Context Window

Compare

Deepseek V3 1 Terminus 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.

Deepseek

Model

Deepseek V3 1 Terminus

Tool calling

Context window

164K

163,840 tokens · ~123K 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.

Deepseek V3 1 Terminus164K
Trinity Large Thinking (free)262K

Trinity Large Thinking (free) has about 1.6× the context window of the other in this pair.

Token calculator →Memory savings →

Trinity Large Thinking (free) has 60% more context capacity (262K vs 163K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Trinity Large Thinking (free). Its 262K context fits entire documents without chunking (vs 163K).

  • Long output (reports, code files)

    Use Deepseek V3 1 Terminus. Its 163K 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.

SpecDeepseek V3 1 TerminusTrinity Large Thinking (free)
Context window163,840 tokens (163K)262,144 tokens (262K)
Max output tokens163,840 tokens (163K)80,000 tokens (80K)
Speed tierBalancedDeep
VisionNoNo
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.

ProviderDeepseek V3 1 Terminus inDeepseek V3 1 Terminus outTrinity Large Thinking (free) inTrinity Large Thinking (free) out
Deepinfra$0.270/M$1.00/M——
Novita$0.270/M$1.00/M——

Frequently asked questions

Trinity Large Thinking (free) has a larger context window: 262K tokens vs 163K. 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
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Current turn

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