LLM Use Cases, Analyses &amp; Benchmarks

Jul 12

Self-hosting an LLM means running inference on hardware the operator controls rather than via a third-party API, which changes the cost, data control, and privacy profile. Whether a model runs at all depends on memory. The calculator estimates the VRAM or unified memory a model needs to run locally, based on the model, its precision,…

Jul 10

Benchmark of 40+ LLMs in Finance: Claude Fable 5 & GPT-5.6 Sol

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. We evaluated LLMs on 238 hard questions from the FinanceReasoning benchmark (Tang et al.).33 This subset targets the most challenging financial-reasoning tasks, assessing complex,…

Jul 10

LLM Automation: Top 7 Tools & 8 Case Studies

LLM automation refers to shift to intelligent automation tools that leverage LLMs, including AI agents, fine-tuned LLMs and RAG models to automate and coordinate tasks. Explore what LLM automation is, its top real-life applications and major tools: Large language models in automation is a systematic approach that combines Natural Language Processing (NLP) with existing process…

Jul 8

LLM Latency Benchmark by Use Cases in 2026

We benchmarked 11 top large language models with a total of 1,320 requests, splitting reasoning and non-reasoning models, and measured first-token latency, per-token latency, and overall response time. You can find details on how we measured latency here. We report reasoning and non-reasoning models separately. Reasoning models spend several seconds thinking before the first visible…

Jul 7

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. claude-fable-5 answered all 204 traps correctly at every haystack…

Jul 7

Intelligence Density of 71 LLMs: Smarter and 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…

Jul 6

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.41 OpenAI surpassed $20 billion in annual revenue for 2025, confirmed by CFO Sarah Friar.43 The Anthropic Economic Index distinguishes two modes of use: augmentation, in which a human interacts with AI, and automation, in which AI completes tasks…

Jul 2

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. See our benchmark methodology…

Jul 2

LLM Market Share: Compare Usage & Adoption

We analyzed LLM market share by combining usage-based data and web visit estimates to show how demand for large language models is distributed across AI labs and AI applications: Read the methodology to see how we measured and calculated these results. The United States dominated web visits across all four months, consistently accounting for 85.5–90.5%.…