Context Window

Compare

Claude Opus 4.5 (batch) vs Databricks Gpt 5 Mini

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.

Anthropic

Model

Claude Opus 4.5 (batch)

Image inputTool calling

Context window

200K

200,000 tokens · ~150K words

Model page
Openai

Model

Databricks Gpt 5 Mini

Context window

272K

272,000 tokens · ~204K 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.

Claude Opus 4.5 (batch)200K
Databricks Gpt 5 Mini272K

Databricks Gpt 5 Mini has about 1.4× the context window of the other in this pair.

Token calculator →Memory savings →

Databricks Gpt 5 Mini has 36% more context capacity (272K vs 200K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Databricks Gpt 5 Mini. Its 272K context fits entire documents without chunking (vs 200K).

  • Long output (reports, code files)

    Use Databricks Gpt 5 Mini. 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.

SpecClaude Opus 4.5 (batch)Databricks Gpt 5 Mini
Context window200,000 tokens (200K)272,000 tokens (272K)
Max output tokens64,000 tokens (64K)128,000 tokens (128K)
Speed tierDeepFast
VisionYesNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
Batch APIYesNo
Release dateNov 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.

ProviderClaude Opus 4.5 (batch) inClaude Opus 4.5 (batch) outDatabricks Gpt 5 Mini inDatabricks Gpt 5 Mini out
Databricks——$0.250/M$2.00/M

Frequently asked questions

Databricks Gpt 5 Mini has a larger context window: 272K tokens vs 200K. 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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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
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Key memories
Current turn

80% less to send — works with any model