Context Window

Compare

Chat Bison vs Meta Llama3 70b 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.

Google

Model

Chat Bison

Context window

8K

8,192 tokens · ~6K words

Model page
Meta

Model

Meta Llama3 70b Instruct

Context window

8K

8,192 tokens · ~6K 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.

Chat Bison8K
Meta Llama3 70b Instruct8K

Same context window size for both models.

Token calculator →Memory savings →

Chat Bison and Meta Llama3 70b Instruct have identical context windows (8K tokens). Chat Bison is 96% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Chat Bison. Input tokens are 96% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Meta Llama3 70b Instruct. 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.

SpecChat BisonMeta Llama3 70b Instruct
Context window8,192 tokens (8K)8,192 tokens (8K)
Max output tokens4,096 tokens (4K)8,192 tokens (8K)
Speed tierBalancedDeep
VisionNoNo
Function callingNoNo
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.

ProviderChat Bison inChat Bison outMeta Llama3 70b Instruct inMeta Llama3 70b Instruct out
Aws Bedrock——$3.18/M$4.20/M
Google$0.125/M$0.125/M——

Frequently asked questions

Meta Llama3 70b Instruct has a larger context window: 8K tokens vs 8K. 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.

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