Compare

Grok 4 5 Latest vs Trinity Mini (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.

Xai

Model

Grok 4 5 Latest

Image inputTool calling

Context window

500K

500,000 tokens · ~375K words

Model page
Arcee Ai

Model

Trinity Mini (free)

Tool calling

Context window

131K

131,072 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.

Grok 4 5 Latest500K
Trinity Mini (free)131K

Grok 4 5 Latest has about 3.8× the context window of the other in this pair.

Grok 4 5 Latest has 281% more context capacity (500K vs 131K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Grok 4 5 Latest. Its 500K 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.

SpecGrok 4 5 LatestTrinity Mini (free)
Context window500,000 tokens (500K)131,072 tokens (131K)
Max output tokens500,000 tokens (500K)N/A
Speed tierBalancedFast
VisionYesNo
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesNo
Batch APINoNo
Release dateN/ADec 2025

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.

ProviderGrok 4 5 Latest inGrok 4 5 Latest outTrinity Mini (free) inTrinity Mini (free) out
Xai$2.00/M$6.00/M

Frequently asked questions

Grok 4 5 Latest has a larger context window: 500K 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.
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

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

80% less to send — works with any model