An exceptionally fast, open-weight model from IBM, offering a huge context window and cost-effective inputs, ideal for high-throughput, non-reasoning tasks.
IBM's Granite 3.3 8B (Non-reasoning) is an open-weight large language model that carves out a specific niche in the AI landscape. With 8 billion parameters, it's a mid-sized model, but its standout features are a colossal 128,000-token context window and class-leading generation speed. As its name explicitly states, this is a "non-reasoning" model, meaning it has been fine-tuned for tasks that do not require deep logical inference, complex problem-solving, or nuanced conversational abilities. Instead, it's engineered for high-throughput scenarios like summarization, data extraction, and Retrieval-Augmented Generation (RAG).
The performance profile of Granite 3.3 8B is one of stark contrasts. In our benchmarks, it achieved the #1 rank for speed, delivering an astonishing median output of over 375 tokens per second. This makes it a powerhouse for batch processing jobs where generating large volumes of text quickly is the primary goal. However, this raw throughput is paired with a very high time-to-first-token (TTFT) of over 11 seconds on the benchmarked provider, Replicate. This significant latency makes it a poor choice for real-time, interactive applications. Furthermore, its intelligence score of 15 on the Artificial Analysis Intelligence Index is well below the class average of 20, reinforcing that it is not built for tasks requiring intricate understanding or creativity.
The pricing model further accentuates its specialized nature. Input tokens are very affordable at $0.03 per million, ranking it favorably against peers. This encourages its use in applications that need to process, analyze, or summarize vast amounts of text. In contrast, output tokens are priced at $0.25 per million, which is slightly above the average. This cost structure creates a clear financial incentive: use Granite 3.3 8B for tasks that are input-heavy and output-light. Our own evaluation on the Intelligence Index, which involved generating 7.4 million tokens, cost a total of $3.67, providing a tangible sense of its operational cost at scale.
Ultimately, Granite 3.3 8B is a tool built for a purpose. It's not a generalist chatbot or a creative partner. It is an industrial-strength text processor. Developers who can align their use case with the model's strengths—its speed, massive context window, and input-friendly pricing—will find it to be a uniquely powerful and cost-effective solution. Those who need quick-witted interaction, creative generation, or complex reasoning should look elsewhere.
15 (36 / 55)
375.2 tokens/s
0.03 $/1M tokens
0.25 $/1M tokens
7.4M tokens
11.17 seconds
| Spec | Details |
|---|---|
| Model Owner | IBM |
| Model Variant | 3.3 8B (Non-reasoning) |
| Parameters | ~8 Billion |
| Context Window | 128,000 tokens |
| License | Open License (Apache 2.0-based) |
| Input Modalities | Text |
| Output Modalities | Text |
| Intelligence Index Score | 15 / 100 |
| Speed Rank | #1 / 55 |
| Input Price Rank | #21 / 55 |
| Primary Use Cases | High-throughput summarization, data extraction, Retrieval-Augmented Generation (RAG) |
| Not Suited For | Complex reasoning, creative writing, chatbot applications, real-time interaction |
Currently, our benchmark data for Granite 3.3 8B is available from Replicate. This makes it the de facto choice for developers looking to get started quickly with a managed API. The selection of a provider for this model is less about a competitive landscape and more about understanding the specific performance profile offered by the available implementation.
Your choice, therefore, is less about which provider to use and more about which use case is appropriate for the performance characteristics observed on Replicate.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Raw Speed | Replicate | It delivers the #1 ranked output speed in our benchmarks, making it the go-to for maximum throughput in batch jobs. | Extremely high latency (TTFT) means it's for non-interactive tasks only. |
| Large Document Analysis | Replicate | Combines the model's 128k context window with a low input price, ideal for processing extensive texts. | High cost for detailed, lengthy output summaries. |
| Simplicity & Quick Start | Replicate | As the primary provider with a public API for this model in our tests, it's the easiest and fastest way to integrate Granite 3.3 8B. | Lack of competitive pricing or performance options from other vendors. |
| Cost Control | Replicate (with caution) | The low input price is attractive, but requires careful management of output length to avoid high costs. | Requires application-level logic (e.g., `max_tokens`) to keep outputs concise. |
Note: Performance metrics like latency and throughput are specific to the provider's infrastructure and implementation. As more providers adopt Granite 3.3 8B, these figures may vary. The current analysis is based solely on data from Replicate.
To understand Granite 3.3 8B's practical costs, let's model a few real-world scenarios. These estimates are based on Replicate's pricing of $0.03 per 1M input tokens and $0.25 per 1M output tokens. Notice how the cost balance shifts dramatically depending on the ratio of input to output, which is the key to using this model effectively.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Batch Summarization | 100 articles, 2,000 tokens each (200k total) | 100 summaries, 150 tokens each (15k total) | A core RAG or content summarization task, playing to the model's strengths. | ~$0.01 |
| Data Extraction from a Report | One 50,000-token financial report | 500 tokens of structured JSON data | Analyzing a large document for specific information, a key use case for the large context window. | ~$0.002 |
| Reformatting 1,000 User Comments | 1,000 comments, 100 tokens each (100k total) | 1,000 formatted comments, 120 tokens each (120k total) | A bulk processing task where output is slightly larger than input. | ~$0.033 |
| Daily News Briefing Service | 500k tokens of news feeds | 10k tokens of summarized briefings | A recurring, input-heavy automated task perfect for this model's cost structure. | ~$0.018 per day |
| Generating Long-Form Content (Anti-Pattern) | A 500-token prompt | A 4,000-token article | A task that misuses the model's pricing, leading to higher costs due to expensive outputs. | ~$0.001 per article |
The takeaway is clear: Granite 3.3 8B is exceptionally cheap for tasks that 'read' a lot and 'write' a little. The cost for analyzing hundreds of thousands of tokens is often less than a cent. However, as soon as the task requires generating significant amounts of text, the higher output price becomes the dominant factor in the total cost. Successful implementation depends entirely on fitting the workload to this cost model.
Managing costs for Granite 3.3 8B is all about controlling the output. Its unique pricing structure—cheap inputs, expensive outputs—creates specific opportunities for optimization and potential pitfalls. Ignoring this dynamic is the fastest way to an unexpectedly high bill. Here are several strategies to ensure you're using the model efficiently.
This is the most critical cost control measure. The model's output is over 8x more expensive than its input. Never let it generate text unbounded.
max_tokens parameter (or equivalent) in your API calls to set a hard ceiling on generation length.Design your applications around the model's strengths. The ideal task for Granite 3.3 8B has a high input-to-output token ratio.
Don't force Granite 3.3 8B to do what it's not good at. For tasks that require both large-context processing and nuanced reasoning, use a multi-step, multi-model approach.
While not a direct token cost, the high TTFT has financial implications. An 11-second wait is unacceptable for users in a real-time app, leading to abandonment. For backend tasks, it can tie up resources.
It is an 8-billion parameter, open-weight large language model developed by IBM. It is specifically designed for high-throughput, non-reasoning tasks, featuring a very large 128,000-token context window and extremely fast text generation speeds.
It means the model has been intentionally fine-tuned to excel at tasks that don't require complex logic, multi-step problem solving, or deep semantic understanding. It's good at pattern matching, summarization, and information retrieval (finding facts in a text). It is not good at math, writing code, or having a nuanced conversation.
Developers and businesses that need to process large volumes of text quickly and affordably. Ideal use cases include:
This is a common performance profile for certain model architectures and serving infrastructures. The high latency (Time To First Token) of 11+ seconds likely reflects a 'cold start' problem, where the model takes a long time to load into memory and prepare for inference. However, once it's running, the model is highly optimized to generate subsequent tokens very rapidly (high throughput). This makes it great for long, continuous generation jobs but poor for short, interactive queries.
A 128,000-token context window allows the model to 'read' and reference a very large amount of text in a single prompt—equivalent to a 200-page book. This is invaluable for tasks where context is everything, such as asking detailed questions about a long annual report or summarizing an entire book, without having to split the text into smaller chunks.
No, it is a very poor choice for a chatbot. Its low intelligence score means it will struggle with conversational nuance, and its 11-second latency would create a frustratingly slow user experience. A chatbot requires low latency and strong reasoning/conversational skills, which are the primary weaknesses of this model.