Context Window

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Gemini 2.5 Flash vs Gemma 3 4b It Gguf

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 2.5 Flash

Image inputTool calling

Context window

1M

1,000,000 tokens · ~750K words

Model page
Google

Model

Gemma 3 4b It Gguf

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 2.5 Flash1M
Gemma 3 4b It Gguf128K

Gemini 2.5 Flash has about 7.8× the context window of the other in this pair.

Token calculator →Memory savings →

Gemini 2.5 Flash has 681% more context capacity (1000K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Gemini 2.5 Flash. Its 1000K context fits entire documents without chunking (vs 128K).

  • Long output (reports, code files)

    Use Gemini 2.5 Flash. Its 1000K 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.

SpecGemini 2.5 FlashGemma 3 4b It Gguf
Context window1,000,000 tokens (1000K)128,000 tokens (128K)
Max output tokens1,000,000 tokens (1000K)8,192 tokens (8K)
Speed tierFastBalanced
VisionYesNo
Function callingYesYes
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateJun 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.

ProviderGemini 2.5 Flash inGemini 2.5 Flash outGemma 3 4b It Gguf inGemma 3 4b It Gguf out
Deepinfra$0.300/M$2.50/M——
Google$0.300/M$2.50/M——
Google Vertex$0.300/M$2.50/M——
Lemonade————
Openrouter$0.300/M$2.50/M——
Replicate$2.50/M$2.50/M——

Frequently asked questions

Gemini 2.5 Flash has a larger context window: 1000K 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.
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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