LLM Use Cases, Analyses & Benchmarks
LLMs are AI systems trained on vast text data to understand, generate, and manipulate human language for business tasks. We benchmark performance, use cases, cost analyses, deployment options, and best practices to guide enterprise LLM adoption.
Explore LLM Use Cases, Analyses & Benchmarks
Text-to-SQL: Comparison of LLM Accuracy in 2026
I have relied on SQL for data analysis for 18 years, beginning in my days as a consultant. Translating natural-language questions into SQL makes data more accessible, allowing anyone, even those without technical skills, to work directly with databases.
Top 5 AI Gateways for OpenAI: OpenRouter Alternatives
The increasing number of LLM providers complicates API management. AI gateways simplify this by serving as a unified access point, allowing developers to interact with multiple providers through a single API.
LLM Parameters: GPT-5 High, Medium, Low and Minimal
New LLMs, such as OpenAI’s GPT-5 family, come in different versions (e.g., GPT-5, GPT-5-mini, and GPT-5-nano) and with various parameter settings, including high, medium, low, and minimal. Below, we explore the differences between these model versions by gathering their benchmark performance and the costs to run the benchmarks. Price vs.
LLM Latency Benchmark by Use Cases in 2026
The effectiveness of large language models (LLMs) is determined not only by their accuracy and capabilities but also by the speed at which they engage with users. We benchmarked the performance of leading language models across various use cases, measuring their response times to user input.
LLM Scaling Laws: Analysis from AI Researchers in 2026
Large language models are usually trained as neural language models that predict the next token in natural language. The term LLM scaling laws refers to empirical regularities that link model performance to the amount of compute, training data, and model parameters used when training models.
LLM Pricing: Top 15+ Providers Compared in 2026
LLM API pricing can be complex and depends on your preferred usage. We analyzed 15+ LLMs and their pricing and performance: Hover over model names to view their benchmark results, real-world latency, and pricing, to assess each model’s efficiency and cost-effectiveness. Ranking: Models are ranked by their average position across all benchmarks.
Large Language Model Training in 2026
While using existing LLMs in enterprise workflows is table stakes, leading enterprises are building their custom models. However, building custom models can cost millions and require investing in an internal AI team.
LLM Fine-Tuning Guide for Enterprises in 2026
Follow the links for the specific solutions to your LLM output challenges. If your LLM: The widespread adoption of large language models (LLMs) has improved our ability to process human language. However, their generic training often results in suboptimal performance for specific tasks.
Supervised Fine-Tuning vs Reinforcement Learning in 2026
Can large language models internalize decision rules that are never stated explicitly? To examine this, we designed an experiment in which a 14B parameter model was trained on a hidden “VIP override” rule within a credit decisioning task, without any prompt-level description of the rule itself.
LLM VRAM Calculator for Self-Hosting in 2026
The use of LLMs has become inevitable, but relying solely on cloud-based APIs can be limiting due to cost, reliance on third parties, and potential privacy concerns. That’s where self-hosting an LLM for inference (also called on-premises LLM hosting or on-prem LLM hosting) comes in.