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ModelCap

Model decision surface

Compare AI models

Start with Kimi K3 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 vs GLM 5.3

Live dataset updated 10/2/2026, 11:14:31 PM UTC

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

Moonshot AI

ModelCap position#9#11
Index score81.680.0
EvidenceMeasured6 public benchmark observations across 6 boardsMeasured4 public benchmark observations across 4 boards
Input / 1M$2.70$1.40
Output / 1M$13.50$4.40
Pricing statusfreshfresh
Context1M1M
Providers1932
Weight accessRestricted licenseRestricted license

Decision facts

  • Kimi K3 is #9; GLM 5.3 is #11 on the same current language board.
  • Index scores are 81.6 for Kimi K3 and 80.0 for GLM 5.3. Their published uncertainty intervals overlap, so the rank difference alone does not establish a reliable capability advantage for your workload.
  • Both positions use Measured evidence.
  • Listed output price per 1M tokens is $13.50 for Kimi K3 and $4.40 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 $12.15 for Kimi K3 versus $5.00 for GLM 5.3. GLM 5.3 costs 58.8% 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 and 1,048,576 for GLM 5.3.
  • ModelCap currently lists 19 providers for Kimi K3 and 32 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.