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In Moonshotai Kimi K3 1 vs Moonshotai Kimi K3 1

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

In Moonshotai Kimi K3 1

Image inputTool calling

Context window

869K

868,928 tokens · ~652K words

Model page
Moonshot

Model

Moonshotai Kimi K3 1

Image inputTool calling

Context window

869K

868,928 tokens · ~652K 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.

In Moonshotai Kimi K3 1869K
Moonshotai Kimi K3 1869K

Same context window size for both models.

In Moonshotai Kimi K3 1 and Moonshotai Kimi K3 1 have identical context windows (868K tokens). Moonshotai Kimi K3 1 is 0% cheaper on input.

Full specs

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

SpecIn Moonshotai Kimi K3 1Moonshotai Kimi K3 1
Context window868,928 tokens (868K)868,928 tokens (868K)
Max output tokens131,072 tokens (131K)131,072 tokens (131K)
Speed tierBalancedBalanced
VisionYesYes
Function callingYesYes
Extended thinkingYesYes
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.

ProviderIn Moonshotai Kimi K3 1 inIn Moonshotai Kimi K3 1 outMoonshotai Kimi K3 1 inMoonshotai Kimi K3 1 out
Aws Bedrock$4.40/M$22.00/M$4.40/M$22.00/M

Frequently asked questions

Moonshotai Kimi K3 1 has a larger context window: 868K tokens vs 868K. 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.

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