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ModelCap

Model decision surface

Compare AI models

Start with granite 4.2 30b and Qwen3 4B Instruct 2507, 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

granite 4.2 30b vs Qwen3 4B Instruct 2507

Live dataset updated 9/9/2026, 9:50:24 PM UTC

Open 1200×630 evidence receipt
Factual comparison of granite 4.2 30b and Qwen3 4B Instruct 2507
Field
granite 4.2 30b

IBM Granite

ModelCap position#123#124
Index score33.332.4
EvidenceMeasured2 public benchmark observations across 2 boardsEstimatedpublisher-corpus-prior over 180 held-out anchors (18%); launch card against 2 resolved peers on 9 rows (82%); exceeds every named peer on 7 of 9 rows; no cross-lab optimism probe available; shrunk 4.1 up toward the measured corpus
Input / 1MUnavailableUnavailable
Output / 1MUnavailableUnavailable
Pricing statusunavailableunavailable
Context131K262K
Providers00
Weight accessOpen weightsOpen weights

Decision facts

  • granite 4.2 30b is #123; Qwen3 4B Instruct 2507 is #124 on the same current language board.
  • Index scores are 33.3 for granite 4.2 30b and 32.4 for Qwen3 4B Instruct 2507. Their published uncertainty intervals overlap, so the rank difference alone does not establish a reliable capability advantage for your workload.
  • Evidence differs: granite 4.2 30b is Measured; Qwen3 4B Instruct 2507 is Estimated.
  • Published context is 131,072 tokens for granite 4.2 30b and 262,144 for Qwen3 4B Instruct 2507.

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.