Context Window

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Mistral Large 3 2512 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.

Mistral

Model

Mistral Large 3 2512

Image inputTool calling

Context window

262K

262,144 tokens · ~197K 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.

Mistral Large 3 2512262K
Trinity Large Thinking (free)262K

Same context window size for both models.

Token calculator →Memory savings →

Mistral Large 3 2512 and Trinity Large Thinking (free) have identical context windows (262K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long output (reports, code files)

    Use Mistral Large 3 2512. Its 262K 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.

SpecMistral Large 3 2512Trinity Large Thinking (free)
Context window262,144 tokens (262K)262,144 tokens (262K)
Max output tokens262,144 tokens (262K)80,000 tokens (80K)
Speed tierDeepDeep
VisionYesNo
Function callingYesYes
Extended thinkingNoYes
Prompt cachingYesNo
Batch APINoNo
Release dateDec 2025Apr 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.

ProviderMistral Large 3 2512 inMistral Large 3 2512 outTrinity Large Thinking (free) inTrinity Large Thinking (free) out
Mistral$0.500/M$1.50/M——
Openrouter$0.500/M$1.50/M——

Frequently asked questions

Trinity Large Thinking (free) 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.

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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

80% less to send — works with any model