Context Window

Compare

Ft:gpt 4 0613 vs Meta Llama 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

Ft:gpt 4 0613

Tool calling

Context window

8K

8,192 tokens · ~6K words

Model page
Meta

Model

Meta Llama 3 70b

Context window

8K

8,192 tokens · ~6K 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.

Ft:gpt 4 06138K
Meta Llama 3 70b8K

Same context window size for both models.

Token calculator →Memory savings →

Ft:gpt 4 0613 and Meta Llama 3 70b have identical context windows (8K tokens). Meta Llama 3 70b is 99% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Meta Llama 3 70b. Input tokens are 99% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Meta Llama 3 70b. Its 8K 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.

SpecFt:gpt 4 0613Meta Llama 3 70b
Context window8,192 tokens (8K)8,192 tokens (8K)
Max output tokens4,096 tokens (4K)8,192 tokens (8K)
Speed tierBalancedDeep
VisionNoNo
Function callingYesNo
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
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.

ProviderFt:gpt 4 0613 inFt:gpt 4 0613 outMeta Llama 3 70b inMeta Llama 3 70b out
Anyscale——$1.00/M$1.00/M
Azure——$1.10/M$0.370/M
Hyperbolic——$0.120/M$0.300/M
Openai$30.00/M$60.00/M——

Frequently asked questions

Meta Llama 3 70b has a larger context window: 8K tokens vs 8K. 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