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ModelCap

Model decision surface

Compare AI models

Start with Kimi K3.1 Pro and GLM 5.3, 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

Kimi K3.1 Pro vs GLM 5.3

Live dataset updated 10/23/2026, 7:44:34 PM UTC

Open 1200×630 evidence receipt
Factual comparison of Kimi K3.1 Pro and GLM 5.3
Field
Kimi K3.1 Pro

Moonshot AI

ModelCap position#21#18
Index score78.679.0
EvidenceEstimated1 reported rows against measured corpus ladders (61%); publisher-corpus-prior over 211 held-out anchors (39%); shrunk 4 toward the measured corpusMeasured3 public benchmark observations across 3 boards
Input / 1M$2.00$0.079
Output / 1M$10.00$1.40
Pricing statusfreshfresh
Context1M1M
Providers137
Weight accessNo linked weightsRestricted license

Decision facts

  • Kimi K3.1 Pro is #21; GLM 5.3 is #18 on the same current language board.
  • Index scores are 78.6 for Kimi K3.1 Pro and 79.0 for GLM 5.3. Their published uncertainty intervals overlap, so the rank difference alone does not establish a reliable capability advantage for your workload.
  • Evidence differs: Kimi K3.1 Pro is Estimated; GLM 5.3 is Measured.
  • Listed output price per 1M tokens is $10.00 for Kimi K3.1 Pro and $1.40 for GLM 5.3. For 1,000 requests with 2,000 input and 500 output tokens each (2M input + 0.5M output), the listed-rate estimate is $9.00 for Kimi K3.1 Pro versus $0.86 for GLM 5.3. GLM 5.3 costs 90.5% 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 Kimi K3.1 Pro and 1,048,576 for GLM 5.3.
  • Weight access differs: Kimi K3.1 Pro is none; GLM 5.3 is restricted.
  • ModelCap currently lists 1 providers for Kimi K3.1 Pro and 37 for GLM 5.3.

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.