Context Window

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GLM 5.3 FlashX vs Solar Pro 4

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

GLM 5.3 FlashX

Image inputTool calling

Context window

1.0M

1,048,576 tokens · ~786K words

Model page
Upstage

Model

Solar Pro 4

Tool calling

Context window

524K

524,288 tokens · ~393K 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.

GLM 5.3 FlashX1.0M
Solar Pro 4524K

GLM 5.3 FlashX has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

GLM 5.3 FlashX has 100% more context capacity (1048K vs 524K tokens). Solar Pro 4 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 524K).

  • RAG / high-volume retrieval

    Use Solar Pro 4. 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.

SpecGLM 5.3 FlashXSolar Pro 4
Context window1,048,576 tokens (1048K)524,288 tokens (524K)
Max output tokens131,072 tokens (131K)131,072 tokens (131K)
Speed tierFastBalanced
VisionYesNo
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesYes
Batch APINoNo
Release dateSep 2026Aug 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.

ProviderGLM 5.3 FlashX inGLM 5.3 FlashX outSolar Pro 4 inSolar Pro 4 out
Openrouter$0.370/M$1.25/M$0.090/M$0.360/M

Frequently asked questions

GLM 5.3 FlashX has a larger context window: 1048K tokens vs 524K. 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