Context Window

Compare

Fw Glm 5 1 vs GLM 5.3 FlashX

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

Fw Glm 5 1

Tool calling

Context window

203K

202,800 tokens · ~152K words

Model page
Z Ai

Model

GLM 5.3 FlashX

Image inputTool 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.

Fw Glm 5 1203K
GLM 5.3 FlashX1.0M

GLM 5.3 FlashX has about 5.2× the context window of the other in this pair.

Token calculator →Memory savings →

GLM 5.3 FlashX has 417% more context capacity (1048K vs 202K tokens). GLM 5.3 FlashX is 75% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use GLM 5.3 FlashX. Its 1048K context fits entire documents without chunking (vs 202K).

  • RAG / high-volume retrieval

    Use GLM 5.3 FlashX. Input tokens are 75% cheaper — critical when sending large retrieved contexts.

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecFw Glm 5 1GLM 5.3 FlashX
Context window202,800 tokens (202K)1,048,576 tokens (1048K)
Max output tokens131,072 tokens (131K)131,072 tokens (131K)
Speed tierBalancedFast
VisionNoYes
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesYes
Batch APINoNo
Release dateN/ASep 2026

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.

ProviderFw Glm 5 1 inFw Glm 5 1 outGLM 5.3 FlashX inGLM 5.3 FlashX out
Azure$1.54/M$4.84/M——
Openrouter——$0.370/M$1.25/M

Frequently asked questions

GLM 5.3 FlashX has a larger context window: 1048K 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.

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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