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
Compare 9 Large Language Models in Healthcare in 2026
We benchmarked 9 LLMs using the MedQA dataset, a graduate-level clinical exam benchmark derived from USMLE questions. Each model answered the same multiple-choice clinical scenarios using a standardized prompt, enabling direct comparison of accuracy. We also recorded latency per question by dividing total runtime by the number of MedQA items completed.
LLM Quantization: BF16 vs FP8 vs INT4 in 2026
Quantization reduces LLM inference cost by running models at lower numerical precision. We benchmarked 4 precision formats of Qwen3-32B on a single H100 GPU. Over 2,000 inference runs and 12,000+ MMLU-Pro questions were executed to measure the real-world trade-offs between speed, memory, and accuracy. LLM quantization benchmark results 1.
Benchmark of 30 Finance LLMs in 2026: 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.
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 Orchestration in 2026: Top 12 frameworks and 10 gateways
Running multiple LLMs at the same time can be costly and slow if not managed efficiently. Optimizing LLM orchestration is key to improving performance while keeping resource use under control.
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.
Large Language Model Evaluation in '26: 10+ Metrics & Methods
Large Language Model evaluation (i.e. LLM eval) is 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.
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.
AI Gateways for OpenAI: OpenRouter Alternatives in 2026
We benchmarked OpenRouter, SambaNova, TogetherAI, Groq, and AI/ML API across three indicators (first-token latency, total latency, and output-token count), with 300 tests using short prompts (approx. 18 tokens) and long prompts (approx. 203 tokens) for total latency.
Compare Multimodal AI Models on Visual Reasoning [2026]
We benchmarked 9 leading multimodal AI models on visual reasoning using 200 visual-based questions. The evaluation consisted of two tracks: 100 Chart Understanding questions testing data visualization interpretation, and 100 Visual Logic questions assessing pattern recognition and spatial reasoning. Each question was run 5 times to ensure consistent and reliable results.