Context Window

Compare

Claude Instant vs GPT-4o Audio

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.

Anthropic

Model

Claude Instant

Context window

100K

100,000 tokens · ~75K words

Model page
Openai

Model

GPT-4o Audio

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.

Claude Instant100K
GPT-4o Audio128K

GPT-4o Audio has about 1.3× the context window of the other in this pair.

Token calculator →Memory savings →

GPT-4o Audio has 28% more context capacity (128K vs 100K tokens). Claude Instant is 68% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use GPT-4o Audio. Its 128K context fits entire documents without chunking (vs 100K).

  • RAG / high-volume retrieval

    Use Claude Instant. Input tokens are 68% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use GPT-4o Audio. 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.

SpecClaude InstantGPT-4o Audio
Context window100,000 tokens (100K)128,000 tokens (128K)
Max output tokens8,191 tokens (8K)16,384 tokens (16K)
Speed tierBalancedBalanced
VisionNoNo
Function callingNoYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APIYesYes
Release dateN/AAug 2025

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.

ProviderClaude Instant inClaude Instant outGPT-4o Audio inGPT-4o Audio out
Aws Bedrock$0.800/M$2.40/M——
Openai——$2.50/M$10.00/M

Frequently asked questions

GPT-4o Audio has a larger context window: 128K tokens vs 100K. 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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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.

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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