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.
Github Stars of Open-Source Multimodal Models
Analyzed 2021–2025 growth of open-source multimodal models like LLaVA, CLIP, and CogVLM.
Cost comparison of AI gateways
Compared AI gateway costs for Llama 4 Scout using 1M input/output tokens.
First token latency comparison of AI gateways
Benchmarked AI gateways with 50 short and long prompts, successful runs only.
Text-to-SQL Benchmark
Benchmarked 24 LLMs on converting questions to SQL, assessing accuracy and common errors.
Explore LLM Use Cases, Analyses & Benchmarks
Large Multimodal Models (LMMs) vs LLMs
We evaluated the performance of Large Multimodal Models (LMMs) in financial reasoning tasks using a carefully selected dataset. By analyzing a subset of high-quality financial samples, we assess the models’ capabilities in processing and reasoning with multimodal data in the financial domain. The methodology section provides detailed insights into the dataset and evaluation framework employed.
10+ Large Language Model Examples & Benchmark
We have used open-source benchmarks to compare top proprietary and open-source large language model (LLM) examples. You can choose your use case to find the right model for it. Comparison of the most popular large language models We have developed a model scoring system based on three key metrics: user preference, coding, and reliability.
LLM Latency Benchmark by Use Cases
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 responsiveness to user input.
LLM VRAM Calculator for Self-Hosting
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.
Benchmark 30 Finance LLMs: GPT-5, Gemini 2.5 Pro & more
Large language models (LLMs) are transforming finance by automating complex tasks such as risk assessment, fraud detection, customer support, and financial analysis. Benchmarking finance LLM can help identify the most reliable and effective solutions.
Text-to-SQL: Comparison of LLM Accuracy
I have been relying on SQL for data analysis for 18 years, beginning with 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.
Large Language Model Evaluation: 10+ Metrics & Methods
Large Language Model evaluation (i.e., LLM eval) refers to the multidimensional assessment of large language models (LLMs). Effective evaluation is crucial for selecting and optimizing LLMs. Enterprises have a range of base models and their variations to choose from, but achieving success is uncertain without precise performance measurement.
Large Language Models: Complete Guide
Large language models (LLMs) have generated much hype in recent months (see Figure 1). The demand has led to the ongoing development of websites and solutions that leverage language models. ChatGPT set the record for the fastest-growing user base in January 2023, proving that language models are here to stay.
Compare 10+ LLMs in Healthcare
Large language models (LLMs) are increasingly being applied in healthcare to support clinical tasks such as medical question answering, patient communication, and summarizing medical records.
Large Language Model Training
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.