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ModelCap

Model decision surface

Compare AI models

Start with Gemini 3.8 Flash and Gemma 3 4B, 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

Gemini 3.8 Flash vs Gemma 3 4B

Live dataset updated 10/18/2026, 4:49:00 AM UTC

Open 1200×630 evidence receipt
Factual comparison of Gemini 3.8 Flash and Gemma 3 4B
Field
ModelCap position#5#239
Index score83.526.6
EvidenceMeasured5 public benchmark observations across 5 boardsMeasured2 public benchmark observations across 2 boards
Input / 1M$0.825$0.05
Output / 1M$4.13$0.10
Pricing statusfreshfresh
Context1M131K
Providers22
Weight accessNo linked weightsGated access

Decision facts

  • Gemini 3.8 Flash is #5; Gemma 3 4B is #239 on the same current language board.
  • Index scores are 83.5 for Gemini 3.8 Flash and 26.6 for Gemma 3 4B. Their published uncertainty intervals do not overlap, but the aggregate Index does not predict performance on every task; compare the relevant benchmark configurations before choosing.
  • Both positions use Measured evidence.
  • Listed output price per 1M tokens is $4.13 for Gemini 3.8 Flash and $0.10 for Gemma 3 4B. For 1,000 requests with 2,000 input and 500 output tokens each (2M input + 0.5M output), the listed-rate estimate is $3.71 for Gemini 3.8 Flash versus $0.15 for Gemma 3 4B. Gemma 3 4B costs 96.0% 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 1,048,576 tokens for Gemini 3.8 Flash and 131,072 for Gemma 3 4B.
  • Weight access differs: Gemini 3.8 Flash is none; Gemma 3 4B is gated.

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.