500 output tokens each (2M input + 0.5M output), the listed-rate estimate is $2.31 for DeepSeek V4 Pro 0813 versus $18.00 for Muse Wonder. DeepSeek V4 Pro 0813 costs 87.2% less in this scenario. This excludes caching, batch discounts, prompt-length tiers, tool charges and retries; verify the selected endpoint before budgeting.
Published context is 1,048,576 tokens for DeepSeek V4 Pro 0813 and 1,048,576 for Muse Wonder.
Weight access differs: DeepSeek V4 Pro 0813 is open; Muse Wonder is none.
ModelCap currently lists 20 providers for DeepSeek V4 Pro 0813 and 1 for Muse Wonder.

These are separate published fields, not a synthetic winner. ModelCap does not collapse price, access, context, and capability evidence into a hidden recommendation score.

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