Context Window

Compare

Deepseek V2 Lite vs Meta Llama3 1 8b Instruct

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 V2 Lite

Context window

164K

163,840 tokens · ~123K words

Model page
Meta

Model

Meta Llama3 1 8b Instruct

Tool calling

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.

Deepseek V2 Lite164K
Meta Llama3 1 8b Instruct128K

Deepseek V2 Lite has about 1.3× the context window of the other in this pair.

Token calculator →Memory savings →

Deepseek V2 Lite has 28% more context capacity (163K vs 128K tokens). Meta Llama3 1 8b Instruct is 56% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Deepseek V2 Lite. Its 163K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Meta Llama3 1 8b Instruct. Input tokens are 56% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Deepseek V2 Lite. Its 163K 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 V2 LiteMeta Llama3 1 8b Instruct
Context window163,840 tokens (163K)128,000 tokens (128K)
Max output tokens163,840 tokens (163K)2,048 tokens (2K)
Speed tierBalancedFast
VisionNoNo
Function callingNoYes
Extended thinkingNoNo
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.

ProviderDeepseek V2 Lite inDeepseek V2 Lite outMeta Llama3 1 8b Instruct inMeta Llama3 1 8b Instruct out
Aws Bedrock——$0.220/M$0.220/M
Fireworks$0.500/M$0.500/M——

Frequently asked questions

Deepseek V2 Lite has a larger context window: 163K 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.

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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