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ModelCap

Model decision surface

Compare AI models

Start with NextCoder 32B and Phi 3.5 MoE instruct, 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

NextCoder 32B vs Phi 3.5 MoE instruct

Live dataset updated 9/22/2026, 3:10:38 AM UTC

Open 1200×630 evidence receipt
Factual comparison of NextCoder 32B and Phi 3.5 MoE instruct
Field
NextCoder 32B

Microsoft

ModelCap position#203#162
Index score17.032.0
EvidenceInheritedfinetune of qwen/qwen-2.5-coder-32b-instruct · finetune (100%)Estimatedglobal-corpus-prior over 203 held-out anchors (42%); launch card against 4 resolved peers on 6 rows (58%); exceeds every named peer on 4 of 6 rows; optimism haircut 0.67 from cross-lab-probe; shrunk 16.8 up toward the measured corpus
Input / 1MUnavailableUnavailable
Output / 1MUnavailableUnavailable
Pricing statusunavailableunavailable
Context——
Providers00
Weight accessOpen weightsOpen weights

Decision facts

  • NextCoder 32B is #203; Phi 3.5 MoE instruct is #162 on the same current language board.
  • Index scores are 17.0 for NextCoder 32B and 32.0 for Phi 3.5 MoE instruct. Their published uncertainty intervals overlap, so the rank difference alone does not establish a reliable capability advantage for your workload.
  • Evidence differs: NextCoder 32B is Inherited; Phi 3.5 MoE instruct is Estimated.

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.