Context Window

Compare

Grok4 6 vs o1

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

Grok4 6

Context window

2M

2,000,000 tokens · ~1.5M words

Model page
Openai

Model

o1

Image inputTool calling

Context window

200K

200,000 tokens · ~150K 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.

Grok4 62M
o1200K

Grok4 6 has about 10× the context window of the other in this pair.

Token calculator →Memory savings →

Grok4 6 has 900% more context capacity (2000K vs 200K tokens). Grok4 6 is 86% 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 200K).

  • RAG / high-volume retrieval

    Use Grok4 6. 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.

SpecGrok4 6o1
Context window2,000,000 tokens (2000K)200,000 tokens (200K)
Max output tokensN/A100,000 tokens (100K)
Speed tierBalancedDeep
VisionNoYes
Function callingNoYes
Extended thinkingNoYes
Prompt cachingYesYes
Batch APINoYes
Release dateN/ADec 2024

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.

ProviderGrok4 6 inGrok4 6 outo1 ino1 out
Azure——$16.50/M$66.00/M
Gmi$2.00/M$6.00/M——
Openai——$15.00/M$60.00/M
Openrouter——$15.00/M$60.00/M
Replicate——$15.00/M$60.00/M

Frequently asked questions

Grok4 6 has a larger context window: 2000K tokens vs 200K. 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