Model decision surface

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Start with gemma 4 E2B it and Qwen3.5-9B, 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.

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Current public data

gemma 4 E2B it vs Qwen3.5-9B

Live dataset updated 9/13/2026, 2:40:29 PM UTC

Open 1200×630 evidence receipt

Factual comparison of gemma 4 E2B it and Qwen3.5-9B
Field	gemma 4 E2B it Google	Qwen3.5-9B Qwen
ModelCap position	#109	#107
Index score	40.5	41.3
Evidence	Estimatedpublisher-corpus-prior over 186 held-out anchors (8%); launch card against 3 resolved peers on 5 rows (92%); no cross-lab optimism probe available; shrunk 2.2 up toward the measured corpus	Estimatedpublisher-corpus-prior over 186 held-out anchors (29%); launch card against 3 resolved peers on 10 rows (71%); exceeds every named peer on 4 of 10 rows; optimism haircut 0.6 from cross-lab-probe; shrunk 3.6 up toward the measured corpus
Input / 1M	Unavailable	$0.10
Output / 1M	Unavailable	$0.15
Pricing status	unavailable	fresh
Context	—	262K
Providers	0	6
Weight access	Open weights	Open weights

Decision facts

gemma 4 E2B it is #109; Qwen3.5-9B is #107 on the same current language board.
Index scores are 40.5 for gemma 4 E2B it and 41.3 for Qwen3.5-9B. Their published uncertainty intervals overlap, so the rank difference alone does not establish a reliable capability advantage for your workload.
Both positions use Estimated evidence.
ModelCap currently lists 0 providers for gemma 4 E2B it and 6 for Qwen3.5-9B.

These are separate published fields, not a synthetic winner. ModelCap does not collapse price, access, context, and capability evidence into a hidden recommendation score.

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