Context Window

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Gemini 2.0 Flash vs Glm 5 1 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.

Google

Model

Gemini 2.0 Flash

Image inputTool calling

Context window

1M

1,000,000 tokens · ~750K words

Model page
Z Ai

Model

Glm 5 1 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.

Gemini 2.0 Flash1M
Glm 5 1 Fp8203K

Gemini 2.0 Flash has about 4.9× the context window of the other in this pair.

Token calculator →Memory savings →

Gemini 2.0 Flash has 393% more context capacity (1000K vs 202K tokens). Gemini 2.0 Flash is 92% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Gemini 2.0 Flash. Its 1000K context fits entire documents without chunking (vs 202K).

  • RAG / high-volume retrieval

    Use Gemini 2.0 Flash. Input tokens are 92% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecGemini 2.0 FlashGlm 5 1 Fp8
Context window1,000,000 tokens (1000K)202,752 tokens (202K)
Max output tokens1,000,000 tokens (1000K)N/A
Speed tierFastBalanced
VisionYesNo
Function callingYesNo
Extended thinkingNoNo
Prompt cachingYesYes
Batch APINoNo
Release dateFeb 2025N/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.

ProviderGemini 2.0 Flash inGemini 2.0 Flash outGlm 5 1 Fp8 inGlm 5 1 Fp8 out
Deepinfra$0.100/M$0.400/M——
Gmi——$1.40/M$4.40/M
Google$0.100/M$0.400/M——
Google Vertex$0.150/M$0.600/M——
Openrouter$0.100/M$0.400/M——

Frequently asked questions

Gemini 2.0 Flash has a larger context window: 1000K 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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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