Context Window

Compare

Gemini 3 8 Flash Cyber vs Hy4 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.

Google

Model

Gemini 3 8 Flash Cyber

Image input

Context window

1.0M

1,048,576 tokens · ~786K words

Model page
Tencent

Model

Hy4 preview

Tool calling

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.

Gemini 3 8 Flash Cyber1.0M
Hy4 preview1.0M

Same context window size for both models.

Token calculator →Memory savings →

Gemini 3 8 Flash Cyber and Hy4 preview have identical context windows (1048K tokens). Hy4 preview is 44% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Hy4 preview. Input tokens are 44% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Gemini 3 8 Flash Cyber. Its 65K 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.

SpecGemini 3 8 Flash CyberHy4 preview
Context window1,048,576 tokens (1048K)1,048,576 tokens (1048K)
Max output tokens65,536 tokens (65K)64,000 tokens (64K)
Speed tierFastBalanced
VisionYesNo
Function callingNoYes
Extended thinkingYesYes
Prompt cachingYesYes
Batch APINoNo
Release dateN/AAug 2026

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 3 8 Flash Cyber inGemini 3 8 Flash Cyber outHy4 preview inHy4 preview out
Google Vertex$1.50/M$7.50/M——
Openrouter——$0.834/M$2.50/M

Frequently asked questions

Hy4 preview has a larger context window: 1048K tokens vs 1048K. 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