Side-by-side comparison
Trinity Large Thinking vs MiniMax M2
Arcee AI
Trinity Large Thinking
🔧 Tools
{} JSON
Input / 1M
$0.2200
Output / 1M
$0.8500
| Trinity Large Thinking | MiniMax M2 | |
|---|---|---|
| Provider | Arcee AI | MiniMax |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 262,144 | 196,608 |
| Capabilities Optional capabilities the model advertises: vision (images), tools (function calling), json_mode (structured output). | tools, json_mode | tools, json_mode |
| Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary → | 0.2200 | 0.2550 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.8500 | 1.0000 |
Frequently asked questions
Which is cheaper, Trinity Large Thinking or MiniMax M2?
Trinity Large Thinking is cheaper than MiniMax M2 on a 50/50 input/output blend by about $0.0925 per 1M tokens. Exact savings depend on your input-vs-output ratio — use the cost calculator on this page for a workload-specific estimate.
Which has a larger context window, Trinity Large Thinking or MiniMax M2?
Trinity Large Thinking has the larger context window at 262k tokens versus 197k tokens for MiniMax M2. That means Trinity Large Thinking can ingest about 1.3x as much text per request.
What is the difference between Trinity Large Thinking and MiniMax M2?
Trinity Large Thinking comes from Arcee AI; MiniMax M2 comes from MiniMax. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.