Context Window

Compare

Gpt Audio 1 5 vs Meta Llama3 1 405b Instruct

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 Audio 1 5

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Meta

Model

Meta Llama3 1 405b Instruct

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 Audio 1 5128K
Meta Llama3 1 405b Instruct128K

Same context window size for both models.

Token calculator →Memory savings →

Gpt Audio 1 5 and Meta Llama3 1 405b Instruct have identical context windows (128K tokens). Gpt Audio 1 5 is 53% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Gpt Audio 1 5. Input tokens are 53% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Gpt Audio 1 5. 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 Audio 1 5Meta Llama3 1 405b Instruct
Context window128,000 tokens (128K)128,000 tokens (128K)
Max output tokens16,384 tokens (16K)4,096 tokens (4K)
Speed tierBalancedDeep
VisionNoNo
Function callingYesYes
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.

ProviderGpt Audio 1 5 inGpt Audio 1 5 outMeta Llama3 1 405b Instruct inMeta Llama3 1 405b Instruct out
Aws Bedrock——$5.32/M$16.00/M
Openai$2.50/M$10.00/M——

Frequently asked questions

Meta Llama3 1 405b Instruct 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