Granite 4.0 H 1B (non-reasoning)

Cost-effective, high-context non-reasoning model

Granite 4.0 H 1B (non-reasoning)

An IBM-developed, open-licensed model offering exceptional value and a large context window for text generation tasks.

Open LicenseText Generation128k ContextCost LeaderIBMNon-Reasoning

Granite 4.0 H 1B stands out as a compelling offering from IBM, particularly for developers and organizations seeking a high-performance, open-licensed model without the associated costs of proprietary APIs. Positioned as a non-reasoning model, it excels in tasks that leverage its extensive 128k token context window and its ability to generate concise, relevant text outputs. Its zero-cost pricing model for both input and output tokens fundamentally shifts the economic calculus, making it an attractive option for large-scale deployments where infrastructure costs become the primary consideration.

In our comprehensive evaluation, Granite 4.0 H 1B achieved an impressive score of 14 on the Artificial Analysis Intelligence Index, placing it above the average of 13 for comparable models in its class. This indicates a robust capability for understanding and generating text, even without advanced reasoning faculties. What truly distinguishes Granite 4.0 H 1B is its remarkable conciseness; during the Intelligence Index evaluation, it generated only 2.6 million tokens, significantly less than the average of 6.7 million tokens. This efficiency translates directly into lower computational resource requirements and faster processing times for self-hosted deployments.

The model's open license further enhances its appeal, providing unparalleled flexibility for deployment, customization, and integration into diverse application environments. This freedom allows organizations to fine-tune the model for specific domain knowledge, ensure data privacy by keeping operations in-house, and avoid vendor lock-in. While its 'non-reasoning' classification means it's not designed for complex logical inference or problem-solving, its strengths lie in high-volume, context-rich text generation, summarization, and data extraction tasks where pattern recognition and contextual understanding are paramount.

Granite 4.0 H 1B represents a strategic choice for projects prioritizing cost-efficiency, data sovereignty, and the ability to handle vast amounts of contextual information. Its performance metrics, combined with its open and free nature, position it as a formidable contender in the landscape of foundational language models, particularly for applications that can leverage its strengths without requiring advanced reasoning capabilities.

Scoreboard

Intelligence

14 (10 / 22 / 22)

Above average for its class, demonstrating strong text generation capabilities.

Output speed

N/A tokens/sec

Speed metrics were not available for this model at the time of evaluation.

Input price

$0.00 per 1M tokens

Unbeatable pricing, setting the standard for cost-efficiency in its category.

Output price

$0.00 per 1M tokens

Zero-cost output makes it ideal for high-volume generation and experimentation.

Verbosity signal

2.6M tokens

Highly concise, generating significantly fewer tokens than average for the same intelligence.

Provider latency

N/A ms

Latency data was not available for this evaluation.

Technical specifications

Spec	Details
Owner	IBM
License	Open
Context Window	128k tokens
Model Type	Non-reasoning
Input Type	Text
Output Type	Text
Intelligence Index Score	14 (Rank #10/22)
Verbosity (Intelligence Index)	2.6M tokens (Rank #3/22)
Input Price	$0.00 per 1M tokens
Output Price	$0.00 per 1M tokens
Total Evaluation Cost	$0.00

What stands out beyond the scoreboard

Where this model wins

**Unbeatable Cost-Efficiency:** With $0.00 per 1M input and output tokens, Granite 4.0 H 1B eliminates API costs, making it ideal for budget-sensitive or high-volume applications.
**Expansive Context Window:** A 128k token context window allows for processing and generating text based on very long documents, conversations, or codebases.
**Exceptional Conciseness:** Its low verbosity (2.6M tokens for Intelligence Index) means more efficient token usage, reducing compute requirements and improving throughput for self-hosted deployments.
**Open License Flexibility:** The open license empowers users with full control over deployment, customization, and integration, fostering innovation and data sovereignty.
**Above-Average Intelligence (for its class):** Despite being non-reasoning, it scores well on the Intelligence Index, indicating strong capabilities for pattern recognition and text generation.
**IBM Backing:** Developed by IBM, it benefits from enterprise-grade research and development, ensuring a robust and reliable foundation.

Where costs sneak up

**Infrastructure & Compute Costs:** While the model itself is free, deploying and running it requires significant computational resources, which can accumulate substantial infrastructure costs, especially at scale.
**Lack of Reasoning Capabilities:** As a non-reasoning model, it cannot perform complex logical inference, problem-solving, or tasks requiring deep understanding beyond pattern matching, limiting its applicability for certain advanced AI use cases.
**No Direct API Provider Support:** The open-source nature means you're responsible for deployment, maintenance, and scaling, which can be a steep learning curve and resource drain for teams without MLOps expertise.
**N/A Speed & Latency Metrics:** The absence of benchmarked speed and latency data means performance characteristics in real-world scenarios must be thoroughly tested and optimized by the user.
**Potential for Over-Generation (if not managed):** While inherently concise, inefficient prompting or lack of output control mechanisms could still lead to higher token usage and associated compute costs.

Provider pick

For a model like Granite 4.0 H 1B, which is offered at $0.00 per token and under an open license, the concept of 'API provider' shifts significantly. The primary consideration moves away from per-token pricing and towards the infrastructure and operational costs associated with deploying and managing the model yourself, or leveraging cloud services that facilitate open-source model hosting.

The choice of 'provider' then becomes about your preferred deployment strategy, existing infrastructure, and the level of control and customization you require. Here, we consider common approaches to running open-licensed models.

Priority	Pick	Why	Tradeoff to accept
Priority	Pick	Why	Tradeoff
Maximum Control & Privacy	Self-hosting on Private Cloud/On-prem	Offers complete control over data, security, and infrastructure. Ideal for sensitive data or highly customized environments.	High operational overhead, requires significant MLOps expertise and hardware investment.
Scalability & Managed Infrastructure	Cloud Provider (e.g., AWS SageMaker, Azure ML, GCP Vertex AI)	Leverages managed services for easier deployment, scaling, and maintenance. Access to robust infrastructure and tooling.	Incurs cloud compute and storage costs, potential vendor lock-in for specific services, less granular control than self-hosting.
Rapid Prototyping & Community Support	Hugging Face Inference Endpoints / Spaces	Quickly deploy and experiment with the model. Benefits from the vast Hugging Face ecosystem and community support.	May have usage limits or higher costs for dedicated endpoints, less suitable for highly sensitive production data without private deployment.
Cost-Optimized Compute	Bare Metal or Dedicated Servers	Potentially lower long-term compute costs than public clouds for consistent, high-volume workloads, especially with older hardware.	Requires significant upfront investment, extensive hardware management, and expertise in system administration.

For $0.00 models, the 'provider' decision is less about API cost and more about optimizing your compute infrastructure, operational overhead, and data governance requirements.

Real workloads cost table

When a model is priced at $0.00 per token, the cost analysis for real-world workloads shifts entirely from API fees to the underlying infrastructure and operational expenses. The 'estimated cost' below reflects the compute and storage resources required to run the model for these scenarios, assuming a self-hosted or cloud-based deployment where you pay for the hardware and electricity, not the model's usage directly.

These estimates are highly variable and depend on factors like hardware specifications, optimization techniques, and regional electricity costs. The key takeaway is that efficient model deployment and resource management become paramount.

Scenario	Input	Output	What it represents	Estimated cost
Scenario	Input	Output	What it represents	Estimated cost
Long-form Content Generation	10k tokens (prompt)	50k tokens (article)	Generating a detailed blog post or report from a comprehensive prompt.	$0.00 (plus compute for ~60k tokens)
Data Extraction from Large Documents	100k tokens (document)	5k tokens (extracted data)	Parsing legal documents or research papers to extract specific information.	$0.00 (plus compute for ~105k tokens)
Summarization of Extensive Texts	80k tokens (book chapter)	2k tokens (summary)	Condensing lengthy academic papers or technical manuals into concise summaries.	$0.00 (plus compute for ~82k tokens)
Code Generation & Refactoring	20k tokens (codebase snippet + prompt)	15k tokens (new/refactored code)	Assisting developers with generating functions or refactoring existing code segments.	$0.00 (plus compute for ~35k tokens)
Chatbot with Long Context History	5k tokens (conversation history)	500 tokens (response)	Maintaining a detailed conversation with a user over an extended period.	$0.00 (plus compute for ~5.5k tokens per turn)

For Granite 4.0 H 1B, the 'cost' is entirely a function of your infrastructure, energy consumption, and operational overhead. Its conciseness helps minimize these compute costs by reducing the total tokens processed.

How to control cost (a practical playbook)

Leveraging a $0.00 open-licensed model like Granite 4.0 H 1B effectively means shifting your cost optimization strategy from API fees to infrastructure and operational efficiency. The playbook below focuses on maximizing value and minimizing the total cost of ownership for such a powerful, yet free, resource.

Optimize Infrastructure for Self-Hosting

Since Granite 4.0 H 1B is free to use, your primary cost will be the hardware and electricity to run it. Strategic infrastructure choices are crucial.

**GPU Selection:** Invest in GPUs that offer the best performance-to-cost ratio for your specific workload. Consider cloud instances with spot pricing or dedicated servers for consistent loads.
**Scalability Planning:** Design your deployment for horizontal scaling to handle fluctuating demand efficiently, spinning up or down instances as needed.
**Energy Efficiency:** Choose hardware and data centers known for energy efficiency to reduce ongoing electricity costs.

Leverage the Open License for Customization

The open license is a significant advantage, allowing deep customization that can improve performance and reduce token usage for specific tasks.

**Fine-tuning:** Fine-tune the model on your domain-specific data to improve accuracy and conciseness for your particular use cases, potentially reducing the need for lengthy prompts.
**Quantization & Pruning:** Explore techniques like quantization and pruning to reduce the model's memory footprint and computational requirements, allowing it to run on less powerful (and cheaper) hardware.
**Integration:** Seamlessly integrate the model into your existing software stack without proprietary API constraints or licensing fees.

Efficient Prompt Engineering & Context Management

Even with a 128k context window, efficient prompt engineering is vital to optimize performance and resource usage.

**Concise Prompts:** While the model is concise, crafting clear, direct prompts reduces unnecessary input tokens and guides the model to more focused outputs.
**Context Summarization:** For extremely long contexts, consider pre-processing or summarizing parts of the input to fit within the most relevant window, reducing overall token processing.
**Iterative Refinement:** Experiment with different prompt structures to find what yields the most accurate and concise results for your specific tasks.

Batch Processing for Throughput

For high-volume tasks, batching requests can significantly improve GPU utilization and overall throughput, leading to more efficient use of your compute resources.

**Maximize GPU Utilization:** Group multiple inference requests into a single batch to process them in parallel on the GPU, reducing idle time and increasing efficiency.
**Asynchronous Processing:** Implement asynchronous processing queues to handle incoming requests and feed them to the model in optimized batches.
**Resource Scheduling:** Use job schedulers to manage and prioritize batches, ensuring critical workloads are processed promptly.

Continuous Monitoring & Optimization

Ongoing monitoring of your deployment is essential to identify bottlenecks and opportunities for further cost savings.

**Performance Metrics:** Track GPU utilization, memory usage, and inference times to understand your model's performance profile.
**Cost Tracking:** Monitor your cloud or on-premise infrastructure costs closely to identify unexpected spikes or inefficiencies.
**A/B Testing:** Continuously test different deployment configurations or model versions to find the most cost-effective setup for your evolving needs.

FAQ

What is Granite 4.0 H 1B?

Granite 4.0 H 1B is an open-licensed, non-reasoning language model developed by IBM. It is designed for text generation tasks, offering a large 128k token context window and notable cost-efficiency due to its $0.00 per token pricing.

How does its intelligence compare to other models?

Granite 4.0 H 1B scored 14 on the Artificial Analysis Intelligence Index, which is above the average of 13 for comparable models. This indicates strong capabilities in understanding and generating text, particularly for a non-reasoning model.

What are the primary use cases for Granite 4.0 H 1B?

It excels in tasks requiring extensive context, such as long-form content generation, summarization of large documents, data extraction, and chatbot applications where maintaining a long conversation history is crucial. Its non-reasoning nature means it's best for pattern-based text tasks rather than complex logical problem-solving.

Is Granite 4.0 H 1B truly free to use?

Yes, the model itself is free to use under an open license, with $0.00 per 1M input and output tokens. However, users are responsible for the infrastructure costs (compute, storage, electricity) associated with deploying and running the model, whether self-hosted or on a cloud platform.

What is its context window size?

Granite 4.0 H 1B features a substantial 128k token context window, allowing it to process and generate text based on very large inputs, such as entire documents or extended dialogues.

What does 'non-reasoning' mean in this context?

'Non-reasoning' indicates that the model primarily relies on statistical patterns and contextual relationships learned from its training data to generate text. It does not perform complex logical inference, abstract problem-solving, or deep causal reasoning like some more advanced, often proprietary, models.

How does its verbosity impact usage and costs?

Granite 4.0 H 1B is highly concise, generating significantly fewer tokens (2.6M vs. 6.7M average) for the same intelligence output. This conciseness is a major advantage, as it reduces the amount of data processed, leading to lower compute resource consumption and faster inference times for self-hosted deployments.

Can I fine-tune Granite 4.0 H 1B?

Yes, as an open-licensed model, Granite 4.0 H 1B is designed to be fine-tuned on custom datasets. This allows users to adapt the model to specific domains, improve its performance on niche tasks, and tailor its output style, further enhancing its utility and efficiency for particular applications.

Granite 4.0 H 1B (non-reasoning)