Context Window

Compare

GPT-5.6 Luna Pro (batch) vs Grok4 6

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 (batch)

Image inputTool calling

Context window

1.1M

1,050,000 tokens · ~788K words

Model page
Xai

Model

Grok4 6

Context window

2M

2,000,000 tokens · ~1.5M 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 Pro (batch)1.1M
Grok4 62M

Grok4 6 has about 1.9× the context window of the other in this pair.

Token calculator →Memory savings →

Grok4 6 has 90% more context capacity (2000K vs 1050K tokens). GPT-5.6 Luna Pro (batch) is 95% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Grok4 6. Its 2000K context fits entire documents without chunking (vs 1050K).

  • RAG / high-volume retrieval

    Use GPT-5.6 Luna Pro (batch). Input tokens are 95% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecGPT-5.6 Luna Pro (batch)Grok4 6
Context window1,050,000 tokens (1050K)2,000,000 tokens (2000K)
Max output tokens128,000 tokens (128K)N/A
Speed tierBalancedBalanced
VisionYesNo
Function callingYesNo
Extended thinkingYesNo
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 (batch) inGPT-5.6 Luna Pro (batch) outGrok4 6 inGrok4 6 out
Gmi——$2.00/M$6.00/M
Openrouter$0.100/M$0.600/M——

Frequently asked questions

Grok4 6 has a larger context window: 2000K tokens vs 1050K. 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
Key memories
Current turn

80% less to send — works with any model