Context Window

Compare

Gpt 35 Turbo Instruct 0914 vs Granite 4 H Small

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 35 Turbo Instruct 0914

Context window

4K

4,097 tokens · ~3K words

Model page
Ibm

Model

Granite 4 H Small

Tool calling

Context window

20K

20,480 tokens · ~15K 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 35 Turbo Instruct 09144K
Granite 4 H Small20K

Granite 4 H Small has about 5× the context window of the other in this pair.

Token calculator →Memory savings →

Granite 4 H Small has 399% more context capacity (20K vs 4K tokens). Granite 4 H Small is 96% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Granite 4 H Small. Its 20K context fits entire documents without chunking (vs 4K).

  • RAG / high-volume retrieval

    Use Granite 4 H Small. Input tokens are 96% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecGpt 35 Turbo Instruct 0914Granite 4 H Small
Context window4,097 tokens (4K)20,480 tokens (20K)
Max output tokensN/A20,480 tokens (20K)
Speed tierBalancedBalanced
VisionNoNo
Function callingNoYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
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.

ProviderGpt 35 Turbo Instruct 0914 inGpt 35 Turbo Instruct 0914 outGranite 4 H Small inGranite 4 H Small out
Azure$1.50/M$2.00/M——
Ibm Watsonx——$0.060/M$0.250/M

Frequently asked questions

Granite 4 H Small has a larger context window: 20K tokens vs 4K. 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.

Calculate your savingsLearn about Mem0

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