Compare

o1-pro (batch) vs Trinity Large Preview (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.

Openai

Model

o1-pro (batch)

Image input

Context window

200K

200,000 tokens · ~150K words

Model page
Arcee Ai

Model

Trinity Large Preview (free)

Tool calling

Context window

131K

131,000 tokens · ~98K words

Model page

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.

o1-pro (batch)200K
Trinity Large Preview (free)131K

o1-pro (batch) has about 1.5× the context window of the other in this pair.

o1-pro (batch) has 52% more context capacity (200K vs 131K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use o1-pro (batch). Its 200K context fits entire documents without chunking (vs 131K).

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

Speco1-pro (batch)Trinity Large Preview (free)
Context window200,000 tokens (200K)131,000 tokens (131K)
Max output tokens100,000 tokens (100K)N/A
Speed tierDeepDeep
VisionYesNo
Function callingNoYes
Extended thinkingYesNo
Prompt cachingNoNo
Batch APIYesNo
Release dateMar 2025Jan 2026

Frequently asked questions

o1-pro (batch) has a larger context window: 200K tokens vs 131K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

Powered by Mem0

Use a smaller model.
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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

Without Mem0~128K tokens sent
Full history
Repeated info
Old context
With Mem0~20K tokens sent
Key memories
Current turn

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