Context Window

Compare

GPT-4o Search Preview vs Llama 3 3 70b

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

GPT-4o Search Preview

Image inputTool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Meta

Model

Llama 3 3 70b

Tool calling

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.

GPT-4o Search Preview128K
Llama 3 3 70b128K

Same context window size for both models.

Token calculator →Memory savings →

GPT-4o Search Preview and Llama 3 3 70b have identical context windows (128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long output (reports, code files)

    Use GPT-4o Search Preview. Its 16K 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.

SpecGPT-4o Search PreviewLlama 3 3 70b
Context window128,000 tokens (128K)128,000 tokens (128K)
Max output tokens16,384 tokens (16K)2,048 tokens (2K)
Speed tierBalancedDeep
VisionYesNo
Function callingYesYes
Extended thinkingNoNo
Prompt cachingYesNo
Batch APIYesNo
Release dateMar 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.

ProviderGPT-4o Search Preview inGPT-4o Search Preview outLlama 3 3 70b inLlama 3 3 70b out
Azure——$0.710/M$0.710/M
Cerebras——$0.850/M$1.20/M
Deepinfra——$0.230/M$0.400/M
Hyperbolic——$0.120/M$0.300/M
Ibm Watsonx——$0.710/M$0.710/M
Meta————
Nebius——$0.130/M$0.400/M
Novita——$0.135/M$0.400/M
Nscale——$0.200/M$0.200/M
Openai$2.50/M$10.00/M——

Frequently asked questions

Llama 3 3 70b has a larger context window: 128K 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.

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