Context Window

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Glm 5p3 Us vs Gov Xai Grok 4 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.

Z Ai

Model

Glm 5p3 Us

Tool calling

Context window

1.0M

1,048,576 tokens · ~786K words

Model page
Xai

Model

Gov Xai Grok 4 6

Image inputTool calling

Context window

500K

500,000 tokens · ~375K 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.

Glm 5p3 Us1.0M
Gov Xai Grok 4 6500K

Glm 5p3 Us has about 2.1× the context window of the other in this pair.

Token calculator →Memory savings →

Glm 5p3 Us has 109% more context capacity (1048K vs 500K tokens). Glm 5p3 Us is 20% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Glm 5p3 Us. Its 1048K context fits entire documents without chunking (vs 500K).

  • RAG / high-volume retrieval

    Use Glm 5p3 Us. Input tokens are 20% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Gov Xai Grok 4 6. Its 500K 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.

SpecGlm 5p3 UsGov Xai Grok 4 6
Context window1,048,576 tokens (1048K)500,000 tokens (500K)
Max output tokens128,000 tokens (128K)500,000 tokens (500K)
Speed tierBalancedBalanced
VisionNoYes
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesYes
Batch APINoNo
Release dateN/AN/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.

ProviderGlm 5p3 Us inGlm 5p3 Us outGov Xai Grok 4 6 inGov Xai Grok 4 6 out
Aws Bedrock——$2.64/M$7.92/M
Fireworks$2.10/M$6.60/M——

Frequently asked questions

Glm 5p3 Us has a larger context window: 1048K tokens vs 500K. 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