Context Window

Compare

Llama 3 1 405b vs O1 2024 12 17

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

Llama 3 1 405b

Context window

4K

4,096 tokens · ~3K words

Model page
Openai

Model

O1 2024 12 17

Image inputTool calling

Context window

200K

200,000 tokens · ~150K 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.

Llama 3 1 405b4K
O1 2024 12 17200K

O1 2024 12 17 has about 48.8× the context window of the other in this pair.

Token calculator →Memory savings →

O1 2024 12 17 has 4782% more context capacity (200K vs 4K tokens). Llama 3 1 405b is 76% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use O1 2024 12 17. Its 200K context fits entire documents without chunking (vs 4K).

  • RAG / high-volume retrieval

    Use Llama 3 1 405b. Input tokens are 76% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecLlama 3 1 405bO1 2024 12 17
Context window4,096 tokens (4K)200,000 tokens (200K)
Max output tokensN/A100,000 tokens (100K)
Speed tierDeepDeep
VisionNoYes
Function callingNoYes
Extended thinkingNoYes
Prompt cachingNoYes
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.

ProviderLlama 3 1 405b inLlama 3 1 405b outO1 2024 12 17 inO1 2024 12 17 out
Azure——$16.50/M$66.00/M
Openai——$15.00/M$60.00/M
Together Ai$3.50/M$3.50/M——

Frequently asked questions

O1 2024 12 17 has a larger context window: 200K tokens vs 4K. 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.
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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.

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