Context Window

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Deepseek R1 Distill Qwen 7b vs Meta Llama3 2 11b 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.

Alibaba

Model

Deepseek R1 Distill Qwen 7b

Context window

131K

131,072 tokens · ~98K words

Model page
Meta

Model

Meta Llama3 2 11b Instruct

Image inputTool 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 R1 Distill Qwen 7b131K
Meta Llama3 2 11b Instruct128K

Deepseek R1 Distill Qwen 7b has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

Deepseek R1 Distill Qwen 7b has 2% more context capacity (131K vs 128K tokens). Deepseek R1 Distill Qwen 7b is 42% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Deepseek R1 Distill Qwen 7b. Its 131K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Deepseek R1 Distill Qwen 7b. Input tokens are 42% 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 R1 Distill Qwen 7bMeta Llama3 2 11b Instruct
Context window131,072 tokens (131K)128,000 tokens (128K)
Max output tokensN/A4,096 tokens (4K)
Speed tierFastFast
VisionNoYes
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 R1 Distill Qwen 7b inDeepseek R1 Distill Qwen 7b outMeta Llama3 2 11b Instruct inMeta Llama3 2 11b Instruct out
Aws Bedrock——$0.350/M$0.350/M
Fireworks$0.200/M$0.200/M——
Nscale$0.200/M$0.200/M——

Frequently asked questions

Deepseek R1 Distill Qwen 7b has a larger context window: 131K 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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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