Context Window

Compare

Databricks Gemma 3 12b vs Open Mistral Nemo

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

Databricks Gemma 3 12b

Context window

128K

128,000 tokens · ~96K words

Model page
Mistral

Model

Open Mistral Nemo

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.

Databricks Gemma 3 12b128K
Open Mistral Nemo128K

Same context window size for both models.

Token calculator →Memory savings →

Databricks Gemma 3 12b and Open Mistral Nemo have identical context windows (128K tokens). Databricks Gemma 3 12b is 50% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Databricks Gemma 3 12b. Input tokens are 50% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Open Mistral Nemo. 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 Gemma 3 12bOpen Mistral Nemo
Context window128,000 tokens (128K)128,000 tokens (128K)
Max output tokens32,000 tokens (32K)128,000 tokens (128K)
Speed tierBalancedBalanced
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.

ProviderDatabricks Gemma 3 12b inDatabricks Gemma 3 12b outOpen Mistral Nemo inOpen Mistral Nemo out
Databricks$0.150/M$0.500/M——
Mistral——$0.300/M$0.300/M

Frequently asked questions

Open Mistral Nemo has a larger context window: 128K 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.

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