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ModelCap

Model decision surface

Compare AI models

Start with GPT-5.5 Pro and o4 Mini, 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-5.5 Pro vs o4 Mini

Live dataset updated 9/22/2026, 9:31:46 PM UTC

Open 1200×630 evidence receipt
Factual comparison of GPT-5.5 Pro and o4 Mini
Field
o4 Mini

OpenAI

ModelCap position#59#106
Index score64.351.6
EvidenceMeasuredpublisher-corpus-prior over 208 held-out anchors (59%); 1 specialist-board observation at 2.1% support (41%); shrunk 10.1 toward the measured corpusMeasured3 public benchmark observations across 3 boards
Input / 1M$30.00$1.10
Output / 1M$180$4.40
Pricing statusfreshfresh
Context1M200K
Providers11
Weight accessAPI onlyAPI only

Decision facts

  • GPT-5.5 Pro is #59; o4 Mini is #106 on the same current language board.
  • Index scores are 64.3 for GPT-5.5 Pro and 51.6 for o4 Mini. 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 $180.00 for GPT-5.5 Pro and $4.40 for o4 Mini. For 1,000 requests with 2,000 input and 500 output tokens each (2M input + 0.5M output), the listed-rate estimate is $150.00 for GPT-5.5 Pro versus $4.40 for o4 Mini. o4 Mini costs 97.1% 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-5.5 Pro and 200,000 for o4 Mini.

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.