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Databricks Gpt 5 4 vs Gemini Gemma 2 27b It

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.

Openai

Model

Databricks Gpt 5 4

Context window

272K

272,000 tokens · ~204K words

Model page
Google

Model

Gemini Gemma 2 27b It

Image inputTool calling

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.

Databricks Gpt 5 4272K
Gemini Gemma 2 27b It8K

Databricks Gpt 5 4 has about 33.2× the context window of the other in this pair.

Databricks Gpt 5 4 has 3220% more context capacity (272K vs 8K tokens). Gemini Gemma 2 27b It is 86% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Databricks Gpt 5 4. Its 272K context fits entire documents without chunking (vs 8K).

  • RAG / high-volume retrieval

    Use Gemini Gemma 2 27b It. Input tokens are 86% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Databricks Gpt 5 4. Its 128K 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.

SpecDatabricks Gpt 5 4Gemini Gemma 2 27b It
Context window272,000 tokens (272K)8,192 tokens (8K)
Max output tokens128,000 tokens (128K)8,192 tokens (8K)
Speed tierBalancedFast
VisionNoYes
Function callingNoYes
Extended thinkingYesNo
Prompt cachingYesNo
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.

ProviderDatabricks Gpt 5 4 inDatabricks Gpt 5 4 outGemini Gemma 2 27b It inGemini Gemma 2 27b It out
Databricks$2.50/M$15.00/M——
Google——$0.350/M$1.05/M

Frequently asked questions

Databricks Gpt 5 4 has a larger context window: 272K 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.

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