IBM's compact open-weight model, offering extreme cost-effectiveness for basic text generation and classification where high intelligence is not required.
IBM's Granite 4.0 H 350M is a member of the Granite family of open-weight models, specifically engineered for efficiency and cost-effectiveness. With only 350 million parameters, it stands in stark contrast to the multi-billion parameter giants that dominate headlines. This small size is not a weakness but a design choice, positioning the model for a specific niche: high-volume, low-complexity tasks where speed, low resource consumption, and minimal cost are the primary drivers. It's a tool built for utility over sophistication, designed to be deployed easily and run cheaply.
The model's performance on the Artificial Analysis Intelligence Index reflects this positioning. Scoring an 8, it sits at the lower end of the spectrum compared to its peers, which average a score of 13. This indicates that Granite 350M is not suited for tasks requiring deep reasoning, nuanced understanding, or complex instruction following. It may struggle with creative writing, multi-step problem-solving, or generating deeply analytical text. Instead, its strengths lie in straightforward, repetitive functions like basic classification, keyword extraction, and simple text formatting.
Where Granite 350M truly distinguishes itself is on cost and conciseness. With a benchmarked price of $0.00 per million tokens for both input and output on select providers, it is effectively free to run for many use cases. This makes it an incredibly compelling option for startups, researchers, or any organization operating under tight budget constraints. Furthermore, its evaluation on the Intelligence Index required only 1.2 million tokens, a fraction of the 6.7 million average. This low verbosity means it produces concise, to-the-point outputs, which not only reduces the token count but also can lead to faster overall response times and lower data processing overhead.
Released under a permissive Apache 2.0 license, Granite 350M offers developers maximum flexibility for commercial use, modification, and distribution. It represents a strategic move by IBM to provide the open-source community with foundational models that can be adapted for specific, resource-constrained environments. For developers building applications that need a lightweight text-processing engine, and who can work within its intellectual limitations, Granite 350M presents an almost unbeatable value proposition.
8 (18 / 22)
N/A tok/s
$0.00 / 1M tokens
$0.00 / 1M tokens
1.2M tokens
N/A seconds
| Spec | Details |
|---|---|
| Model Name | Granite 4.0 H 350M |
| Owner / Developer | IBM |
| Parameters | ~350 Million |
| Architecture | Decoder-only Transformer |
| Context Window | 32,768 tokens |
| License | Apache 2.0 (Open Weight) |
| Input Modalities | Text |
| Output Modalities | Text |
| Training Data | A proprietary mix of public web data (CommonCrawl), code, and academic sources, filtered for quality. |
| Intended Use | Simple text generation, summarization, classification, and other non-reasoning tasks. |
| Finetuning Support | Yes, as an open-weight model it is designed to be adaptable and finetunable for specific domains. |
| Model Family | Part of IBM's Granite series of enterprise-focused models. |
Choosing a provider for an open-weight model like Granite 350M involves balancing cost, performance, and operational complexity. While some platforms offer it for free as a loss leader or for community access, this often comes with tradeoffs in speed, reliability, or usage limits. Your choice depends entirely on your project's specific priorities.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Lowest Cost | Free Tier Providers | Unbeatable price point of $0.00 for both input and output. Ideal for experimentation, academic use, or non-critical, high-volume tasks. | May have stricter rate limits, lower throughput, and potential 'cold start' latency. Not suitable for production-critical, low-latency applications. |
| Balanced Performance | Pay-as-you-go GPU Platforms | Offers a good middle ground with reasonable per-second or per-token pricing, better reliability, and more consistent performance than free tiers. | No longer free. Costs can add up with high volume, requiring careful monitoring of usage and infrastructure. |
| Maximum Control & Privacy | Self-Hosting (Cloud or On-Prem) | Complete control over the model, data privacy, and performance tuning. No per-token costs, only hardware and operational expenses. | Highest upfront cost and complexity. Requires significant MLOps expertise to manage deployment, scaling, and maintenance. |
| Ease of Use | Managed Inference APIs | Simplifies deployment to a single API call. The provider handles all the infrastructure, scaling, and maintenance, allowing teams to focus on the application. | Less control over the underlying hardware and potentially higher costs compared to self-hosting, but with a much lower operational burden. |
Provider availability, pricing, and performance metrics for open-weight models change frequently. The 'free' pricing noted in our benchmarks is specific to the providers evaluated at the time of testing and may not be universally available or may be subject to change.
To understand the practical cost implications of Granite 350M, let's examine a few common, low-complexity workloads. We'll use the benchmarked price of $0.00 per million input and output tokens. This makes the direct cost calculation straightforward but underscores the importance of evaluating output quality for each task.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Email Subject Line Generation | 200 tokens (email body) | 10 tokens (subject line) | A high-volume, repetitive task for marketing automation. | $0.00 |
| Sentiment Analysis | 150 tokens (customer review) | 5 tokens ('Positive', 'Negative', 'Neutral') | Classifying a large batch of user feedback for trend analysis. | $0.00 |
| Keyword Extraction | 500 tokens (short article) | 25 tokens (comma-separated keywords) | Basic content tagging for a CMS or search index. | $0.00 |
| Simple Data Formatting | 100 tokens (unstructured text) | 30 tokens (JSON object) | Converting snippets of text into a structured format. | $0.00 |
| Basic Chatbot Response | 250 tokens (user query + history) | 40 tokens (canned or simple response) | Handling first-level support questions with predefined answers. | $0.00 |
The direct API cost for these workloads is zero on benchmarked providers, making Granite 350M exceptionally attractive for cost-sensitive applications. The real 'cost' to consider is whether its low intelligence can reliably perform these tasks to the required quality standard without needing frequent human intervention or a fallback to a more expensive model.
While Granite 350M is already priced at zero on some platforms, optimizing its use is still crucial to manage overall system costs and ensure quality. The focus shifts from minimizing token counts to maximizing the model's effectiveness within its limited capabilities and preventing costly errors.
Use Granite 350M as a first-pass filter in a multi-model chain. It can handle simple, high-volume requests and escalate more complex ones to a larger, more expensive model.
Given the model's low intelligence, its outputs must be validated. The cost of building these guardrails is often less than the cost of errors in production.
Do not treat Granite 350M like a sophisticated reasoning engine. Prompts should be clear, direct, and simple. Avoid ambiguity and complex, multi-part instructions.
The model's tendency to produce short outputs is an advantage. Design workflows that benefit from this.
Granite 4.0 H 350M is a small, 350-million-parameter, open-weight language model developed by IBM. It is designed for efficiency and cost-effectiveness, making it suitable for simple, high-volume text processing tasks rather than complex reasoning.
This model is ideal for developers, startups, and organizations with budget constraints who need to perform simple, repetitive NLP tasks at scale. Examples include sentiment analysis, keyword extraction, basic classification, and simple data formatting.
Granite 350M is significantly smaller (350M vs. 7B or 8B parameters). As a result, it is less intelligent and capable than models like Llama 3 8B or Mistral 7B. However, it is also much faster, requires fewer computational resources, and is cheaper (or free) to run, making it a better choice for tasks that do not require high levels of reasoning.
'Open weight' means that the model's parameters (the 'weights') are publicly released, in this case under an Apache 2.0 license. This allows anyone to download, modify, and run the model on their own hardware, offering maximum flexibility and control compared to closed, API-only models.
The model itself is free to download and use due to its open license. Some cloud providers also offer managed API access to it for $0.00 per million tokens as a promotional or free-tier offering. However, this pricing is provider-specific and may come with usage limits or performance trade-offs. Self-hosting the model incurs hardware and operational costs.
The primary limitations are a lower capacity for reasoning, a smaller knowledge base, and a reduced ability to understand nuance and complex instructions. This can lead to factual errors, overly simplistic outputs, and failure to follow complex prompts. It is not suitable for tasks requiring creativity, deep analysis, or multi-step problem-solving.
Yes. As an open-weight model, it is designed to be finetuned. Developers can adapt the base model to a specific domain or task using their own data, potentially improving its performance and accuracy for a specialized use case.