LG EXAONE Deep vs DeepSeek R1: AI Reasoning Models Compared

Introduction

The landscape of artificial intelligence reasoning models has become increasingly competitive, with multiple organizations claiming breakthrough performance on complex mathematical and logical reasoning tasks. LG’s recent release of EXAONE Deep, a 32-billion parameter reasoning model, generated significant attention with claims of outperforming established competitors like DeepSeek R1. However, real-world testing reveals a more nuanced picture than marketing claims suggest. This article provides an in-depth analysis of EXAONE Deep’s actual performance compared to other leading reasoning models, examining the gap between claimed benchmarks and practical functionality. Through hands-on testing and detailed comparison, we’ll explore what these models can actually do, how they handle complex reasoning tasks, and what this means for organizations considering these tools for production use.

Understanding AI Reasoning Models and Test-Time Decoding

The emergence of reasoning models represents a fundamental shift in how artificial intelligence approaches complex problem-solving. Unlike traditional language models that generate responses in a single forward pass, reasoning models employ a technique called test-time decoding, which allocates significant computational resources during inference to think through problems step-by-step. This approach mirrors human reasoning, where we often need to work through multiple angles of a problem before arriving at a solution. The concept gained prominence with OpenAI’s o1 model and has since been adopted by multiple organizations including DeepSeek, Alibaba, and now LG. These models generate what’s called a “thinking” or “reasoning” token sequence that users typically don’t see in the final output, but which represents the model’s internal deliberation process. The thinking tokens are crucial because they allow the model to explore different solution paths, catch errors, and refine its approach before committing to a final answer. This is particularly valuable for mathematical problems, logical reasoning tasks, and complex multi-step scenarios where a single pass through the problem might miss important details or lead to incorrect conclusions.

Why Reasoning Models Matter for Enterprise AI Deployment

For organizations implementing AI systems, reasoning models represent a significant advancement in reliability and accuracy for complex tasks. Traditional language models often struggle with multi-step mathematical problems, logical deduction, and scenarios requiring careful analysis of constraints and conditions. Reasoning models address these limitations by explicitly showing their work, which also provides transparency into how the model arrived at its conclusion. This transparency is particularly important in enterprise settings where decisions based on AI recommendations need to be auditable and explainable. The trade-off, however, is computational cost and latency. Because reasoning models generate extensive thinking tokens before producing a final answer, they require more processing power and take longer to respond compared to standard language models. This makes model selection critical—organizations need to understand not just benchmark scores but actual real-world performance on their specific use cases. The proliferation of reasoning models from different vendors, each claiming superior performance, makes independent testing and comparison essential for making informed deployment decisions.

LG’s EXAONE Deep: Claims vs. Reality

LG’s entry into the reasoning model space with EXAONE Deep generated considerable interest, particularly given the company’s significant research capabilities and the model’s relatively modest 32-billion parameter size. LG’s marketing materials presented impressive benchmark results, claiming that EXAONE Deep achieved 90% accuracy on the AIME (American Invitational Mathematics Examination) competition with just 64 attempts, and 95% on MATH-500 problems. These numbers, if accurate, would represent performance competitive with or exceeding DeepSeek R1 and Alibaba’s QwQ models. The company also released multiple versions of the model in different sizes, including a 2.4-billion parameter variant designed for use as a draft model in speculative decoding—a technique that uses smaller models to predict tokens that larger models will generate, potentially speeding up inference. However, when subjected to practical testing on standard reasoning problems, EXAONE Deep exhibited concerning behavior that contradicts the benchmark claims. The model demonstrated a tendency to enter extended thinking loops without reaching logical conclusions, generating thousands of tokens that appeared to be repetitive or nonsensical rather than productive reasoning. This behavior suggests potential issues with the model’s training, the benchmark evaluation methodology, or how the model handles certain types of prompts.

The Ice Cube Problem: A Critical Test Case

To understand the practical differences between reasoning models, consider a seemingly simple problem that has become a standard test for reasoning model quality: “Beth places some whole ice cubes in a pan. After one minute, there are 20 ice cubes. After two minutes, there are 10 ice cubes. After three minutes, there are 0 ice cubes. How many whole ice cubes can be found in the pan at the end of the third minute?” The correct answer is zero, as the question explicitly asks about whole ice cubes at the end of the third minute, and the problem states there are zero at that point. However, this problem is designed to trick models that overthink it or get confused by the melting ice cube narrative. Some models might reason that ice cubes melt over time and try to calculate melting rates, leading them astray from the straightforward answer. When EXAONE Deep was tested on this problem, it generated approximately 5,000 tokens of thinking without arriving at a coherent conclusion. The model’s reasoning process appeared to go off the rails, with the generated text becoming increasingly incoherent and failing to demonstrate logical problem-solving. The tokens generated included fragments that didn’t form complete thoughts, and the model never clearly articulated a reasoning path or final answer. This performance stands in stark contrast to how the problem should be handled—a reasoning model should recognize the trick, work through the logic clearly, and arrive at the answer efficiently.

Comparative Performance: EXAONE Deep vs. DeepSeek R1 vs. QwQ

When the same ice cube problem was tested on DeepSeek R1 and Alibaba’s QwQ model, both demonstrated significantly better performance. DeepSeek R1 generated a clear thinking process, worked through the problem methodically, and arrived at the correct answer of zero. The model’s reasoning was transparent and logical, showing how it considered the problem, recognized the trick, and settled on the correct answer. QwQ similarly demonstrated strong performance, though it also generated an extended thinking process. Interestingly, QwQ initially considered whether ice cubes might take time to melt and whether the problem was asking about physics or mathematics, but it ultimately arrived at the correct answer. The key difference was that both models showed coherent reasoning throughout their thinking process, even when exploring multiple angles. They demonstrated the ability to recognize when they had sufficient information to answer the question and to commit to a final answer. EXAONE Deep, by contrast, never reached this point. The model continued generating tokens without apparent purpose, never settling on an answer or demonstrating clear logical progression. This suggests fundamental issues with how the model handles reasoning tasks, despite the impressive benchmark claims.

Understanding Speculative Decoding and Model Optimization

One interesting technical aspect of EXAONE Deep’s release is the inclusion of multiple model sizes designed to work together through speculative decoding. The 2.4-billion parameter version can serve as a draft model that predicts tokens the larger 32-billion parameter model will generate. When the draft model’s predictions align with the main model’s generation, the system can skip the main model’s computation and use the draft prediction, effectively speeding up inference. This is a sophisticated optimization technique that can significantly reduce latency and computational requirements. In testing, the speculative decoding implementation showed green tokens indicating successful draft predictions, suggesting the technique was working as intended. However, this optimization doesn’t address the fundamental issue of the main model’s reasoning quality. Faster inference of poor reasoning is still poor reasoning. The presence of this optimization feature also raises questions about whether LG’s benchmark results might have been achieved using configurations or techniques that don’t translate well to real-world usage patterns.

FlowHunt’s Approach to AI Model Evaluation and Automation

For organizations struggling to evaluate and compare multiple AI models, FlowHunt provides a comprehensive automation platform that streamlines the testing and benchmarking process. Rather than manually running tests on different models and comparing results, FlowHunt enables teams to set up automated workflows that systematically evaluate model performance across multiple dimensions. This is particularly valuable when comparing reasoning models, where performance can vary significantly based on problem type, complexity, and specific prompt formulation. FlowHunt’s automation capabilities allow teams to test models against standardized problem sets, track performance metrics over time, and generate comprehensive comparison reports. The platform can integrate with multiple model providers and APIs, making it possible to evaluate models from different vendors within a single unified workflow. For teams considering deployment of reasoning models like EXAONE Deep, DeepSeek R1, or QwQ, FlowHunt provides the infrastructure to make data-driven decisions based on actual performance rather than vendor claims. The platform’s ability to automate repetitive testing tasks also frees up engineering resources to focus on integration and optimization rather than manual benchmarking.

The Importance of Independent Testing and Verification

The gap between EXAONE Deep’s claimed performance and its actual behavior in testing highlights a critical lesson for AI adoption: vendor benchmarks should always be verified through independent testing. Benchmark results can be influenced by numerous factors including the specific test set used, the evaluation methodology, the hardware configuration, and the model’s inference parameters. A model might perform well on a particular benchmark while struggling with other types of problems or real-world scenarios. This is why organizations like Weights & Biases and independent researchers play such an important role in the AI ecosystem—they provide unbiased testing and analysis that helps the community understand what models can actually do. When evaluating reasoning models for production deployment, organizations should conduct their own testing on representative problem sets from their specific domain. A model that excels at mathematical reasoning might struggle with logical deduction or code generation. The ice cube problem, while seemingly simple, serves as a useful diagnostic test because it reveals whether a model can handle trick questions and avoid overthinking. Models that fail on such problems are likely to struggle with more complex reasoning tasks as well.

Technical Issues and Potential Causes

The extended thinking loops observed in EXAONE Deep testing could stem from several potential issues. One possibility is that the model’s training process didn’t adequately teach it when to stop thinking and commit to an answer. Reasoning models require careful calibration during training to balance the benefits of extended thinking against the risks of overthinking and generating unproductive tokens. If the training process didn’t include sufficient examples of when to stop, the model might default to generating tokens until it hits a maximum limit. Another possibility is that the model’s prompt handling has issues, particularly with how it interprets certain types of questions or instructions. Some models are sensitive to specific prompt formulations and might behave differently depending on how a question is phrased. The fact that EXAONE Deep generated incoherent token sequences suggests the model might be entering a state where it’s generating tokens without meaningful semantic content, which could indicate issues with the model’s attention mechanisms or token prediction logic. A third possibility is that the benchmark evaluation methodology used different configurations or prompting strategies than what was used in the real-world testing, leading to a significant performance gap between reported and actual results.

Implications for the Reasoning Model Market

The performance issues observed with EXAONE Deep have broader implications for the reasoning model market. As more organizations release reasoning models, the market risks becoming saturated with models that have impressive benchmark claims but questionable real-world performance. This creates a challenging environment for organizations trying to select models for production use. The solution is increased emphasis on independent testing, standardized evaluation methodologies, and transparency about model limitations. The reasoning model space would benefit from industry-wide standards for how these models are evaluated and compared, similar to how other AI benchmarks have evolved. Additionally, organizations should be cautious about models that claim to significantly outperform established competitors, particularly when the performance gap seems inconsistent with the model’s architecture or training approach. DeepSeek R1 and QwQ have both demonstrated consistent performance across multiple testing scenarios, which provides confidence in their capabilities. EXAONE Deep’s inconsistent performance—excellent benchmark claims but poor real-world results—suggests either issues with the model itself or with how the benchmarks were conducted.

Best Practices for Evaluating Reasoning Models

Organizations considering deployment of reasoning models should follow a structured evaluation process. First, establish a representative test set that includes problems from your specific domain or use case. Generic benchmarks might not reflect how the model will perform on your actual problems. Second, test multiple models on the same problems to enable direct comparison. This requires standardizing the testing environment, including hardware, inference parameters, and prompt formulation. Third, evaluate not just accuracy but also efficiency metrics like latency and token generation. A model that generates correct answers but requires 10,000 thinking tokens might not be practical for production use if you need real-time responses. Fourth, examine the model’s reasoning process, not just the final answer. A model that arrives at the correct answer through flawed reasoning might fail on similar problems with different parameters. Fifth, test edge cases and trick questions to understand how the model handles scenarios designed to confuse it. Finally, consider the total cost of ownership, including not just the model’s license or API costs but also the computational resources required for inference and the engineering effort needed for integration.

The Role of Model Size and Efficiency

EXAONE Deep’s 32-billion parameter size is notably smaller than some competing reasoning models, which raises questions about whether the model’s issues stem from insufficient capacity. However, model size alone doesn’t determine reasoning capability. QwQ, which also operates in a similar parameter range, demonstrates strong reasoning performance. This suggests that EXAONE Deep’s issues are more likely related to training methodology, architecture design, or inference configuration rather than fundamental limitations of the model size. The inclusion of a 2.4-billion parameter draft model in EXAONE Deep’s release shows that LG is thinking about efficiency, which is commendable. However, efficiency gains are only valuable if the underlying model produces correct results. A fast wrong answer is worse than a slow correct answer in most production scenarios. The reasoning model market will likely see continued emphasis on efficiency as organizations seek to deploy these models at scale, but this optimization must not come at the expense of reasoning quality.

Future Directions for Reasoning Models

The reasoning model space is still in its early stages, and we can expect significant evolution in the coming months and years. As more organizations release reasoning models and more independent testing occurs, the market will likely consolidate around models that demonstrate consistent, reliable performance. Organizations like DeepSeek and Alibaba have established credibility through consistent performance, while newer entrants like LG will need to address the performance issues observed in testing to gain market acceptance. We can also expect continued innovation in how reasoning models are trained and evaluated. The current approach of generating extensive thinking tokens is effective but computationally expensive. Future models might develop more efficient reasoning mechanisms that achieve similar accuracy with fewer tokens. Additionally, we’ll likely see increased specialization, with reasoning models optimized for specific domains like mathematics, code generation, or logical reasoning. The integration of reasoning models with other AI techniques, such as retrieval-augmented generation or tool use, will also expand their capabilities and applicability.

Conclusion

LG’s EXAONE Deep represents an ambitious entry into the reasoning model market, but real-world testing reveals significant gaps between the model’s claimed performance and its actual capabilities. While the model’s benchmark results suggest competitive performance with DeepSeek R1 and Alibaba’s QwQ, practical testing on standard reasoning problems demonstrates that EXAONE Deep struggles with basic tasks, generating excessive tokens without reaching coherent conclusions. DeepSeek R1 and QwQ both demonstrated superior performance on the same problems, arriving at correct answers through clear, logical reasoning processes. For organizations evaluating reasoning models for production deployment, this analysis underscores the critical importance of independent testing and verification. Vendor benchmarks should be treated as starting points for evaluation rather than definitive measures of model capability. The reasoning model market will benefit from increased transparency, standardized evaluation methodologies, and continued independent testing by the research community. As this technology matures, organizations that invest in rigorous model evaluation and comparison processes will be better positioned to select and deploy reasoning models that deliver genuine value for their specific use cases.