Context Window

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DeepSeek V4 Flash 0731 (batch) vs Moonshotai 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 V4 Flash 0731 (batch)

Tool calling

Context window

1.0M

1,048,576 tokens · ~786K words

Model page
Moonshot

Model

Moonshotai Kimi K3

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.

DeepSeek V4 Flash 0731 (batch)1.0M
Moonshotai Kimi K31M

DeepSeek V4 Flash 0731 (batch) has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

DeepSeek V4 Flash 0731 (batch) has 4% more context capacity (1048K vs 1000K tokens). DeepSeek V4 Flash 0731 (batch) is 96% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use DeepSeek V4 Flash 0731 (batch). Its 1048K context fits entire documents without chunking (vs 1000K).

  • RAG / high-volume retrieval

    Use DeepSeek V4 Flash 0731 (batch). Input tokens are 96% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecDeepSeek V4 Flash 0731 (batch)Moonshotai Kimi K3
Context window1,048,576 tokens (1048K)1,000,000 tokens (1000K)
Max output tokens943,718 tokens (943K)N/A
Speed tierFastBalanced
VisionNoYes
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesYes
Batch APINoNo
Release dateJul 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.

ProviderDeepSeek V4 Flash 0731 (batch) inDeepSeek V4 Flash 0731 (batch) outMoonshotai Kimi K3 inMoonshotai Kimi K3 out
Aws Bedrock——$3.00/M$15.00/M
Openrouter$0.110/M$0.330/M——

Frequently asked questions

DeepSeek V4 Flash 0731 (batch) 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.

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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
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Current turn

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