A highly capable and verbose open-weight model from LG AI Research, offering top-tier intelligence for its size class at an exceptionally competitive price point.
Exaone 4.0 1.2B (Reasoning) is a small-yet-mighty language model developed by LG AI Research. As part of the broader Exaone family, this model represents a significant effort to pack advanced capabilities into a compact, efficient, and accessible package. The "Reasoning" variant is specifically tuned for tasks that require logical deduction, problem-solving, and multi-step thinking, setting it apart from more generalized text generation models. Despite its relatively small parameter count of 1.2 billion, it punches well above its weight, demonstrating that thoughtful architecture and high-quality training data can rival the performance of much larger models in specific domains.
In our standardized testing, Exaone 4.0 1.2B achieves an impressive score of 27 on the Artificial Analysis Intelligence Index. This places it at rank #4 out of 30 comparable models, a remarkable feat that positions it firmly in the top tier. The class average for intelligence is just 14, meaning Exaone more than doubles the typical performance. This high score indicates a strong aptitude for understanding complex prompts, following instructions, and generating coherent, logically sound responses. It suggests that for developers needing a reliable reasoning engine without the overhead of a 70B+ parameter model, Exaone is a formidable contender.
One of the most striking features of this model is its pricing structure. With an API cost of $0.00 per million input tokens and $0.00 per million output tokens, it is, by our metrics, the most affordable model in its class. This pricing suggests that the model is intended for free use via self-hosting or through specific research and partnership programs by LG. This effectively removes the per-token cost barrier, making it an incredibly attractive option for startups, researchers, and developers on a tight budget. The primary cost consideration shifts from API calls to the operational expenses of hosting and inference, a trade-off many are willing to make for this level of performance.
However, this cost-effectiveness comes with a notable characteristic: high verbosity. During our intelligence evaluation, the model generated 71 million tokens, a figure that dwarfs the class average of 10 million. This means Exaone tends to provide extremely detailed, comprehensive, and sometimes loquacious answers. While this can be a significant advantage for tasks like report generation or detailed explanations, it can also be a drawback for applications requiring concise, to-the-point responses. This verbosity, combined with a generous 64,000-token context window, makes the model well-suited for deep analysis of long documents but requires careful prompt engineering to control output length for other use cases.
27 (4 / 30)
N/A tokens/sec
0.00 USD per 1M tokens
0.00 USD per 1M tokens
71M tokens
N/A seconds
| Spec | Details |
|---|---|
| Model Name | Exaone 4.0 1.2B |
| Variant | Reasoning |
| Owner | LG AI Research |
| License | Open (Specifics of the license should be verified from the source) |
| Parameters | ~1.2 Billion |
| Context Window | 64,000 tokens |
| Input Modalities | Text |
| Output Modalities | Text |
| Architecture | Transformer-based Decoder-only |
| Primary Language | English (with multilingual capabilities) |
| Release Date | Unspecified in provided data |
| Intended Use | Reasoning, Q&A, Summarization, Text Generation |
As an open-weight model with a listed price of $0.00, Exaone 4.0 1.2B is not typically offered through traditional pay-as-you-go API providers. Instead, "providers" are the platforms and methods you use to host the model yourself. The best choice depends on your team's technical expertise, budget for infrastructure, and scalability requirements.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Cost-Effectiveness | On-Premise Server | If you already own suitable hardware (especially GPUs), this is the cheapest long-term option as you only pay for power and maintenance. | Requires significant upfront capital investment if you don't own hardware, plus dedicated expertise for setup and maintenance. |
| Balanced Choice | Cloud VM (e.g., AWS, GCP, Azure) | Offers a balance of control and flexibility. You can choose the exact GPU instance (like an A10G or T4) that fits your performance and budget needs, and scale up or down as required. | Can be complex to configure and manage. Costs can become unpredictable if usage spikes, and you are responsible for all software setup. |
| Ease of Use | Managed Inference Service (e.g., Hugging Face, Replicate) | These platforms handle all the infrastructure complexity. You can often deploy a model like Exaone with a few clicks, providing an API endpoint automatically. | This is the most expensive hosting option, as you pay a premium for the convenience. You also have less control over the underlying hardware. |
| Scalability | Kubernetes on Cloud | For high-demand production applications, deploying the model on a Kubernetes cluster provides maximum scalability, resilience, and automated management. | This is the most complex and engineering-intensive approach, requiring deep expertise in both MLOps and cloud-native infrastructure. |
Note: The choice of hosting will directly impact the model's real-world latency and throughput. Performance benchmarks are recommended on your target infrastructure before committing to a production deployment.
To understand how Exaone 4.0 1.2B performs in practice, let's examine several real-world scenarios. These examples highlight how its intelligence, large context window, and high verbosity play out. The estimated cost for all scenarios is $0.00 in API fees, but remember to factor in your own compute and hosting costs.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Document Analysis & Summary | A 15-page PDF research paper (~7,500 words, ~10k tokens) is provided as input. | The model produces a detailed 3-page summary (~1,500 words, ~2k tokens) covering methodology, findings, and limitations. | Demonstrates the 64k context window's ability to handle long documents and the model's capacity for detailed, structured output. | $0.00 |
| Code Explanation | A 300-line Python script (~1.5k tokens) with complex logic is provided. | The model generates a line-by-line explanation with examples, totaling over 800 words (~1.1k tokens). | Highlights the model's reasoning capabilities applied to code, and its tendency towards high verbosity and detail. | $0.00 |
| Customer Support Email Triage | An angry customer email (~300 tokens) is input with a request to classify intent and draft a reply. | The model correctly identifies the issue and drafts a very polite, thorough, and empathetic response of 400 words (~550 tokens). | Shows its utility in customer-facing roles, but the verbose output might need to be manually shortened by an agent. | $0.00 |
| Brainstorming Session | A simple prompt: "Brainstorm five unique marketing angles for a new eco-friendly water bottle." (~20 tokens). | The model provides five distinct angles, each with a detailed paragraph explaining the target audience, messaging, and potential channels (~700 tokens). | A great example of its creative and reasoning abilities, where verbosity is a clear advantage for generating rich ideas. | $0.00 |
Exaone 4.0 1.2B excels at tasks where detail and thoroughness are valued. Its large context window is a key asset for document-heavy workloads. However, for applications needing quick, concise answers, its high verbosity requires active management through careful prompting or post-processing to avoid overly long outputs.
While Exaone 4.0 1.2B is nominally "free" at the API level, real-world costs are driven by the compute infrastructure required to run it. Managing these operational costs is key to leveraging the model effectively. The primary goals are to optimize hardware utilization and control the model's verbose nature to reduce generation time.
The most direct way to manage compute time is to manage output length. Since Exaone is naturally verbose, your prompts must be explicit about the desired output format and length.
Choosing the right hardware and software stack is critical for cost-effective inference. A misconfigured environment can lead to wasted resources and high bills.
Reduce redundant computations by intelligently managing how you process requests. This is especially important for applications with repetitive queries.
Exaone 4.0 1.2B (Reasoning) is an open-weight language model with approximately 1.2 billion parameters, developed by LG AI Research. This specific variant has been fine-tuned to excel at tasks requiring logical deduction, problem-solving, and multi-step thinking.
It performs exceptionally well. In our testing, it scored 27 on the Artificial Analysis Intelligence Index, placing it #4 out of 30 comparable models and well above the class average of 14. This suggests it is one of the most capable reasoning models at this parameter scale.
The model weights are released under an open license, and benchmarked API providers list the price as $0.00. This means there are no per-token fees to use the model's logic. However, you are responsible for the costs of hosting it, which includes server infrastructure (preferably with a GPU), electricity, and maintenance. So, while the software is free, running it is not.
It means the model tends to generate very long, detailed, and comprehensive answers by default. In our tests, it produced over seven times more text than the average model. This can be an advantage for tasks like report generation but may require careful prompt engineering to get concise answers for other applications, like chatbots or quick data extraction.
Given its strengths, Exaone 4.0 1.2B is ideal for:
A 64,000-token context window is very large, equivalent to roughly 48,000 words or about 100 single-spaced pages of text. This is a significant advantage because it allows the model to process and 'remember' vast amounts of information in a single prompt, making it perfect for summarizing long reports, analyzing entire codebases, or maintaining long, coherent conversations without losing track of earlier details.