Context Window

Compare

Deepseek V3 2 251201 vs Llama V3p1 70b Instruct 1b

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 2 251201

Tool calling

Context window

98K

98,304 tokens · ~74K words

Model page
Meta

Model

Llama V3p1 70b Instruct 1b

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 2 25120198K
Llama V3p1 70b Instruct 1b4K

Deepseek V3 2 251201 has about 24× the context window of the other in this pair.

Token calculator →Memory savings →

Deepseek V3 2 251201 has 2300% more context capacity (98K vs 4K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Deepseek V3 2 251201. Its 98K context fits entire documents without chunking (vs 4K).

  • Long output (reports, code files)

    Use Deepseek V3 2 251201. Its 32K 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 V3 2 251201Llama V3p1 70b Instruct 1b
Context window98,304 tokens (98K)4,096 tokens (4K)
Max output tokens32,768 tokens (32K)4,096 tokens (4K)
Speed tierBalancedFast
VisionNoNo
Function callingYesNo
Extended thinkingYesNo
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 V3 2 251201 inDeepseek V3 2 251201 outLlama V3p1 70b Instruct 1b inLlama V3p1 70b Instruct 1b out
Fireworks——$0.100/M$0.100/M
Volcengine————

Frequently asked questions

Deepseek V3 2 251201 has a larger context window: 98K 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.

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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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