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ModelCap

Model decision surface

Compare AI models

Start with Muse Wonder and Qwen3.7 Plus, 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.

Current public data

Muse Wonder vs Qwen3.7 Plus

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

Open 1200×630 evidence receipt
Factual comparison of Muse Wonder and Qwen3.7 Plus
Field
ModelCap position#6#34
Index score83.072.4
EvidenceEstimated1 reported rows against measured corpus ladders (69%); publisher-corpus-prior over 211 held-out anchors (31%); shrunk 0.2 up toward the measured corpusMeasured3 public benchmark observations across 3 boards
Input / 1M$4.00$0.32
Output / 1M$20.00$1.28
Pricing statusfreshfresh
Context1M1M
Providers11
Weight accessNo linked weightsNo linked weights

Decision facts

  • Muse Wonder is #6; Qwen3.7 Plus is #34 on the same current language board.
  • Index scores are 83.0 for Muse Wonder and 72.4 for Qwen3.7 Plus. Their published uncertainty intervals overlap, so the rank difference alone does not establish a reliable capability advantage for your workload.
  • Evidence differs: Muse Wonder is Estimated; Qwen3.7 Plus is Measured.
  • Listed output price per 1M tokens is $20.00 for Muse Wonder and $1.28 for Qwen3.7 Plus. For 1,000 requests with 2,000 input and 500 output tokens each (2M input + 0.5M output), the listed-rate estimate is $18.00 for Muse Wonder versus $1.28 for Qwen3.7 Plus. Qwen3.7 Plus costs 92.9% 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 Muse Wonder and 1,000,000 for Qwen3.7 Plus.

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

Each comparison page is a permanent, shareable URL with the same live figures as this tool: ModelCap Index position, API pricing, context window, provider count, weight access and every shared benchmark board.