EXAONE 4.0 32B (Non-reasoning)

Since the model tends to be verbose and output tokens are expensive, explicitly instructing the model to be concise can yield significant savings. Add phrases to your prompts like:

• "Provide a brief, one-paragraph summary."
• "Answer in three sentences or less."
• "Be direct and concise."

This reduces the number of output tokens you pay for and can also improve response speed.

The 131k context window is powerful but expensive to fill. Instead of re-sending conversation history with every turn, maintain the state in a single, long-running context. For document analysis, batch multiple questions about the same document into a single API call rather than making separate calls for each question.

However, always be mindful of the input cost. Only provide the context that is absolutely necessary for the task at hand to avoid unnecessary expense.

Many applications receive repetitive queries. Implementing a caching layer (e.g., using Redis) to store the results of common prompts can dramatically reduce API calls. For any incoming request, first check if an identical or semantically similar request exists in your cache. Serving a cached response avoids the cost and latency of a new API call entirely.

In some cases, you may need a detailed internal representation but only a short final answer for the user. You can let the model generate a longer, more detailed response (e.g., a chain-of-thought process) and then programmatically extract or summarize the final answer in your application logic. This gives you the benefit of the model's full capabilities while controlling what the end-user sees, though you still pay for the full generation.

EXAONE 4.0 32B is a 32-billion parameter large language model developed by LG AI Research. It is an open-weight model optimized for knowledge-intensive, non-reasoning tasks. It is distinguished by its high performance on intelligence benchmarks and its very large 131,072-token context window.

This model is ideal for developers and organizations that require top-tier performance for tasks like long-document summarization, advanced RAG-based Q&A, and high-quality content generation. Users must be prepared to handle its premium pricing, making it best suited for high-value applications where quality and deep context are more important than minimizing cost.

The "Non-reasoning" tag indicates that the model is not primarily designed for complex, multi-step logical deduction or mathematical problem-solving. Instead, its strengths lie in retrieving, processing, and generating text based on the vast knowledge encoded in its parameters and the context provided in the prompt.

A 131k token context window allows the model to consider a massive amount of information—equivalent to a small book—in a single request. This is a game-changer for applications like:

• Analyzing lengthy legal contracts or financial reports.
• Creating chatbots that remember an entire long conversation.
• Performing Retrieval-Augmented Generation (RAG) with extensive source material for highly accurate answers.

The high price is likely a combination of several factors. First, the resources required to train a high-quality 32B parameter model with a large context window are immense. Second, its high intelligence score positions it as a premium product. Finally, the provider, FriendliAI, sets a price that reflects the value of this performance and the cost of serving such a large model efficiently via their optimized inference engine.

It's a mixed bag. The time-to-first-token (latency) of 0.38 seconds is good, providing a responsive initial feel. However, its output speed of 88 tokens/s is slightly below average. For short responses, it will feel fast enough. For longer, more detailed generations, users might notice a slight delay compared to faster models. The high cost per token is also a major consideration for a high-volume chat service.