L LLM Cloud Hub
Side-by-side comparison

Trinity Large Thinking (free) vs Laguna M.1 (free)

Arcee AI

Trinity Large Thinking (free)

πŸ”§ Tools
Input / 1M
$0.0000
Output / 1M
$0.0000
View Trinity Large Thinking (free) β†’
Poolside

Laguna M.1 (free)

πŸ”§ Tools
Input / 1M
$0.0000
Output / 1M
$0.0000
View Laguna M.1 (free) β†’
Trinity Large Thinking (free)Laguna M.1 (free)
Provider Arcee AI Poolside
Context window Maximum tokens (input + output) the model can process in a single request. Glossary β†’ 262,144 131,072
Capabilities Optional capabilities the model advertises: vision (images), tools (function calling), json_mode (structured output). tools tools
Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary β†’ 0.0000 0.0000
Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5Γ— pricier than input. Glossary β†’ 0.0000 0.0000

Frequently asked questions

Which is cheaper, Trinity Large Thinking (free) or Laguna M.1 (free)?

Trinity Large Thinking (free) is cheaper than Laguna M.1 (free) on a 50/50 input/output blend by about $0 per 1M tokens. Exact savings depend on your input-vs-output ratio β€” use the cost calculator on this page for a workload-specific estimate.

Which has a larger context window, Trinity Large Thinking (free) or Laguna M.1 (free)?

Trinity Large Thinking (free) has the larger context window at 262k tokens versus 131k tokens for Laguna M.1 (free). That means Trinity Large Thinking (free) can ingest about 2.0x as much text per request.

What is the difference between Trinity Large Thinking (free) and Laguna M.1 (free)?

Trinity Large Thinking (free) comes from Arcee AI; Laguna M.1 (free) comes from Poolside. They differ in pricing, context window, and supported capabilities β€” see the side-by-side table on this page for the exact figures, refreshed nightly.

Keyboard shortcuts

?
Show this overlay
/
Focus the first form field
g h
Go to / (home)
g b
Go to /best-llm-for
g c
Go to /cost
g s
Go to /self-hosted
g x
Go to /compliance
Esc
Close any overlay

Inspired by Linear and GitHub conventions. The two-key sequences (g then h) work within ~1 second.