Context Window

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Deepseek Reasoner vs Fw Kimi K3

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.

Deepseek

Model

Deepseek Reasoner

Context window

131K

131,072 tokens · ~98K words

Model page
Moonshot

Model

Fw Kimi K3

Image inputTool 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.

Deepseek Reasoner131K
Fw Kimi K31.0M

Fw Kimi K3 has about 8× the context window of the other in this pair.

Token calculator →Memory savings →

Fw Kimi K3 has 700% more context capacity (1048K vs 131K tokens). Deepseek Reasoner is 91% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Fw Kimi K3. Its 1048K context fits entire documents without chunking (vs 131K).

  • RAG / high-volume retrieval

    Use Deepseek Reasoner. Input tokens are 91% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Fw Kimi K3. Its 131K 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.

SpecDeepseek ReasonerFw Kimi K3
Context window131,072 tokens (131K)1,048,576 tokens (1048K)
Max output tokens65,536 tokens (65K)131,072 tokens (131K)
Speed tierBalancedBalanced
VisionNoYes
Function callingNoYes
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.

ProviderDeepseek Reasoner inDeepseek Reasoner outFw Kimi K3 inFw Kimi K3 out
Azure——$3.30/M$16.50/M
Deepseek$0.280/M$0.420/M——

Frequently asked questions

Fw Kimi K3 has a larger context window: 1048K tokens vs 131K. 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