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Databricks Grok 4 7 vs Glm 4 7 Fp8

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

Databricks Grok 4 7

Image inputTool calling

Context window

500K

500,000 tokens · ~375K words

Model page
Z Ai

Model

Glm 4 7 Fp8

Context window

203K

202,752 tokens · ~152K 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.

Databricks Grok 4 7500K
Glm 4 7 Fp8203K

Databricks Grok 4 7 has about 2.5× the context window of the other in this pair.

Databricks Grok 4 7 has 146% more context capacity (500K vs 202K tokens). Glm 4 7 Fp8 is 84% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Databricks Grok 4 7. Its 500K context fits entire documents without chunking (vs 202K).

  • RAG / high-volume retrieval

    Use Glm 4 7 Fp8. Input tokens are 84% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Databricks Grok 4 7. 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.

SpecDatabricks Grok 4 7Glm 4 7 Fp8
Context window500,000 tokens (500K)202,752 tokens (202K)
Max output tokens500,000 tokens (500K)16,384 tokens (16K)
Speed tierBalancedBalanced
VisionYesNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
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.

ProviderDatabricks Grok 4 7 inDatabricks Grok 4 7 outGlm 4 7 Fp8 inGlm 4 7 Fp8 out
Databricks$2.50/M$7.50/M——
Gmi——$0.400/M$2.00/M

Frequently asked questions

Databricks Grok 4 7 has a larger context window: 500K tokens vs 202K. 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.

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