Model decision surface

Compare AI models

Start with Ling 3.1 Flash 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

Ling 3.1 Flash vs Muse Wonder

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

Open 1200×630 evidence receipt

Factual comparison of Ling 3.1 Flash and Muse Wonder
Field	Ling 3.1 Flash InclusionAI	Muse Wonder Meta
ModelCap position	#35	#6
Index score	71.6	83.0
Evidence	Estimated1 reported rows against measured corpus ladders (69%); global-corpus-prior over 211 held-out anchors (31%); shrunk 7.8 toward the measured corpus	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	Free	$4.00
Output / 1M	Free	$20.00
Pricing status	fresh	fresh
Context	262K	1M
Providers	1	1
Weight access	No linked weights	No linked weights

Decision facts

Ling 3.1 Flash is #35; Muse Wonder is #6 on the same current language board.
Index scores are 71.6 for Ling 3.1 Flash 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.
Both positions use Estimated evidence.
Listed output price per 1M tokens is $0 for Ling 3.1 Flash 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 $0 for Ling 3.1 Flash versus $18.00 for Muse Wonder. Ling 3.1 Flash costs 100.0% 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 262,144 tokens for Ling 3.1 Flash and 1,048,576 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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