Context Window

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Gemma 3 12B 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

Gemma 3 12B

Image inputTool calling

Context window

131K

131,072 tokens · ~98K 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.

Gemma 3 12B131K
Llama 3 1 8b Instant128K

Gemma 3 12B has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

Gemma 3 12B has 2% more context capacity (131K vs 128K tokens). Llama 3 1 8b Instant is 0% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Gemma 3 12B. Its 131K context fits entire documents without chunking (vs 128K).

  • Long output (reports, code files)

    Use Gemma 3 12B. Its 131K 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.

SpecGemma 3 12BLlama 3 1 8b Instant
Context window131,072 tokens (131K)128,000 tokens (128K)
Max output tokens131,072 tokens (131K)8,192 tokens (8K)
Speed tierBalancedFast
VisionYesNo
Function callingYesYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateMar 2025N/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.

ProviderGemma 3 12B inGemma 3 12B outLlama 3 1 8b Instant inLlama 3 1 8b Instant out
Deepinfra$0.050/M$0.100/M——
Groq——$0.050/M$0.080/M
Novita$0.050/M$0.100/M——

Frequently asked questions

Gemma 3 12B has a larger context window: 131K 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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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.

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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