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
Compare Top 12 LLM Orchestration Frameworks
Leveraging multiple LLMs concurrently demands significant computational resources, driving up costs and introducing latency challenges. In the evolving landscape of AI, efficient LLM orchestration is essential for optimizing performance while minimizing expenses. Explore key strategies and tools for managing multiple LLMs effectively.
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.
LLM Parameters: GPT-5 High, Medium, Low and Minimal
New LLMs, such as OpenAI’s GPT-5 family, come with different versions (e.g., GPT-5, GPT-5-mini, and GPT-5-nano) and various parameters, including high, medium, low, and minimal. Below, we explore the differences between these versions of the models by gathering their benchmark performances and the costs to run these benchmarks. Price vs.
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.
Top 5 AI Gateways for OpenAI: OpenRouter Alternatives
The growing number of LLM providers creates significant API management hurdles. AI gateways address this complexity by acting as a central routing point, enabling developers to interact with multiple providers through a single, unified API, thereby simplifying development and maintenance.
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.
Large Language Models in Cybersecurity
Large language models (LLMs) are increasingly applied across cybersecurity domains, including threat intelligence, vulnerability detection, anomaly analysis, and red teaming. These applications are supported by both specialized cybersecurity LLMs and general-purpose models.
Audience Simulation: Can LLMs Predict Human Behavior?
In marketing, evaluating how accurately LLMs predict human behavior is crucial for assessing their effectiveness in anticipating audience needs and recognizing the risks of misalignment, ineffective communication, or unintended influence.
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.
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.