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Databricks Gpt 5 1 vs Ft:gpt 3 5

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 1

Context window

272K

272,000 tokens · ~204K words

Model page
Openai

Model

Ft:gpt 3 5

Context window

16K

16,385 tokens · ~12K 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 1272K
Ft:gpt 3 516K

Databricks Gpt 5 1 has about 16.6× the context window of the other in this pair.

Databricks Gpt 5 1 has 1560% more context capacity (272K vs 16K tokens). Databricks Gpt 5 1 is 58% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Databricks Gpt 5 1. Its 272K context fits entire documents without chunking (vs 16K).

  • RAG / high-volume retrieval

    Use Databricks Gpt 5 1. Input tokens are 58% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Databricks Gpt 5 1. 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 1Ft:gpt 3 5
Context window272,000 tokens (272K)16,385 tokens (16K)
Max output tokens128,000 tokens (128K)4,096 tokens (4K)
Speed tierBalancedBalanced
VisionNoNo
Function callingNoNo
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoYes
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 1 inDatabricks Gpt 5 1 outFt:gpt 3 5 inFt:gpt 3 5 out
Databricks$1.25/M$10.00/M
Openai$3.00/M$6.00/M

Frequently asked questions

Databricks Gpt 5 1 has a larger context window: 272K tokens vs 16K. 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