Context Window

Compare

Glm 5p1 vs GPT-5.2-Codex

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 5p1

Context window

203K

202,800 tokens · ~152K words

Model page
Openai

Model

GPT-5.2-Codex

Image inputTool calling

Context window

272K

272,000 tokens · ~204K 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 5p1203K
GPT-5.2-Codex272K

GPT-5.2-Codex has about 1.3× the context window of the other in this pair.

Token calculator →Memory savings →

GPT-5.2-Codex has 34% more context capacity (272K vs 202K tokens). Glm 5p1 is 20% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use GPT-5.2-Codex. Its 272K context fits entire documents without chunking (vs 202K).

  • RAG / high-volume retrieval

    Use Glm 5p1. Input tokens are 20% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Glm 5p1. Its 202K 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.

SpecGlm 5p1GPT-5.2-Codex
Context window202,800 tokens (202K)272,000 tokens (272K)
Max output tokens202,800 tokens (202K)128,000 tokens (128K)
Speed tierBalancedBalanced
VisionNoYes
Function callingNoYes
Extended thinkingYesYes
Prompt cachingYesYes
Batch APINoNo
Release dateN/AJan 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 5p1 inGlm 5p1 outGPT-5.2-Codex inGPT-5.2-Codex out
Fireworks$1.40/M$4.40/M——
Openrouter——$1.75/M$14.00/M

Frequently asked questions

GPT-5.2-Codex has a larger context window: 272K 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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Example: a multi-turn chat session

Without Mem0~128K tokens sent
Full history
Repeated info
Old context
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