Context Window

Compare

Fw Kimi K2 7 Code vs Hy-MT2-7B

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.

Moonshot

Model

Fw Kimi K2 7 Code

Image inputTool calling

Context window

262K

262,144 tokens · ~197K words

Model page
Tencent

Model

Hy-MT2-7B

Context window

8K

8,192 tokens · ~6K 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.

Fw Kimi K2 7 Code262K
Hy-MT2-7B8K

Fw Kimi K2 7 Code has about 32× the context window of the other in this pair.

Token calculator →Memory savings →

Fw Kimi K2 7 Code has 3100% more context capacity (262K vs 8K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Fw Kimi K2 7 Code. Its 262K context fits entire documents without chunking (vs 8K).

  • Long output (reports, code files)

    Use Fw Kimi K2 7 Code. Its 262K 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.

SpecFw Kimi K2 7 CodeHy-MT2-7B
Context window262,144 tokens (262K)8,192 tokens (8K)
Max output tokens262,144 tokens (262K)4,096 tokens (4K)
Speed tierBalancedFast
VisionYesNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
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.

ProviderFw Kimi K2 7 Code inFw Kimi K2 7 Code outHy-MT2-7B inHy-MT2-7B out
Azure$1.05/M$4.40/M——

Frequently asked questions

Fw Kimi K2 7 Code has a larger context window: 262K tokens vs 8K. 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