Context Window

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Databricks Gpt 5 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.

Openai

Model

Databricks Gpt 5 1

Context window

272K

272,000 tokens · ~204K 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 Gpt 5 1272K
Fw Glm 5 21.0M

Fw Glm 5 2 has about 3.9× the context window of the other in this pair.

Token calculator →Memory savings →

Fw Glm 5 2 has 285% more context capacity (1048K vs 272K tokens). Databricks Gpt 5 1 is 18% 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 272K).

  • RAG / high-volume retrieval

    Use Databricks Gpt 5 1. Input tokens are 18% 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 Gpt 5 1Fw Glm 5 2
Context window272,000 tokens (272K)1,048,576 tokens (1048K)
Max output tokens128,000 tokens (128K)131,072 tokens (131K)
Speed tierBalancedBalanced
VisionNoNo
Function callingNoYes
Extended thinkingNoYes
Prompt cachingNoYes
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 Gpt 5 1 inDatabricks Gpt 5 1 outFw Glm 5 2 inFw Glm 5 2 out
Azure——$1.54/M$4.84/M
Databricks$1.25/M$10.00/M——

Frequently asked questions

Fw Glm 5 2 has a larger context window: 1048K tokens vs 272K. 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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