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
LLM Pricing: Top 15+ Providers Compared
We analyzed 15+ LLMs and their pricing and performance. LLM API pricing can be complex and depends on your preferred usage. If you plan to use: Hover over model names to view their benchmark results, real-world latency, and pricing, to assess each model’s efficiency and cost-effectiveness.
Compare Multimodal AI Models on Visual Reasoning
We benchmarked 8 leading multimodal AI models on visual reasoning using 98 visual-based questions. The evaluation consisted of two tracks: 70 Chart Understanding questions testing data visualization interpretation, and 28 Visual Logic questions assessing pattern recognition and spatial reasoning. Visual reasoning benchmark See our benchmark methodology to learn our testing procedures.
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.
Relational Foundation Models: SAP vs. Gradient Boosting
We benchmarked SAP-RPT-1-OSS against gradient boosting (LightGBM, CatBoost) on 17 tabular datasets spanning the full semantic-numeral spectrum, small/high-semantic tables, mixed business datasets, and large low-semantic numerical datasets.
10+ Large Language Model Examples & Benchmark
We have used open-source benchmarks to compare top proprietary and open-source large language model examples. You can choose your use case to find the right model. 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 how quickly they respond to user input.
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.
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.
The LLM Evaluation Landscape: 16 Frameworks by Functionality
We spent 2 days reviewing popular LLM evaluation frameworks that provide structured metrics, logs, and traces to identify how and when a model deviates from expected behavior.
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. We benchmarked OpenRouter, SambaNova, TogetherAI, Groq, and AI/ML API as AI gateways since they provide unified API access to multiple models.