Context Window

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DeepSeek V3 0324 vs Llama 3 1 405b

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
Meta

Model

Llama 3 1 405b

Context window

4K

4,096 tokens · ~3K 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
Llama 3 1 405b4K

DeepSeek V3 0324 has about 16× the context window of the other in this pair.

Token calculator →Memory savings →

DeepSeek V3 0324 has 1500% more context capacity (65K vs 4K tokens). DeepSeek V3 0324 is 92% 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 4K).

  • RAG / high-volume retrieval

    Use DeepSeek V3 0324. Input tokens are 92% 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 0324Llama 3 1 405b
Context window65,536 tokens (65K)4,096 tokens (4K)
Max output tokens8,192 tokens (8K)N/A
Speed tierBalancedDeep
VisionNoNo
Function callingYesNo
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 outLlama 3 1 405b inLlama 3 1 405b out
Openrouter$0.250/M$1.00/M——
Together Ai——$3.50/M$3.50/M

Frequently asked questions

DeepSeek V3 0324 has a larger context window: 65K tokens vs 4K. 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.

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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