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Llama3 2 1b vs Sonar

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.

Meta

Model

Llama3 2 1b

Context window

128K

128,000 tokens · ~96K words

Model page
Perplexity

Model

Sonar

Image input

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.

Llama3 2 1b128K
Sonar128K

Same context window size for both models.

Llama3 2 1b and Sonar have identical context windows (128K tokens).

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecLlama3 2 1bSonar
Context window128,000 tokens (128K)128,000 tokens (128K)
Max output tokens8,192 tokens (8K)N/A
Speed tierFastBalanced
VisionNoYes
Function callingNoNo
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateN/AJan 2025

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.

ProviderLlama3 2 1b inLlama3 2 1b outSonar inSonar out
Perplexity$1.00/M$1.00/M
Snowflake

Frequently asked questions

Sonar has a larger context window: 128K 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.

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.

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