Compare

Gpt 5 3 Chat Latest vs Grok 3 Beta

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 5 3 Chat Latest

Image inputTool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Xai

Model

Grok 3 Beta

Tool calling

Context window

131K

131,072 tokens · ~98K 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 5 3 Chat Latest128K
Grok 3 Beta131K

Grok 3 Beta has about 1× the context window of the other in this pair.

Grok 3 Beta has 2% more context capacity (131K vs 128K tokens). Gpt 5 3 Chat Latest is 41% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Grok 3 Beta. Its 131K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Gpt 5 3 Chat Latest. Input tokens are 41% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Grok 3 Beta. Its 131K 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 5 3 Chat LatestGrok 3 Beta
Context window128,000 tokens (128K)131,072 tokens (131K)
Max output tokens16,384 tokens (16K)131,072 tokens (131K)
Speed tierBalancedBalanced
VisionYesNo
Function callingYesYes
Extended thinkingYesNo
Prompt cachingYesYes
Batch APINoNo
Release dateN/AApr 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.

ProviderGpt 5 3 Chat Latest inGpt 5 3 Chat Latest outGrok 3 Beta inGrok 3 Beta out
Openai$1.75/M$14.00/M
Xai$3.00/M$15.00/M

Frequently asked questions

Grok 3 Beta has a larger context window: 131K 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.

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