Context Window

Compare

Databricks Gpt 5 Nano vs Trinity Large Thinking (free)

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 Nano

Context window

272K

272,000 tokens · ~204K words

Model page
Arcee Ai

Model

Trinity Large Thinking (free)

Tool calling

Context window

262K

262,144 tokens · ~197K 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 Nano272K
Trinity Large Thinking (free)262K

Databricks Gpt 5 Nano has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

Databricks Gpt 5 Nano has 3% more context capacity (272K vs 262K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Databricks Gpt 5 Nano. Its 272K context fits entire documents without chunking (vs 262K).

  • Long output (reports, code files)

    Use Databricks Gpt 5 Nano. 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 NanoTrinity Large Thinking (free)
Context window272,000 tokens (272K)262,144 tokens (262K)
Max output tokens128,000 tokens (128K)80,000 tokens (80K)
Speed tierFastDeep
VisionNoNo
Function callingNoYes
Extended thinkingNoYes
Prompt cachingNoNo
Batch APINoNo
Release dateN/AApr 2026

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 Nano inDatabricks Gpt 5 Nano outTrinity Large Thinking (free) inTrinity Large Thinking (free) out
Databricks$0.050/M$0.400/M——

Frequently asked questions

Databricks Gpt 5 Nano has a larger context window: 272K tokens vs 262K. 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