A small, open-weight model from Google offering multi-modal capabilities and a low price point, but with significant trade-offs in speed and intelligence.
Gemma 3n E4B is a member of Google's Gemma family of open-weight models, designed to provide accessible and scalable AI for a wide range of developers. As a 3-billion parameter model, it occupies the smaller end of the spectrum, positioning it for tasks that do not require state-of-the-art reasoning or extensive world knowledge. Its primary appeal lies in a compelling combination of an open license, multi-modal capabilities, and an exceptionally low price point. However, this affordability comes with a clear and significant trade-off: Gemma 3n E4B is one of the slower models in its class, a factor that heavily influences its ideal use cases.
Our benchmark analysis, conducted via the Together.ai API, paints a clear picture of these trade-offs. On the Artificial Analysis Intelligence Index, Gemma 3n E4B scores a 15, placing it in the lower quartile of models tested and well below the class average of 20. This suggests limitations in handling complex, nuanced, or multi-step instructions. In stark contrast, its pricing is exceptionally competitive at just $0.02 per million input tokens and $0.04 per million output tokens. This makes it orders of magnitude cheaper than many proprietary models and highly affordable even among its open-weight peers. The total cost to run our entire intelligence benchmark on the model was a mere $1.10, highlighting its economic efficiency.
The most significant performance bottleneck is its speed. With a median output of just 43 tokens per second, it is substantially slower than the average model in its category, which typically performs at over 90 tokens per second. This sluggishness makes it a poor choice for any real-time, interactive application like a customer-facing chatbot. On the other hand, the model boasts several attractive features. It has a generous 32,000-token context window, allowing it to process large amounts of information in a single prompt. Furthermore, its ability to accept image inputs alongside text opens up a variety of use cases in vision-language tasks, a rare feature for a model at this price. It also demonstrates a tendency towards conciseness, generating fewer tokens on our benchmark than the average model, which can be a benefit for both cost and clarity.
Ultimately, Gemma 3n E4B is a specialized tool, not a general-purpose workhorse. It is not designed to compete with frontier models like GPT-4 Turbo or Claude 3 Opus. Instead, it carves out a niche for developers and organizations prioritizing cost above all else for non-time-sensitive tasks. It is best suited for asynchronous, background processes like batch data classification, first-pass content moderation, or simple document analysis where its slow speed is not a detriment and its low cost and multi-modal skills can be fully leveraged.
15 (35 / 55)
43 tokens/s
$0.02 / 1M tokens
$0.04 / 1M tokens
10M tokens
0.38 seconds
| Spec | Details |
|---|---|
| Model Owner | |
| License | Open License |
| Context Window | 32,000 tokens |
| Knowledge Cutoff | July 2024 |
| Input Modalities | Text, Image |
| Output Modalities | Text |
| Model Type | Instruction-tuned Transformer |
| Parameters | ~3 Billion |
| Benchmarked Provider | Together.ai |
| Input Price | $0.02 / 1M tokens |
| Output Price | $0.04 / 1M tokens |
| Blended Price (3:1) | $0.03 / 1M tokens |
Our performance and pricing benchmarks for Gemma 3n E4B were conducted using the Together.ai API. Together.ai specializes in providing optimized access to a wide range of open-weight models, making it a natural choice for evaluating and deploying models like this one. While self-hosting is an option due to the model's open license, using a provider like Together.ai abstracts away the complexity of infrastructure management.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Lowest Price | Together.ai | Together.ai offers Gemma 3n E4B at its rock-bottom price of $0.02 per 1M input and $0.04 per 1M output tokens, making it the clear choice for cost-sensitive applications. | The trade-off is the model's inherent performance; the low price reflects its raw capabilities without additional optimization layers that might increase cost. |
| Best Performance | Together.ai | The benchmarked speed of 43 tokens/s on Together.ai is the established performance baseline. While objectively slow, it represents a known quantity on an optimized inference stack. | This speed is a significant bottleneck. Self-hosting on specialized hardware could potentially yield better performance but requires significant technical investment. |
| Ease of Access | Together.ai | The platform provides a simple, unified API endpoint for Gemma 3n E4B, consistent with dozens of other open models, drastically reducing development and integration time. | You are reliant on the provider's uptime and infrastructure, sacrificing the full control that comes with self-hosting. |
Note: Performance metrics are specific to the Together.ai platform at the time of testing. Speeds and availability may vary on other platforms or with self-hosted configurations. Pricing is subject to change by the provider.
Gemma 3n E4B's unique profile of low cost, multi-modality, and slow speed makes it suitable for specific types of tasks. It excels where cost is the primary concern and immediate results are not required. The following examples illustrate practical scenarios where this model can provide significant value without being hampered by its limitations.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Batch Image Tagging | 1,000 tokens (prompt with instructions) + Image data | 100 tokens (JSON array of descriptive tags) | An asynchronous, non-critical background job where thousands of images are categorized overnight. Speed is irrelevant, but cost at scale is paramount. | ~$0.000024 per image |
| First-Pass Content Moderation | 300 tokens (user-generated comment) | 10 tokens (e.g., 'SAFE', 'FLAG_HATE', 'FLAG_SPAM') | A cheap, automated initial filter to catch obvious policy violations, escalating borderline cases to a more powerful model or human reviewer. | ~$0.0000064 per comment |
| Internal Document Summarization | 4,000 tokens (internal weekly report) | 400 tokens (bullet-point summary) | A non-urgent task for an internal tool where an employee can trigger a summary and wait a few seconds for the result. | ~$0.000096 per summary |
| Data Extraction from Forms | 500 tokens (prompt) + Scanned form image | 150 tokens (JSON with extracted fields like 'name', 'date') | Using vision capabilities to digitize structured data from images in a batch process where real-time performance is not needed. | ~$0.000016 per form |
The key takeaway is that Gemma 3n E4B is a cost-cutter's tool for the backend. It is most effective when deployed in asynchronous pipelines, batch processing jobs, or as a preliminary filter in a multi-step workflow. Its value diminishes rapidly as real-time interaction or high-level reasoning becomes a requirement.
While Gemma 3n E4B is already one of the cheapest models on the market, strategic implementation can further optimize your spend and ensure you're using the model effectively. The goal is to maximize its strengths (low cost, vision) while mitigating its weaknesses (slow speed, low intelligence). The following strategies provide a playbook for getting the most value out of Gemma 3n E4B.
Given its slow output speed of ~43 tokens/s, never place Gemma 3n E4B in a request/response loop that a user is actively waiting on. Instead, build your application around asynchronous job queues.
This pattern makes the model's slow speed invisible to the end-user and prevents your application from being blocked by long-running API calls.
In a multi-model system, Gemma 3n E4B can serve as an inexpensive first-pass filter. It can handle the majority of simple, high-volume requests, escalating only the complex or ambiguous cases to a more capable (and expensive) model like Claude 3 Sonnet or GPT-4o.
The model's ability to process images is a standout feature at its price point. Capitalize on this for offline, large-scale image analysis tasks where cost is the primary driver.
These tasks are often prohibitively expensive with higher-end multi-modal models, but become feasible with Gemma 3n E4B's pricing.
Gemma 3n E4B is a small, 3-billion parameter open-weight language model developed by Google. It is part of the Gemma family, which are built from the same research and technology used to create the Gemini models. It is designed to be a lightweight, accessible model that is instruction-tuned and capable of processing both text and image inputs.
No, it is generally not recommended for real-time, user-facing chatbots. Its output speed of approximately 43 tokens per second is quite slow, which would result in a laggy and frustrating user experience as they wait for responses to be generated. It is better suited for applications where response time is not a critical factor.
The primary advantages are:
The main drawbacks are directly related to its performance:
Gemma 3n E4B is one of the smaller models in the Gemma family. Larger versions, such as Gemma 9B, offer significantly better performance on reasoning and knowledge-based tasks, but at a higher computational cost and price. The 3n E4B model is optimized for maximum efficiency and accessibility, targeting use cases where a smaller, more economical model is sufficient.
For Gemma 3n E4B, 'multi-modal' means it can accept more than one type of input data in a single prompt. Specifically, it can process both text and images. You can provide it with an image and ask questions about it in text. However, its output is limited to text only; it cannot generate images.