Context Window

Compare

GPT-5.6 Luna Pro vs Grok 3 Mini Fast Beta

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-5.6 Luna Pro

Image inputTool calling

Context window

1.1M

1,050,000 tokens · ~788K words

Model page
Xai

Model

Grok 3 Mini Fast Beta

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.

GPT-5.6 Luna Pro1.1M
Grok 3 Mini Fast Beta131K

GPT-5.6 Luna Pro has about 8× the context window of the other in this pair.

Token calculator →Memory savings →

GPT-5.6 Luna Pro has 701% more context capacity (1050K vs 131K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use GPT-5.6 Luna Pro. Its 1050K context fits entire documents without chunking (vs 131K).

  • Long output (reports, code files)

    Use Grok 3 Mini Fast Beta. Its 131K 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-5.6 Luna ProGrok 3 Mini Fast Beta
Context window1,050,000 tokens (1050K)131,072 tokens (131K)
Max output tokens128,000 tokens (128K)131,072 tokens (131K)
Speed tierBalancedFast
VisionYesNo
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesYes
Batch APINoNo
Release dateJul 2026N/A

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-5.6 Luna Pro inGPT-5.6 Luna Pro outGrok 3 Mini Fast Beta inGrok 3 Mini Fast Beta out
Xai——$0.600/M$4.00/M

Frequently asked questions

GPT-5.6 Luna Pro has a larger context window: 1050K 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.

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Use a smaller model.
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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

80% less to send — works with any model