Context Window

Compare

Anthropic Claude vs Glm 4 7 Fp8

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 4 7 Fp8

Context window

203K

202,752 tokens · ~152K 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 4 7 Fp8203K

Glm 4 7 Fp8 has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Glm 4 7 Fp8 has 102% more context capacity (202K vs 100K tokens). Glm 4 7 Fp8 is 95% 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 100K).

  • RAG / high-volume retrieval

    Use Glm 4 7 Fp8. Input tokens are 95% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Glm 4 7 Fp8. Its 16K 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 4 7 Fp8
Context window100,000 tokens (100K)202,752 tokens (202K)
Max output tokens8,191 tokens (8K)16,384 tokens (16K)
Speed tierBalancedBalanced
VisionNoNo
Function callingNoNo
Extended thinkingNoNo
Prompt cachingNoNo
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 4 7 Fp8 inGlm 4 7 Fp8 out
Aws Bedrock$8.00/M$24.00/M——
Gmi——$0.400/M$2.00/M

Frequently asked questions

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

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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