IBM's Granite 4.0 H Small excels in speed and conciseness, making it a strong choice for high-throughput text generation where complex reasoning isn't required.
Granite 4.0 H Small, developed by IBM, stands out as a highly efficient and remarkably fast language model tailored for specific text generation needs. Positioned as a non-reasoning model, it delivers exceptional performance in tasks requiring rapid and concise output, making it a compelling option for developers prioritizing speed and cost-effectiveness in high-volume applications. Its design focuses on delivering results quickly and efficiently, rather than tackling complex analytical problems.
In terms of raw intelligence, Granite 4.0 H Small achieves a score of 23 on the Artificial Analysis Intelligence Index, placing it above the average for comparable models, which typically score around 20. What truly distinguishes this model in its class is its remarkable conciseness; during the Intelligence Index evaluation, it generated only 5.2 million tokens, significantly less than the average of 13 million tokens. This efficiency translates directly into lower operational costs and faster processing times, especially for tasks where brevity is an asset.
Speed is a core strength of Granite 4.0 H Small. It boasts an impressive median output speed of 340 tokens per second, making it one of the fastest models benchmarked. This high throughput is critical for applications demanding real-time or near real-time text generation. While its latency, measured at 8.82 seconds for time to first token (TTFT), is a factor to consider for highly interactive, low-latency use cases, its overall output speed compensates significantly for batch processing or scenarios where the initial response time is less critical than the total generation speed.
From a pricing perspective, Granite 4.0 H Small offers a competitive blended rate of $0.11 per 1 million tokens on Replicate, based on a 3:1 input-to-output token ratio. Breaking this down, input tokens are priced at $0.06 per 1 million, which is moderately priced compared to an average of $0.10. Output tokens, however, are somewhat more expensive at $0.25 per 1 million, against an average of $0.20. Despite the higher output token cost, its exceptional conciseness often leads to lower overall costs for many applications, as fewer output tokens are generated to achieve the desired result. The total cost to evaluate this model on the Intelligence Index was $4.16, reflecting its efficiency.
With a substantial 128k token context window, Granite 4.0 H Small can process and generate text based on extensive inputs, providing flexibility for various applications. Its primary utility lies in scenarios where rapid, straightforward text generation is paramount, such as content creation, data summarization, or automated responses, particularly when complex reasoning or nuanced understanding beyond pattern recognition is not the main requirement. Its combination of speed, conciseness, and above-average intelligence for its category makes it a powerful tool for optimizing operational efficiency.
23 (#22 / 55)
340 tokens/s
$0.06 per 1M tokens
$0.25 per 1M tokens
5.2M tokens
8.82 seconds
| Spec | Details |
|---|---|
| Owner | IBM |
| License | Open |
| Context Window | 128k tokens |
| Input Type | Text |
| Output Type | Text |
| Intelligence Index Score | 23 / 55 |
| Output Speed | 340 tokens/s |
| Latency (TTFT) | 8.82 seconds |
| Input Token Price | $0.06 / 1M tokens |
| Output Token Price | $0.25 / 1M tokens |
| Blended Price (3:1) | $0.11 / 1M tokens |
| Verbosity (Intelligence Index) | 5.2M tokens |
Choosing the right API provider for Granite 4.0 H Small is crucial for optimizing both performance and cost. Our analysis focuses on the primary provider where this model has been extensively benchmarked, offering insights into what you can expect.
While the model itself is open, its deployment and API access are managed by specific platforms. Understanding the provider's infrastructure, pricing structure, and support can significantly impact your project's success and budget.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Primary | Replicate | Replicate offers straightforward API access and transparent pricing for Granite 4.0 H Small, making deployment relatively simple. Their platform is well-suited for developers looking to integrate quickly. | While convenient, relying on a single provider might limit flexibility in terms of custom infrastructure or alternative pricing models. Potential for vendor lock-in. |
| Alternative (Self-Host) | Open Source Deployment | For advanced users, deploying the open-source model on your own infrastructure (e.g., AWS, Azure, GCP) offers maximum control over costs, data privacy, and customization. | Requires significant MLOps expertise, infrastructure management, and ongoing maintenance, increasing operational overhead. |
| Future Consideration | Other API Platforms | As the model gains traction, other API providers may offer Granite 4.0 H Small, potentially introducing competitive pricing or specialized features. | Availability and performance on other platforms are currently unverified, requiring independent benchmarking. |
Note: All benchmark data for Granite 4.0 H Small is currently derived from its performance on Replicate. Performance and pricing may vary on other platforms or with self-hosting.
Understanding the real-world cost implications of Granite 4.0 H Small requires looking beyond raw token prices and considering typical usage patterns. The model's conciseness and speed can significantly alter the effective cost for various applications.
Below are several common scenarios, illustrating how Granite 4.0 H Small's unique characteristics translate into estimated costs for specific tasks, assuming a 3:1 input-to-output token ratio for blended pricing where applicable.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Summarization | 10,000 input tokens (long article) | 500 output tokens (concise summary) | Condensing lengthy content into brief, key takeaways. | ~$0.0007 (Input: $0.0006, Output: $0.000125) |
| Product Description Generation | 500 input tokens (product features) | 150 output tokens (short description) | Creating numerous short, engaging product descriptions. | ~$0.00003 (Input: $0.00003, Output: $0.0000375) |
| Email Drafts | 2,000 input tokens (context, bullet points) | 300 output tokens (draft email) | Generating quick drafts for customer service or marketing. | ~$0.00015 (Input: $0.00012, Output: $0.000075) |
| Data Extraction (Structured) | 5,000 input tokens (unstructured text) | 200 output tokens (JSON output) | Extracting specific entities or data points into a structured format. | ~$0.00035 (Input: $0.0003, Output: $0.00005) |
| Simple Chatbot Response | 100 input tokens (user query, history) | 50 output tokens (direct answer) | Providing quick, non-reasoning based responses in a chatbot. | ~$0.000008 (Input: $0.000006, Output: $0.0000125) |
These examples highlight that while Granite 4.0 H Small has a higher output token price, its inherent conciseness often means fewer output tokens are generated, leading to surprisingly low costs for many practical applications, especially those focused on generating brief, direct responses or summaries.
Optimizing costs with Granite 4.0 H Small involves leveraging its strengths and mitigating its potential drawbacks. Given its high speed and conciseness, strategic implementation can lead to significant savings, particularly for high-volume operations.
Here are key strategies to ensure you get the most value from this model while keeping your expenses in check:
Granite 4.0 H Small is inherently concise. Design your prompts to encourage brief, direct answers. Avoid asking for verbose explanations or unnecessary details if not critical for the task. This directly reduces output token count, mitigating the higher output token price.
With 340 tokens/s, Granite 4.0 H Small is a powerhouse for generating large volumes of text. Batching requests can amortize the initial latency (8.82s TTFT) across multiple generations, maximizing throughput and overall efficiency.
The 128k context window is generous, but every input token costs money. Only include information absolutely necessary for the model to perform its task. Avoid sending redundant or irrelevant data.
Granite 4.0 H Small has a lower input token price ($0.06/M) but a higher output token price ($0.25/M). Keep an eye on your actual input-to-output token ratio. If your application consistently generates very long outputs, the higher output price will dominate your costs.
Recognize and respect the model's non-reasoning nature. Deploy it for tasks where it truly excels: straightforward text generation, summarization, rephrasing, or data extraction based on patterns, not complex logic. Using it for tasks beyond its capabilities will lead to poor results and wasted tokens.
Its primary strengths are exceptional output speed (340 tokens/s), remarkable conciseness (generating significantly fewer tokens for tasks), and above-average intelligence for a non-reasoning model. These attributes make it highly efficient and cost-effective for specific text generation tasks.
No, Granite 4.0 H Small is explicitly categorized as a non-reasoning model. It excels at pattern-based text generation, summarization, and extraction but is not designed for complex logical deduction, problem-solving, or tasks requiring deep, nuanced understanding.
It has a moderately priced input token cost ($0.06/M) but a somewhat higher output token cost ($0.25/M). However, its extreme conciseness often leads to a very competitive blended price ($0.11/M) because it generates fewer output tokens overall, making it cost-efficient for many applications.
A 128k token context window allows the model to process and generate text based on very long inputs, such as entire documents or extensive conversation histories. This provides great flexibility for tasks requiring a broad understanding of the provided context.
It is best suited for high-throughput applications requiring fast, concise, and straightforward text generation. Examples include automated content creation (e.g., product descriptions, social media posts), data summarization, structured data extraction, and simple chatbot responses where complex reasoning is not a prerequisite.
To optimize costs, focus on prompt engineering to encourage concise outputs, leverage its high speed for batch processing, manage the context window efficiently by only including necessary information, and monitor your input-to-output token ratio to ensure it aligns with your budget expectations.
The model has a latency (Time To First Token - TTFT) of 8.82 seconds. While this might be a consideration for highly interactive, real-time applications, its exceptional output speed means that once it starts generating, it does so very quickly, making it efficient for tasks where initial response time is less critical than overall generation speed.