Model Cost Profile

IBM: Granite 4.0 Micro

Developer: ibm-granite

Pricing updated Mar 11, 2026

Input rank: #29Output rank: #46

Live Pricing

Input: $0.0170

Output: $0.1100

Pricing via OpenRouter API · Last synced Mar 11, 2026

IBM's Granite 4.0 Micro model, developed by ibm-granite, offers a substantial context window of 131,000 tokens, making it ideal for applications requiring extensive context, such as legal document analysis or large-scale content generation. With an input price of $0.02 per 1 million tokens and an output price of $0.11 per 1 million tokens, teams can effectively manage costs while leveraging the model for complex tasks like data summarization and conversational AI. This pricing structure allows organizations to scale their usage based on specific project needs, ensuring budget-friendly access to advanced AI capabilities.

Context Window

131,000

Tokens

Input Price / 1M

$0.0170

Prompt tokens

Output Price / 1M

$0.1100

Completion tokens

Intelligence (MMLU)

Benchmark Pending

Massive Multitask Language Understanding

Price History

IBM: Granite 4.0 Micro Pricing Trend

Input / 1M tokens0.0%Output / 1M tokens0.0%
Mar 7Mar 11
$0.0170$0.0635$0.1100Mar 7Mar 8Mar 9Mar 10Mar 11

Current Input / 1M

$0.0170

Current Output / 1M

$0.1100

Cheaper Alternatives to Compare

Quick links for cost-down decisions before production rollout.

FAQ

Common pricing and benchmark questions for IBM: Granite 4.0 Micro.

How much does IBM: Granite 4.0 Micro cost per 1M input tokens?

IBM: Granite 4.0 Micro input pricing is $0.0170 per 1M tokens based on the latest synced provider data.

How much does IBM: Granite 4.0 Micro cost per 1M output tokens?

IBM: Granite 4.0 Micro output pricing is $0.1100 per 1M tokens based on the latest synced provider data.

What context window does IBM: Granite 4.0 Micro support?

IBM: Granite 4.0 Micro supports a context window of 131,000 tokens.

How can I compare IBM: Granite 4.0 Micro with cheaper alternatives?

Use the comparison links on this page to open direct model-vs-model pricing and benchmark pages, then evaluate monthly spend projections for your workload.