Context Window

Compare

Anthropic Claude vs Glm 5 Code

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

Anthropic Claude

Context window

100K

100,000 tokens · ~75K words

Model page
Z Ai

Model

Glm 5 Code

Tool calling

Context window

200K

200,000 tokens · ~150K 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.

Anthropic Claude100K
Glm 5 Code200K

Glm 5 Code has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Glm 5 Code has 100% more context capacity (200K vs 100K tokens). Glm 5 Code is 85% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Glm 5 Code. Its 200K context fits entire documents without chunking (vs 100K).

  • RAG / high-volume retrieval

    Use Glm 5 Code. Input tokens are 85% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Glm 5 Code. Its 128K 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.

SpecAnthropic ClaudeGlm 5 Code
Context window100,000 tokens (100K)200,000 tokens (200K)
Max output tokens8,191 tokens (8K)128,000 tokens (128K)
Speed tierBalancedBalanced
VisionNoNo
Function callingNoYes
Extended thinkingNoYes
Prompt cachingNoYes
Batch APIYesNo
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.

ProviderAnthropic Claude inAnthropic Claude outGlm 5 Code inGlm 5 Code out
Aws Bedrock$8.00/M$24.00/M——
Z Ai——$1.20/M$5.00/M

Frequently asked questions

Glm 5 Code has a larger context window: 200K tokens vs 100K. 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