Context Window

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Llama 3 1 8b Instant vs Sonar Pro Search

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

Llama 3 1 8b Instant

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Perplexity

Model

Sonar Pro Search

Image input

Context window

200K

200,000 tokens · ~150K 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.

Llama 3 1 8b Instant128K
Sonar Pro Search200K

Sonar Pro Search has about 1.6× the context window of the other in this pair.

Token calculator →Memory savings →

Sonar Pro Search has 56% more context capacity (200K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Sonar Pro Search. Its 200K context fits entire documents without chunking (vs 128K).

  • Long output (reports, code files)

    Use Llama 3 1 8b Instant. Its 8K 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.

SpecLlama 3 1 8b InstantSonar Pro Search
Context window128,000 tokens (128K)200,000 tokens (200K)
Max output tokens8,192 tokens (8K)8,000 tokens (8K)
Speed tierFastBalanced
VisionNoYes
Function callingYesNo
Extended thinkingNoYes
Prompt cachingNoNo
Batch APINoNo
Release dateN/AOct 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.

ProviderLlama 3 1 8b Instant inLlama 3 1 8b Instant outSonar Pro Search inSonar Pro Search out
Groq$0.050/M$0.080/M——

Frequently asked questions

Sonar Pro Search has a larger context window: 200K 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.
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.

Calculate your savingsLearn about Mem0

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