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ModelCap

Model decision surface

Compare AI models

Start with Mistral Medium 3.5 and GPT-5.5 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

Mistral Medium 3.5 vs GPT-5.5 Pro

Live dataset updated 9/15/2026, 9:19:37 PM UTC

Open 1200×630 evidence receipt
Factual comparison of Mistral Medium 3.5 and GPT-5.5 Pro
Field
ModelCap position#63#54
Index score55.958.9
EvidenceMeasured4 public benchmark observations across 4 boardsMeasuredpublisher-corpus-prior over 194 held-out anchors (61%); 1 specialist-board observation at 2.1% support (39%); shrunk 13.8 toward the measured corpus
Input / 1M$1.50$30.00
Output / 1M$7.50$180
Pricing statusfreshfresh
Context262K1M
Providers11
Weight accessAPI onlyAPI only

Decision facts

  • Mistral Medium 3.5 is #63; GPT-5.5 Pro is #54 on the same current language board.
  • Index scores are 55.9 for Mistral Medium 3.5 and 58.9 for GPT-5.5 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 $7.50 for Mistral Medium 3.5 and $180.00 for GPT-5.5 Pro. For 1,000 requests with 2,000 input and 500 output tokens each (2M input + 0.5M output), the listed-rate estimate is $6.75 for Mistral Medium 3.5 versus $150.00 for GPT-5.5 Pro. Mistral Medium 3.5 costs 95.5% 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 262,144 tokens for Mistral Medium 3.5 and 1,050,000 for GPT-5.5 Pro.

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.