Context Window

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Moonshot Kimi K2 Thinking vs Phi 3 Medium 128k

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

Moonshot Kimi K2 Thinking

Context window

128K

128,000 tokens · ~96K words

Model page
Microsoft

Model

Phi 3 Medium 128k

Context window

128K

128,000 tokens · ~96K 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.

Moonshot Kimi K2 Thinking128K
Phi 3 Medium 128k128K

Same context window size for both models.

Token calculator →Memory savings →

Moonshot Kimi K2 Thinking and Phi 3 Medium 128k have identical context windows (128K tokens). Phi 3 Medium 128k is 71% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Phi 3 Medium 128k. Input tokens are 71% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Moonshot Kimi K2 Thinking. Its 8K 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.

SpecMoonshot Kimi K2 ThinkingPhi 3 Medium 128k
Context window128,000 tokens (128K)128,000 tokens (128K)
Max output tokens8,192 tokens (8K)4,096 tokens (4K)
Speed tierDeepBalanced
VisionNoNo
Function callingNoNo
Extended thinkingYesNo
Prompt cachingNoNo
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.

ProviderMoonshot Kimi K2 Thinking inMoonshot Kimi K2 Thinking outPhi 3 Medium 128k inPhi 3 Medium 128k out
Aws Bedrock$0.600/M$2.50/M——
Azure——$0.170/M$0.680/M

Frequently asked questions

Phi 3 Medium 128k has a larger context window: 128K tokens vs 128K. 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