Context Window

Compare

Claude Opus 5.5 vs Glm 5p3 Fast

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.

Anthropic

Model

Claude Opus 5.5

Image inputTool calling

Context window

1M

1,000,000 tokens · ~750K words

Model page
Z Ai

Model

Glm 5p3 Fast

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.

Claude Opus 5.51M
Glm 5p3 Fast1.0M

Glm 5p3 Fast has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

Glm 5p3 Fast has 4% more context capacity (1048K vs 1000K tokens). Glm 5p3 Fast is 47% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Glm 5p3 Fast. Its 1048K context fits entire documents without chunking (vs 1000K).

  • RAG / high-volume retrieval

    Use Glm 5p3 Fast. Input tokens are 47% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecClaude Opus 5.5Glm 5p3 Fast
Context window1,000,000 tokens (1000K)1,048,576 tokens (1048K)
Max output tokens128,000 tokens (128K)N/A
Speed tierDeepBalanced
VisionYesNo
Function callingYesNo
Extended thinkingYesYes
Prompt cachingYesYes
Batch APIYesNo
Release dateSep 2026N/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.

ProviderClaude Opus 5.5 inClaude Opus 5.5 outGlm 5p3 Fast inGlm 5p3 Fast out
Anthropic$4.00/M$20.00/M——
Aws Bedrock$4.00/M$20.00/M——
Azure$4.00/M$20.00/M——
Fireworks——$2.10/M$6.60/M
Google Vertex$4.00/M$20.00/M——
Openrouter$4.00/M$20.00/M——

Frequently asked questions

Glm 5p3 Fast has a larger context window: 1048K tokens vs 1000K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

Powered by Mem0

Use a smaller model.
Get better results.

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.

Calculate your savingsLearn about Mem0

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