Context Window

Compare

Grok 4 20 Beta 0309 Reasoning vs Openai Gpt 4o Mini

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.

Xai

Model

Grok 4 20 Beta 0309 Reasoning

Image inputTool calling

Context window

2M

2,000,000 tokens · ~1.5M words

Model page
Openai

Model

Openai Gpt 4o Mini

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.

Grok 4 20 Beta 0309 Reasoning2M
Openai Gpt 4o Mini128K

Grok 4 20 Beta 0309 Reasoning has about 15.6× the context window of the other in this pair.

Token calculator →Memory savings →

Grok 4 20 Beta 0309 Reasoning has 1462% more context capacity (2000K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Grok 4 20 Beta 0309 Reasoning. Its 2000K context fits entire documents without chunking (vs 128K).

Full specs

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

SpecGrok 4 20 Beta 0309 ReasoningOpenai Gpt 4o Mini
Context window2,000,000 tokens (2000K)128,000 tokens (128K)
Max output tokens2,000,000 tokens (2000K)N/A
Speed tierDeepFast
VisionYesNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoYes
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.

ProviderGrok 4 20 Beta 0309 Reasoning inGrok 4 20 Beta 0309 Reasoning outOpenai Gpt 4o Mini inOpenai Gpt 4o Mini out
Gradient————
Xai$2.00/M$6.00/M——

Frequently asked questions

Grok 4 20 Beta 0309 Reasoning has a larger context window: 2000K 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.

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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