Context Window

Compare

Glm 5 1 Fp8 vs Kimi K2 0711 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 5 1 Fp8

Context window

203K

202,752 tokens · ~152K words

Model page
Moonshot

Model

Kimi K2 0711 Preview

Tool calling

Context window

131K

131,072 tokens · ~98K 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 5 1 Fp8203K
Kimi K2 0711 Preview131K

Glm 5 1 Fp8 has about 1.5× the context window of the other in this pair.

Token calculator →Memory savings →

Glm 5 1 Fp8 has 54% more context capacity (202K vs 131K tokens). Kimi K2 0711 Preview is 57% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Glm 5 1 Fp8. Its 202K context fits entire documents without chunking (vs 131K).

  • RAG / high-volume retrieval

    Use Kimi K2 0711 Preview. Input tokens are 57% 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 5 1 Fp8Kimi K2 0711 Preview
Context window202,752 tokens (202K)131,072 tokens (131K)
Max output tokensN/A131,072 tokens (131K)
Speed tierBalancedBalanced
VisionNoNo
Function callingNoYes
Extended thinkingNoNo
Prompt cachingYesYes
Batch APINoNo
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 5 1 Fp8 inGlm 5 1 Fp8 outKimi K2 0711 Preview inKimi K2 0711 Preview out
Gmi$1.40/M$4.40/M——
Moonshot——$0.600/M$2.50/M

Frequently asked questions

Glm 5 1 Fp8 has a larger context window: 202K tokens vs 131K. 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