Context Window

Compare

Hy4 preview vs Jp Anthropic Claude Sonnet 4 6

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.

Tencent

Model

Hy4 preview

Tool calling

Context window

1.0M

1,048,576 tokens · ~786K words

Model page
Anthropic

Model

Jp Anthropic Claude Sonnet 4 6

Image inputTool calling

Context window

1M

1,000,000 tokens · ~750K 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.

Hy4 preview1.0M
Jp Anthropic Claude Sonnet 4 61M

Hy4 preview has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

Hy4 preview has 4% more context capacity (1048K vs 1000K tokens). Hy4 preview is 74% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Hy4 preview. Its 1048K context fits entire documents without chunking (vs 1000K).

  • RAG / high-volume retrieval

    Use Hy4 preview. Input tokens are 74% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecHy4 previewJp Anthropic Claude Sonnet 4 6
Context window1,048,576 tokens (1048K)1,000,000 tokens (1000K)
Max output tokens64,000 tokens (64K)64,000 tokens (64K)
Speed tierBalancedBalanced
VisionNoYes
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesYes
Batch APINoYes
Release dateAug 2026N/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.

ProviderHy4 preview inHy4 preview outJp Anthropic Claude Sonnet 4 6 inJp Anthropic Claude Sonnet 4 6 out
Aws Bedrock——$3.30/M$16.50/M
Openrouter$0.834/M$2.50/M——

Frequently asked questions

Hy4 preview has a larger context window: 1048K tokens vs 1000K. 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