Context Window

Compare

Gpt 5 4 2026 03 05 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.

Openai

Model

Gpt 5 4 2026 03 05

Image inputTool calling

Context window

1.1M

1,050,000 tokens · ~788K words

Model page
Arcee Ai

Model

Trinity Large Thinking (free)

Tool calling

Context window

262K

262,144 tokens · ~197K 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.

Gpt 5 4 2026 03 051.1M
Trinity Large Thinking (free)262K

Gpt 5 4 2026 03 05 has about 4× the context window of the other in this pair.

Token calculator →Memory savings →

Gpt 5 4 2026 03 05 has 300% more context capacity (1050K vs 262K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Gpt 5 4 2026 03 05. Its 1050K context fits entire documents without chunking (vs 262K).

  • Long output (reports, code files)

    Use Gpt 5 4 2026 03 05. Its 128K max output lets you generate complete artifacts in one request.

Full specs

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

SpecGpt 5 4 2026 03 05Trinity Large Thinking (free)
Context window1,050,000 tokens (1050K)262,144 tokens (262K)
Max output tokens128,000 tokens (128K)80,000 tokens (80K)
Speed tierBalancedDeep
VisionYesNo
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesNo
Batch APINoNo
Release dateN/AApr 2026

Pricing matrix

Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.

ProviderGpt 5 4 2026 03 05 inGpt 5 4 2026 03 05 outTrinity Large Thinking (free) inTrinity Large Thinking (free) out
Azure$2.50/M$15.00/M——
Openai$2.50/M$15.00/M——

Frequently asked questions

Gpt 5 4 2026 03 05 has a larger context window: 1050K tokens vs 262K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

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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.

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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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