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 69 LLMs: Smarter or More Efficient?
We tracked 69 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).
AI Gateways for OpenAI: OpenRouter Alternatives
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.
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.
Text-to-SQL: Comparison of LLM Accuracy
I have relied on SQL for data analysis for 18 years, beginning in 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.
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.
Benchmark of 40+ LLMs in Finance: Claude Fable 5 & GPT-5
We evaluated 40+ LLMs in finance on 238 hard questions from the FinanceReasoning benchmark to identify which models excel at complex financial reasoning tasks like statement analysis, forecasting, and ratio calculations. LLM finance benchmark overview We evaluated LLMs on 238 hard questions from the FinanceReasoning benchmark (Tang et al.).
Compare Multimodal AI Models on Visual Reasoning
We benchmarked 15 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.
Compare Large Vision Models: GPT-4o vs YOLOv8n
Large vision models (LVMs) can automate and improve visual tasks such as defect detection, medical diagnosis, and environmental monitoring. We benchmarked three object detection models: YOLOv8n, DETR, and GPT-4o Vision, across 1,000 images each, measuring metrics such as mAP@0.5, inference speed, FLOPs, and parameter count.
Large Language Models in Cybersecurity
We evaluated 7 large language models across 9 cybersecurity domains using SecBench, a large-scale and multi-format benchmark for security tasks. We tested each model on 44,823 multiple-choice questions (MCQs) and 3,087 short-answer questions (SAQs), covering data security, identity & access management, network security, vulnerability management, and cloud security.
HALC-Bench: LLM Hallucination on Long-Context Retrieval Benchmark
HALC-Bench (LLM Hallucination on Long-Context Retrieval Benchmark) measures a large language model’s resistance to fabricating evidence for a metric that does not exist in the target document by using 3 haystacks placed at the beginning, middle, and end of the model’s context window, with 204 questions. Results gpt-5.