AI Models
AI models predict based on their training data. They can work in any domain such as numbers, text or multimedia.
Intelligence Density of 71 LLMs for Smarter & Denser Models
We tracked 71 LLMs released between February 2023 and May 2026 and collected 10 public benchmarks to measure intelligence density. We divided the capability score by the resource the model consumes (active parameters, training compute, and inference price). To calculate intelligence density, we executed the following steps: See methodology for the scoring approach, and per-resource…
50+ ChatGPT Use Cases with Real Life Examples
ChatGPT reached approximately 1 billion weekly active users in early 2026 roughly 10% of the world’s population.2 OpenAI surpassed $20 billion in annual revenue for 2025, confirmed by CFO Sarah Friar.23 The Anthropic Economic Index distinguishes two modes of use: augmentation, in which a human interacts with AI, and automation, in which AI completes tasks…
Benchmark of 40+ LLMs in Finance: Claude Fable 5 & GPT-5.6 Sol
We evaluated LLMs on 238 hard questions from the FinanceReasoning benchmark (Tang et al.).39 This subset targets the most challenging financial-reasoning tasks, assessing complex, multi-step quantitative reasoning involving financial concepts and formulas. Our evaluation employed a custom prompt design and scoring criteria of accuracy and token consumption. For a detailed explanation of how these metrics…
Time Series Foundation Models: Use Cases & Benefits
Time series foundation models (TSFMs) are pre-trained models that forecast, classify, impute, and detect anomalies in time series data without requiring a separate model for every dataset or industry. TSFMs use transformer-based architectures and large-scale time-series datasets to generalize across domains such as finance, retail, energy, and healthcare. Discover the architecture, use cases, adoption in…
AIM Enterprise: Agentic Enterprise Benchmark
Enterprises use LLMs everyday for their regular tasks. To find most cost efficient LLMs, we designed AIM Enterprise, an agentic enterprise benchmark, where we used 69 enterprise tasks in different categories. Each model delivered one file per task. Scores are relative: the judges rank every answer against the other answers to the same task, so…
LLM Scaling Laws: Analysis from AI Researchers
Large language models predict the next token based on patterns learned from text data. The term LLM scaling laws refers to empirical regularities that link model performance to the amount of compute, training data, and model parameters used during training. To understand how these relationships influence modern model design in practice, we reviewed findings from…
LLM Observability Tools: Weights & Biases, Langsmith
LLM applications have expanded from single-turn chats into multi-step agents that use tools, query databases, and coordinate with other models, making their behavior harder to interpret. LLM observability provides continuous visibility into these complex workflows, helping organizations monitor quality, detect failures, troubleshoot issues, and manage performance and costs. W&B Weave is Weights & Biases‘ LLM…
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.…
ChatGPT for Customer Service: Top 10 Use Cases
ChatGPT has moved from novelty to infrastructure in customer service. Companies are using it to cut response times, handle volume their teams can’t absorb, and reduce the cost of routine interactions. But results vary sharply depending on how it’s implemented. OpenAI launched GPT-5.6, a materially more capable model that is better at instruction-following, reasoning across…
Time Series Classification Benchmark: Foundation Models vs Classical Methods
We benchmarked 13 time series classification methods, from pretrained time series foundation models to a 22-feature baseline from 2019, on 33 UCR/UEA datasets under one frozen protocol. That is 14,638 recorded method-dataset-resample cells, 11,874 of them scored. The chart shows the benchmark’s main comparison: 12 methods on the 15 univariate datasets where every one of…