Context Window

Compare

Claude 3 Opus vs Gpt 35 Turbo 16k 0613

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 3 Opus

Context window

200K

200,000 tokens · ~150K words

Model page
Openai

Model

Gpt 35 Turbo 16k 0613

Tool calling

Context window

16K

16,385 tokens · ~12K 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 3 Opus200K
Gpt 35 Turbo 16k 061316K

Claude 3 Opus has about 12.2× the context window of the other in this pair.

Token calculator →Memory savings →

Claude 3 Opus has 1120% more context capacity (200K vs 16K tokens). Gpt 35 Turbo 16k 0613 is 80% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Claude 3 Opus. Its 200K context fits entire documents without chunking (vs 16K).

  • RAG / high-volume retrieval

    Use Gpt 35 Turbo 16k 0613. Input tokens are 80% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecClaude 3 OpusGpt 35 Turbo 16k 0613
Context window200,000 tokens (200K)16,385 tokens (16K)
Max output tokensN/A4,096 tokens (4K)
Speed tierDeepBalanced
VisionNoNo
Function callingNoYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APIYesNo
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.

ProviderClaude 3 Opus inClaude 3 Opus outGpt 35 Turbo 16k 0613 inGpt 35 Turbo 16k 0613 out
Azure——$3.00/M$4.00/M
Google Vertex$15.00/M$75.00/M——
Gradient$15.00/M$75.00/M——

Frequently asked questions

Claude 3 Opus has a larger context window: 200K tokens vs 16K. 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