Context Window

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DeepSeek V3 0324 vs Moonshot V1 8k 0430

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 V3 0324

Tool calling

Context window

66K

65,536 tokens · ~49K words

Model page
Moonshot

Model

Moonshot V1 8k 0430

Tool calling

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.

DeepSeek V3 032466K
Moonshot V1 8k 04308K

DeepSeek V3 0324 has about 8× the context window of the other in this pair.

Token calculator →Memory savings →

DeepSeek V3 0324 has 700% more context capacity (65K vs 8K tokens). DeepSeek V3 0324 is 30% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use DeepSeek V3 0324. Its 65K context fits entire documents without chunking (vs 8K).

  • RAG / high-volume retrieval

    Use DeepSeek V3 0324. Input tokens are 30% 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 V3 0324Moonshot V1 8k 0430
Context window65,536 tokens (65K)8,192 tokens (8K)
Max output tokens8,192 tokens (8K)8,192 tokens (8K)
Speed tierBalancedBalanced
VisionNoNo
Function callingYesYes
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateMar 2025N/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 V3 0324 inDeepSeek V3 0324 outMoonshot V1 8k 0430 inMoonshot V1 8k 0430 out
Moonshot——$0.200/M$2.00/M
Openrouter$0.140/M$0.280/M——

Frequently asked questions

DeepSeek V3 0324 has a larger context window: 65K 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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Use a smaller model.
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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