A small, multi-modal open-weight model from Google, offering unbeatable free pricing with significant trade-offs in speed and intelligence.
Gemma 3n E2B Instruct is a small-scale, open-weight model from Google, positioned as a highly accessible entry point into the Gemma family of models. As an instruction-tuned variant, it's designed to follow user prompts for a variety of tasks. Its most notable feature is its multi-modal capability, accepting both text and image inputs to produce text outputs. Released under a license permitting commercial use, Gemma 3n E2B is aimed at researchers, students, and developers looking to experiment with AI without incurring costs, particularly for applications where top-tier performance is not a primary concern.
In our analysis, Gemma 3n E2B's performance profile is one of stark contrasts. It scores a mere 11 on the Artificial Analysis Intelligence Index, placing it at rank #44 out of 55 models evaluated. This positions it firmly in the lower echelon of AI capability, struggling with tasks that require deep reasoning, nuance, or complex instruction-following. This low intelligence is a critical factor to consider, as it directly impacts the model's utility for anything beyond simple, straightforward tasks. However, this weakness is counterbalanced by its primary strength: cost. On Google AI Studio, the model is entirely free, with a price of $0.00 per million input and output tokens.
The trade-offs continue with its speed and verbosity. Gemma 3n E2B is 'notably slow,' with a median output speed of just under 50 tokens per second. This rate can feel sluggish in interactive applications and will significantly extend the duration of batch processing jobs. For comparison, many leading models operate at several hundred tokens per second. On the other hand, the model is fairly concise. During our intelligence evaluation, it generated 12 million tokens, slightly below the class average of 13 million. This conciseness can be an advantage, providing more direct answers without unnecessary filler. Its latency, or time-to-first-token (TTFT), is a respectable 0.37 seconds, meaning it begins responding quickly even if the full response is generated slowly.
Ultimately, Gemma 3n E2B is a specialized tool defined by its limitations as much as its strengths. It's a classic case of getting what you pay for—in this instance, a free model with corresponding performance. With a generous 32k token context window and a very recent knowledge cutoff of July 2024, it's best suited for non-critical, low-throughput workloads where cost is the absolute priority. Developers can leverage it for prototyping, academic research, or internal tools for simple tasks like basic summarization or data tagging, but should look to more powerful models for any production-grade or customer-facing applications.
11 (#44 / 55)
49.9 tokens/s
$0.00 / 1M tokens
$0.00 / 1M tokens
12M tokens
0.37 seconds
| Spec | Details |
|---|---|
| Model Name | Gemma 3n E2B Instruct |
| Owner | |
| License | Gemma Terms of Use (Open, Commercial Use Permitted) |
| Architecture | Transformer-based |
| Model Size | Small-scale (Implied by '3n' designation) |
| Context Window | 32,768 tokens |
| Modalities | Input: Text, Image; Output: Text |
| Knowledge Cutoff | July 2024 |
| Tuning | Instruction-Tuned |
| Intended Use | Research, prototyping, simple non-critical tasks |
| Primary Platform | Google AI Studio |
Choosing a provider for Gemma 3n E2B is straightforward, as our benchmarks are based on its primary, first-party endpoint: Google AI Studio. This centralizes access and simplifies the decision-making process, as the core trade-offs of the model are tied directly to this single provider option.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Cost-Free Experimentation | Google AI Studio | It's the only benchmarked provider and offers the model for free, making it the default choice for any use case. | Vendor lock-in and the model's inherent performance limitations are non-negotiable. |
| Prototyping New Ideas | Google AI Studio | Zero financial risk makes it ideal for testing concepts, validating prompts, and building simple application logic. | The prototype's performance (especially speed) will not be representative of paid, production-grade models. |
| Academic Research | Google AI Studio | Free access to a multi-modal model with a large context window is a boon for academic projects with limited budgets. | Research findings on model capability will reflect a low-tier model and may not generalize to more powerful ones. |
| Simple, Non-Critical Tasks | Google AI Studio | The model's capabilities and cost align perfectly with basic, asynchronous tasks where cost is the only factor. | Unsuitable for any task requiring high accuracy, nuance, or real-time speed. |
Note: Provider analysis is based on data from Google AI Studio. As an open-weight model, Gemma 3n E2B may become available on other platforms, but performance and pricing will vary and are not reflected in this analysis.
While Gemma 3n E2B's price is $0.00 on Google AI Studio, understanding token consumption for typical tasks is still valuable. It helps in capacity planning and in estimating the potential 'time cost' due to slow generation. The costs below are all $0.00, but they illustrate the token counts for common scenarios.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Email Summarization | 1,500 token email thread | 150 token summary | A common productivity task for internal use. | $0.00 |
| Basic Document Q&A | 2,000 token document + 50 token question | 100 token answer | Simple information retrieval from a provided text. | $0.00 |
| Image Captioning | 1 image (~250 tokens) + 10 token prompt | 25 token caption | A basic multi-modal task for asset management. | $0.00 |
| Simple Code Generation | 100 token description of a function | 150 tokens of Python code | A developer assistance task for a simple utility. | $0.00 |
| Batch Data Tagging | 10,000 items, 200 tokens each (2M total) | 10,000 labels, 5 tokens each (50k total) | A larger, non-interactive job where speed is not a primary concern. | $0.00 |
The key takeaway is that financial cost is not a factor with Gemma 3n E2B on Google AI Studio. The real 'cost' is time and performance. A batch job with millions of tokens might take hours or days to complete, and the quality of the output will be lower than that of paid models. This model is for scenarios where 'free' outweighs 'fast' and 'accurate'.
With a price of zero, the cost playbook for Gemma 3n E2B shifts from managing a budget to managing performance expectations and development time. The goal is to strategically leverage its free access for tasks where its significant limitations are acceptable. Success depends on careful task selection and building resilient application logic.
The model's slow generation speed makes it unsuitable for real-time, interactive use cases. Instead, build it into workflows where a delay is acceptable.
The model's low intelligence score means it cannot be trusted with complex, nuanced, or high-stakes work. Reserve it for tasks where 'good enough' is sufficient and errors have minimal impact.
Leverage the zero cost to validate ideas, learn prompt engineering, and build minimum viable products without burning a budget. It's a risk-free sandbox for AI development.
When using the model in any system that a user might interact with, even indirectly, you must protect against its slowness. Failure to do so will result in a poor user experience.
Gemma 3n E2B is a small, open-weight language model from Google's Gemma family. It is instruction-tuned for following prompts and is notable for its multi-modal capabilities, accepting both text and image inputs to generate text outputs. It's designed to be a highly accessible, free-to-use model for research and experimentation.
While Google has not provided an official breakdown, the naming likely follows an internal convention. '3n' probably refers to the model's parameter size class (e.g., in the 3 billion parameter range), and 'E2B' could denote a specific training configuration, version, or capability set. 'Instruct' signifies that it has been fine-tuned to follow user commands.
Yes. Based on our latest analysis of the Google AI Studio provider, the model is available at no cost, with $0.00 pricing for both input and output tokens. This is subject to Google's terms of service and usage limits, and the pricing could change in the future.
The two primary drawbacks are its performance. First, it has a very slow output speed of approximately 50 tokens per second, which is not suitable for real-time applications. Second, it has a low intelligence score, meaning it struggles with complex reasoning, nuance, and difficult instructions, leading to a higher rate of errors or unhelpful responses.
Gemma 3n E2B is one of the smaller and less powerful models in the Gemma family. Larger versions, such as Gemma 7B, and future, more advanced models in the series offer significantly better performance in both speed and intelligence, though typically at a financial cost. This model represents the entry-level, cost-focused tier of the Gemma ecosystem.
The model is released under the Gemma Terms of Use, which permits commercial use. However, its significant performance limitations—particularly its slow speed and low accuracy—make it a poor choice for most production-grade, customer-facing applications. It is far better suited for internal tools, background processes, research, and prototyping where performance is not a critical factor.