Context Window

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Databricks Meta Llama 3 1 8b vs Openai Gpt 4o 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.

Meta

Model

Databricks Meta Llama 3 1 8b

Context window

200K

200,000 tokens · ~150K words

Model page
Openai

Model

Openai Gpt 4o Mini

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 Meta Llama 3 1 8b200K
Openai Gpt 4o Mini128K

Databricks Meta Llama 3 1 8b has about 1.6× the context window of the other in this pair.

Token calculator →Memory savings →

Databricks Meta Llama 3 1 8b has 56% more context capacity (200K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Databricks Meta Llama 3 1 8b. Its 200K context fits entire documents without chunking (vs 128K).

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecDatabricks Meta Llama 3 1 8bOpenai Gpt 4o Mini
Context window200,000 tokens (200K)128,000 tokens (128K)
Max output tokens128,000 tokens (128K)N/A
Speed tierFastFast
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 Meta Llama 3 1 8b inDatabricks Meta Llama 3 1 8b outOpenai Gpt 4o Mini inOpenai Gpt 4o Mini out
Databricks$0.150/M$0.450/M——
Gradient————

Frequently asked questions

Databricks Meta Llama 3 1 8b has a larger context window: 200K 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.

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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