Context Window

Compare

Glm 4 7 Fp8 vs Gpt 4o Mini Audio Preview

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 4 7 Fp8

Context window

203K

202,752 tokens · ~152K words

Model page
Openai

Model

Gpt 4o Mini Audio Preview

Tool calling

Context window

128K

128,000 tokens · ~96K 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 4 7 Fp8203K
Gpt 4o Mini Audio Preview128K

Glm 4 7 Fp8 has about 1.6× the context window of the other in this pair.

Token calculator →Memory savings →

Glm 4 7 Fp8 has 58% more context capacity (202K vs 128K tokens). Gpt 4o Mini Audio Preview is 62% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Glm 4 7 Fp8. Its 202K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Gpt 4o Mini Audio Preview. Input tokens are 62% 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 4 7 Fp8Gpt 4o Mini Audio Preview
Context window202,752 tokens (202K)128,000 tokens (128K)
Max output tokens16,384 tokens (16K)16,384 tokens (16K)
Speed tierBalancedFast
VisionNoNo
Function callingNoYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoYes
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.

ProviderGlm 4 7 Fp8 inGlm 4 7 Fp8 outGpt 4o Mini Audio Preview inGpt 4o Mini Audio Preview out
Gmi$0.400/M$2.00/M——
Openai——$0.150/M$0.600/M

Frequently asked questions

Glm 4 7 Fp8 has a larger context window: 202K tokens vs 128K. 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