Model decision surface

Compare AI models

Start with DeepSeek V3.2 and Laguna M.1, 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

DeepSeek V3.2 vs Laguna M.1

Live dataset updated 9/9/2026, 8:46:14 PM UTC

Open 1200×630 evidence receipt

Factual comparison of DeepSeek V3.2 and Laguna M.1
Field	DeepSeek V3.2 DeepSeek	Laguna M.1 Poolside
ModelCap position	#51	#50
Index score	58.2	58.4
Evidence	Measured4 public benchmark observations across 4 boards	Estimated3 reported rows against measured corpus ladders (55%); global-corpus-prior over 182 held-out anchors (45%); shrunk 4.3 toward the measured corpus
Input / 1M	$0.269	Unavailable
Output / 1M	$0.40	Unavailable
Pricing status	fresh	unavailable
Context	164K	—
Providers	15	0
Weight access	Open weights	Open weights

Decision facts

DeepSeek V3.2 is #51; Laguna M.1 is #50 on the same current language board.
Index scores are 58.2 for DeepSeek V3.2 and 58.4 for Laguna M.1. Their published uncertainty intervals overlap, so the rank difference alone does not establish a reliable capability advantage for your workload.
Evidence differs: DeepSeek V3.2 is Measured; Laguna M.1 is Estimated.
ModelCap currently lists 15 providers for DeepSeek V3.2 and 0 for Laguna M.1.

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

Popular comparisons

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.