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Databricks Claude Opus 4 1 vs Fw Glm 5 2

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.

Anthropic

Model

Databricks Claude Opus 4 1

Tool calling

Context window

200K

200,000 tokens · ~150K words

Model page
Z Ai

Model

Fw Glm 5 2

Tool calling

Context window

1.0M

1,048,576 tokens · ~786K 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 Claude Opus 4 1200K
Fw Glm 5 21.0M

Fw Glm 5 2 has about 5.2× the context window of the other in this pair.

Fw Glm 5 2 has 424% more context capacity (1048K vs 200K tokens). Fw Glm 5 2 is 89% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Fw Glm 5 2. Its 1048K context fits entire documents without chunking (vs 200K).

  • RAG / high-volume retrieval

    Use Fw Glm 5 2. Input tokens are 89% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Fw 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 Claude Opus 4 1Fw Glm 5 2
Context window200,000 tokens (200K)1,048,576 tokens (1048K)
Max output tokens32,000 tokens (32K)131,072 tokens (131K)
Speed tierDeepBalanced
VisionNoNo
Function callingYesYes
Extended thinkingYesYes
Prompt cachingNoYes
Batch APIYesNo
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 Claude Opus 4 1 inDatabricks Claude Opus 4 1 outFw Glm 5 2 inFw Glm 5 2 out
Azure$1.54/M$4.84/M
Databricks$15.00/M$75.00/M

Frequently asked questions

Fw Glm 5 2 has a larger context window: 1048K 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.

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