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Kimi K2 7 Code Highspeed vs Trinity Large Thinking

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.

Moonshot

Model

Kimi K2 7 Code Highspeed

Context window

262K

262,144 tokens · ~197K words

Model page
Arcee Ai

Model

Trinity Large Thinking

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.

Kimi K2 7 Code Highspeed262K
Trinity Large Thinking262K

Same context window size for both models.

Kimi K2 7 Code Highspeed and Trinity Large Thinking have identical context windows (262K tokens). Trinity Large Thinking is 86% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Trinity Large Thinking. Input tokens are 86% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecKimi K2 7 Code HighspeedTrinity Large Thinking
Context window262,144 tokens (262K)262,144 tokens (262K)
Max output tokensN/A262,144 tokens (262K)
Speed tierBalancedDeep
VisionNoNo
Function callingNoYes
Extended thinkingNoYes
Prompt cachingYesYes
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.

ProviderKimi K2 7 Code Highspeed inKimi K2 7 Code Highspeed outTrinity Large Thinking inTrinity Large Thinking out
Gmi$1.90/M$8.00/M——
Openrouter——$0.250/M$0.800/M

Frequently asked questions

Trinity Large Thinking has a larger context window: 262K 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.

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