Context Window

Compare

Aion-RP 1.0 (8B) vs GLM Latest

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.

Meta

Model

Aion-RP 1.0 (8B)

Context window

33K

32,768 tokens · ~25K words

Model page
Z Ai

Model

GLM Latest

Tool calling

Context window

1.3M

1,310,720 tokens · ~983K 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.

Aion-RP 1.0 (8B)33K
GLM Latest1.3M

GLM Latest has about 40× the context window of the other in this pair.

Token calculator →Memory savings →

GLM Latest has 3900% more context capacity (1310K vs 32K tokens). Aion-RP 1.0 (8B) is 5% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use GLM Latest. Its 1310K context fits entire documents without chunking (vs 32K).

  • RAG / high-volume retrieval

    Use Aion-RP 1.0 (8B). Input tokens are 5% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use GLM Latest. Its 235K 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.

SpecAion-RP 1.0 (8B)GLM Latest
Context window32,768 tokens (32K)1,310,720 tokens (1310K)
Max output tokens29,491 tokens (29K)235,929 tokens (235K)
Speed tierFastBalanced
VisionNoNo
Function callingNoYes
Extended thinkingNoYes
Prompt cachingNoYes
Batch APINoNo
Release dateFeb 2025Aug 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.

ProviderAion-RP 1.0 (8B) inAion-RP 1.0 (8B) outGLM Latest inGLM Latest out
Openrouter$0.800/M$1.60/M$0.844/M$2.65/M

Frequently asked questions

GLM Latest has a larger context window: 1310K tokens vs 32K. 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