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Granite 4 H Small vs Grok 4.3

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.

Ibm

Model

Granite 4 H Small

Tool calling

Context window

20K

20,480 tokens · ~15K words

Model page
Xai

Model

Grok 4.3

Image inputTool calling

Context window

1M

1,000,000 tokens · ~750K 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.

Granite 4 H Small20K
Grok 4.31M

Grok 4.3 has about 48.8× the context window of the other in this pair.

Grok 4.3 has 4782% more context capacity (1000K vs 20K tokens). Granite 4 H Small is 95% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Grok 4.3. Its 1000K context fits entire documents without chunking (vs 20K).

  • RAG / high-volume retrieval

    Use Granite 4 H Small. Input tokens are 95% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecGranite 4 H SmallGrok 4.3
Context window20,480 tokens (20K)1,000,000 tokens (1000K)
Max output tokens20,480 tokens (20K)N/A
Speed tierBalancedBalanced
VisionNoYes
Function callingYesYes
Extended thinkingNoYes
Prompt cachingNoYes
Batch APINoNo
Release dateN/AApr 2026

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.

ProviderGranite 4 H Small inGranite 4 H Small outGrok 4.3 inGrok 4.3 out
Ibm Watsonx$0.060/M$0.250/M
Xai$1.25/M$2.50/M

Frequently asked questions

Grok 4.3 has a larger context window: 1000K tokens vs 20K. 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