An open-weight model from LG AI Research, offering strong intelligence for its size at an unbeatable price point, ideal for experimentation and production use where cost is paramount.
Exaone 4.0 1.2B (Non-reasoning) is a small language model developed by LG AI Research, representing a significant entry in the open-weight model landscape. Despite its relatively small size of 1.2 billion parameters, it punches well above its weight class in terms of raw intelligence. With a score of 20 on the Artificial Analysis Intelligence Index, it surpasses the average score of 13 for comparable models, making it a surprisingly capable choice for a variety of text generation and comprehension tasks.
The model's most striking feature is its price: it is completely free to use. Both input and output tokens are priced at $0.00 per million, an aggressive strategy that removes the primary barrier to entry for developers, researchers, and businesses. This makes Exaone 4.0 an exceptional candidate for projects with tight budgets, academic research, rapid prototyping, or applications where usage costs must be minimized. The open license further enhances its appeal, granting users the freedom to modify, deploy, and scale the model as they see fit, without restrictive terms.
However, potential users should be mindful of its characteristics. The "Non-reasoning" designation suggests that it may be less suited for complex, multi-step logical problems compared to models explicitly trained for reasoning tasks. Furthermore, our analysis shows it is somewhat verbose, generating 10 million tokens during intelligence testing compared to the 6.7 million average. While not necessarily a negative, this verbosity can impact perceived speed and may require careful prompt engineering to elicit concise responses. The model also features a generous 64k context window, allowing it to process and maintain context over long documents, a feature not always present in models of this size.
The primary challenge with Exaone 4.0 is not the model itself, but the ecosystem around it. As performance metrics like latency and output speed are not yet available, the real-world user experience will depend heavily on the chosen hosting provider or self-hosting infrastructure. For teams willing to manage deployment, Exaone 4.0 offers a powerful combination of intelligence, a large context window, and zero licensing cost, positioning it as a compelling alternative to proprietary APIs for a wide range of applications.
20 (5 / 22)
N/A tokens/sec
$0.00 per 1M tokens
$0.00 per 1M tokens
10M tokens
N/A seconds
| Spec | Details |
|---|---|
| Model Name | Exaone 4.0 1.2B (Non-reasoning) |
| Owner | LG AI Research |
| License | Open (Specifics of the license should be verified) |
| Parameters | ~1.2 Billion |
| Context Window | 64,000 tokens |
| Modalities | Text-only |
| Architecture | Transformer-based |
| Primary Training | General text and code corpora |
| Specialization | General text generation (Non-reasoning) |
| Release Status | Publicly available |
Since Exaone 4.0 1.2B is free, the choice of 'provider' shifts from cost optimization to a balance of performance, convenience, and operational cost. There are no benchmarked third-party providers yet, so the decision revolves around deployment strategy. Your best option depends on whether your priority is zero cost, maximum performance, or ease of use.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Lowest Cost | Free-Tier Provider | Find a service offering a free tier for open-weight models. This provides API access with zero financial outlay, perfect for hobby projects and validation. | Often comes with strict rate limits, usage caps, and potential performance throttling or 'cold starts'. Not suitable for production. |
| Max Performance | Self-Hosting (Dedicated GPU) | Deploying the model on your own powerful GPU hardware gives you full control over performance, latency, and throughput. | Highest upfront cost for hardware and significant ongoing operational expense (power, cooling, maintenance). Requires deep technical expertise. |
| Balanced Approach | Managed Open-Source Provider | Services that specialize in hosting open models provide a ready-to-use API without the hassle of managing infrastructure. | These services are typically not free. You are paying for convenience and performance, re-introducing a cost factor. |
| Scalability | Cloud ML Platform (e.g., SageMaker, Vertex AI) | Leverage a major cloud provider's infrastructure to deploy the model for on-demand, auto-scaling inference. | Can be complex to configure and pricing can be unpredictable. You pay for compute time, which can become expensive at scale. |
Note: Provider performance and pricing for Exaone 4.0 are not yet benchmarked. These picks represent general strategies for deploying open-weight models. The optimal choice will depend on your specific technical resources and application requirements.
Exaone 4.0's combination of zero cost and strong intelligence makes it a workhorse for foundational NLP tasks. The following examples illustrate its cost-effectiveness in common scenarios. The 'Estimated Cost' reflects only the model's API price; it does not include hosting, infrastructure, or engineering costs which will be the primary expense.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Article Summarization | Input: 3,000 tokens (~2250 words) | Output: 300 tokens (~225 words) | Condensing news articles or blog posts for a content aggregation service. | $0.00 |
| Customer Support Email Triage | Input: 500 tokens | Output: 50 tokens | Classifying an incoming support email into categories like 'Billing', 'Technical Issue', or 'Sales Inquiry'. | $0.00 |
| Data Extraction from Text | Input: 1,500 tokens | Output: 100 tokens | Pulling structured information (names, dates, locations) from an unstructured report. | $0.00 |
| Creative Writing Brainstorming | Input: 100 tokens | Output: 1,000 tokens | Generating plot ideas or character descriptions based on a short prompt. | $0.00 |
| RAG-based Q&A | Input: 4,000 tokens (query + context) | Output: 250 tokens | Answering a user question based on a provided document snippet. | $0.00 |
For any application that can be run on a 1.2B-parameter model, Exaone 4.0 reduces the marginal cost of inference to zero. The entire financial model shifts to managing fixed and operational costs of the underlying compute infrastructure.
While the model itself is free, total cost of ownership is not. The key to managing expenses with Exaone 4.0 lies in optimizing the infrastructure and operational patterns around it. Use these strategies to keep your total costs low while maximizing the value of this free, open-weight model.
If you choose to self-host, your main cost is hardware. You can control this by:
For applications with intermittent or unpredictable traffic, a constantly running dedicated server is wasteful. Consider serverless GPU platforms:
Exaone 4.0 is more verbose than average. While this doesn't have a direct API cost, it has indirect costs: it consumes more compute time per request and can lead to a slower user experience. Mitigate this through prompt engineering:
Before committing to your own infrastructure, explore the ecosystem of platforms that offer free tiers for hosting open-weight models. These are excellent for:
Exaone 4.0 1.2B is a small language model (SLM) with approximately 1.2 billion parameters, created by LG AI Research. It is an open-weight model, meaning its weights are publicly available, and it is free to use. It is designed for general-purpose text generation and features a large 64k token context window.
The "Non-reasoning" designation suggests that this model was not specifically trained or fine-tuned for tasks that require complex, multi-step logical deduction, mathematical problem-solving, or intricate planning. While it possesses strong general intelligence for language tasks, it may be less reliable for applications that heavily depend on pure reasoning capabilities compared to models explicitly labeled for that purpose.
Exaone 4.0 1.2B competes in the same class as other popular small models. Its key differentiators are its high intelligence score for its size and its completely free pricing model. Its 64k context window is also very competitive. However, performance benchmarks for speed and latency are not yet available, which would be a critical point of comparison against well-benchmarked models like those from Microsoft or Google.
Yes, the model itself is free. LG AI Research has priced API usage at $0.00. However, the total cost of using the model is not zero. You must account for the cost of the infrastructure (hardware, electricity) to run it yourself or the fees charged by a third-party service that hosts the model for you. The cost has been shifted from the IP (the model) to the operations (the compute).
Given its strengths, it is ideal for cost-sensitive applications requiring good language understanding and generation. Top use cases include: content summarization, first-line customer support bots, data extraction and classification, creative writing assistance, and as the generation component in a Retrieval-Augmented Generation (RAG) system, where its large context window is a major asset.
LG AI Research is the artificial intelligence research hub of the South Korean multinational conglomerate LG Group. They focus on developing advanced AI technologies, including large-scale language and multi-modal models, with the goal of applying them across various industries and creating a positive impact.