Context Window

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Gemini Exp 1206 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.

Google

Model

Gemini Exp 1206

Image inputTool calling

Context window

2.1M

2,097,152 tokens · ~1.6M 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.

Gemini Exp 12062.1M
Llama 3 1 8b Instant128K

Gemini Exp 1206 has about 16.4× the context window of the other in this pair.

Token calculator →Memory savings →

Gemini Exp 1206 has 1538% more context capacity (2097K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Gemini Exp 1206. Its 2097K context fits entire documents without chunking (vs 128K).

Full specs

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

SpecGemini Exp 1206Llama 3 1 8b Instant
Context window2,097,152 tokens (2097K)128,000 tokens (128K)
Max output tokens8,192 tokens (8K)8,192 tokens (8K)
Speed tierFastFast
VisionYesNo
Function callingYesYes
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.

ProviderGemini Exp 1206 inGemini Exp 1206 outLlama 3 1 8b Instant inLlama 3 1 8b Instant out
Google————
Groq——$0.050/M$0.080/M

Frequently asked questions

Gemini Exp 1206 has a larger context window: 2097K 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