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ModelCap

Model decision surface

Compare AI models

Start with GPT-6 Luna and MiMo-V2.6-Pro, 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

GPT-6 Luna vs MiMo-V2.6-Pro

Live dataset updated 9/22/2026, 10:37:09 PM UTC

Open 1200×630 evidence receipt
Factual comparison of GPT-6 Luna and MiMo-V2.6-Pro
Field
ModelCap position#25#5
Index score78.688.1
EvidenceMeasured3 public benchmark observations across 3 boardsMeasured1 public benchmark observation across 1 board
Input / 1M$0.10$0.435
Output / 1M$0.50$0.87
Pricing statusfreshfresh
Context1M1M
Providers22
Weight accessAPI onlyOpen weights

Decision facts

  • GPT-6 Luna is #25; MiMo-V2.6-Pro is #5 on the same current language board.
  • Index scores are 78.6 for GPT-6 Luna and 88.1 for MiMo-V2.6-Pro. 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 $0.50 for GPT-6 Luna and $0.87 for MiMo-V2.6-Pro. For 1,000 requests with 2,000 input and 500 output tokens each (2M input + 0.5M output), the listed-rate estimate is $0.45 for GPT-6 Luna versus $1.30 for MiMo-V2.6-Pro. GPT-6 Luna costs 65.5% 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 1,050,000 tokens for GPT-6 Luna and 1,048,576 for MiMo-V2.6-Pro.
  • Weight access differs: GPT-6 Luna is none; MiMo-V2.6-Pro is open.

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.