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Nemotron 3 Super vs Trinity Large Thinking (free)
This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.
Model
Trinity Large Thinking (free)
Context window
262K
262,144 tokens · ~197K words
Context window · side by side
Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.
Same context window size for both models.
Nemotron 3 Super and Trinity Large Thinking (free) have identical context windows (262K tokens).
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Nemotron 3 Super | Trinity Large Thinking (free) |
|---|---|---|
| Context window | 262,144 tokens (262K) | 262,144 tokens (262K) |
| Max output tokens | N/A | 80,000 tokens (80K) |
| Speed tier | Balanced | Deep |
| Vision | No | No |
| Function calling | Yes | Yes |
| Extended thinking | Yes | Yes |
| Prompt caching | Yes | No |
| Batch API | No | No |
| Release date | Mar 2026 | Apr 2026 |
Frequently asked questions
Powered by Mem0
Use a smaller model.
Get better results.
Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.
Example: a multi-turn chat session
80% less to send — works with any model