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ModelCap

Model decision surface

Compare AI models

Start with LongCat 2.0 and Hy4 preview, 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

LongCat 2.0 vs Hy4 preview

Live dataset updated 9/9/2026, 8:46:14 PM UTC

Open 1200×630 evidence receipt
Factual comparison of LongCat 2.0 and Hy4 preview
Field
ModelCap position#10#26
Index score79.070.4
EvidenceEstimatedglobal-corpus-prior over 182 held-out anchors (7%); launch card against 4 resolved peers on 6 rows (93%); no cross-lab optimism probe available; shrunk 2 toward the measured corpusMeasured9 reported rows against measured corpus ladders (38%); publisher-corpus-prior over 182 held-out anchors (12%); opens above measured predecessor tencent/hy3 on same-family ordering evidence (41%); 1 specialist-board observation at 2.5% support (9%); shrunk 1.2 toward the measured corpus
Input / 1M$0.30$0.834
Output / 1M$1.20$2.50
Pricing statusfreshfresh
Context1M1M
Providers11
Weight accessOpen weightsOpen weights

Decision facts

  • LongCat 2.0 is #10; Hy4 preview is #26 on the same current language board.
  • Index scores are 79.0 for LongCat 2.0 and 70.4 for Hy4 preview. Their published uncertainty intervals overlap, so the rank difference alone does not establish a reliable capability advantage for your workload.
  • Evidence differs: LongCat 2.0 is Estimated; Hy4 preview is Measured.
  • Listed output price per 1M tokens is $1.20 for LongCat 2.0 and $2.50 for Hy4 preview. For 1,000 requests with 2,000 input and 500 output tokens each (2M input + 0.5M output), the listed-rate estimate is $1.20 for LongCat 2.0 versus $2.92 for Hy4 preview. LongCat 2.0 costs 58.9% 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 1,048,756 tokens for LongCat 2.0 and 1,048,576 for Hy4 preview.

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.