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Claude 3 5 Sonnet vs Llama 3 1 8b Instant

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.

Anthropic

Model

Claude 3 5 Sonnet

Context window

200K

200,000 tokens · ~150K words

Model page
Meta

Model

Llama 3 1 8b Instant

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.

Claude 3 5 Sonnet200K
Llama 3 1 8b Instant128K

Claude 3 5 Sonnet has about 1.6× the context window of the other in this pair.

Claude 3 5 Sonnet has 56% more context capacity (200K vs 128K tokens). Llama 3 1 8b Instant is 98% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Claude 3 5 Sonnet. Its 200K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Llama 3 1 8b Instant. Input tokens are 98% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecClaude 3 5 SonnetLlama 3 1 8b Instant
Context window200,000 tokens (200K)128,000 tokens (128K)
Max output tokensN/A8,192 tokens (8K)
Speed tierBalancedFast
VisionNoNo
Function callingNoYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APIYesNo
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.

ProviderClaude 3 5 Sonnet inClaude 3 5 Sonnet outLlama 3 1 8b Instant inLlama 3 1 8b Instant out
Google Vertex$3.00/M$15.00/M
Gradient$3.00/M$15.00/M
Groq$0.050/M$0.080/M
Openrouter$3.00/M$15.00/M
Replicate$3.75/M$18.75/M
Snowflake

Frequently asked questions

Claude 3 5 Sonnet 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.

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