Context Window

Compare

Databricks Glm 5 2 vs Jp Anthropic Claude Sonnet 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

Databricks Glm 5 2

Tool calling

Context window

1M

1,000,000 tokens · ~750K words

Model page
Anthropic

Model

Jp Anthropic Claude Sonnet 4 6

Image inputTool calling

Context window

1M

1,000,000 tokens · ~750K 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 Glm 5 21M
Jp Anthropic Claude Sonnet 4 61M

Same context window size for both models.

Token calculator →Memory savings →

Databricks Glm 5 2 and Jp Anthropic Claude Sonnet 4 6 have identical context windows (1000K tokens). Databricks Glm 5 2 is 57% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Databricks Glm 5 2. Input tokens are 57% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Databricks Glm 5 2. 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.

SpecDatabricks Glm 5 2Jp Anthropic Claude Sonnet 4 6
Context window1,000,000 tokens (1000K)1,000,000 tokens (1000K)
Max output tokens131,072 tokens (131K)64,000 tokens (64K)
Speed tierBalancedBalanced
VisionNoYes
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesYes
Batch APINoYes
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 Glm 5 2 inDatabricks Glm 5 2 outJp Anthropic Claude Sonnet 4 6 inJp Anthropic Claude Sonnet 4 6 out
Aws Bedrock——$3.30/M$16.50/M
Databricks$1.40/M$4.40/M——

Frequently asked questions

Jp Anthropic Claude Sonnet 4 6 has a larger context window: 1000K tokens vs 1000K. 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