AI Models
AI models predict based on their training data. They can work in any domain such as numbers, text or multimedia.
World Foundation Models: 10 Use Cases
Training robots and autonomous vehicles (AVs) in the physical world can be costly, time-consuming and risky. World Foundation Models offer a scalable alternative by enabling realistic simulations of real-world environments. These models accelerate development and deployment in robotics, AVs, and other domains by reducing reliance on physical testing. Explore how World Foundation Models work, their…
LLM VRAM Calculator for Self-Hosting
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,…
LLM Pricing: Top 15+ Providers Compared
LLM pricing spans four orders of magnitude: the cheapest models launched under $0.03 per million tokens, while frontier reasoning tiers launched at up to $262.50. The chart below tracks launch prices: each point is the average price of the models one size class launched in a calendar quarter, blended 3 parts input to 1 part…
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…
LLM Orchestration: 22 Frameworks and Gateways
Optimizing LLM orchestration is key to improving performance while keeping resource use under control. To evaluate how different orchestration approaches perform in practice, we benchmarked: Discover selected LLM orchestration tools, including developer frameworks and enterprise gateways: LLM Orchestration involves managing and integrating multiple Large Language Models (LLMs) to perform complex tasks efficiently. It ensures smooth…
AIM Enterprise: Agentic Enterprise Benchmark
Enterprises use LLMs every day for their regular tasks. To find the most cost-efficient LLMs, we designed AIM Enterprise, an agentic enterprise benchmark, where we used 69 real enterprise tasks across strategy, marketing, HR, sales, and operations. Two judge models scored every file, and they often disagreed. On about a third of the individual scores…
Text-to-SQL: Comparison of LLM Accuracy
We ran 36 large language models over 759 questions from BIRD-SQL, each model writing SQL against a database it had to identify for itself out of 11 candidates. Every parseable query was executed against the real database and its result set compared with the result set of BIRD’s gold query. Missing, malformed and execution-failing queries…
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 compares 12 methods on the 15 univariate datasets every one of them completed, each dataset run…
LLM Parameters: GPT-5 High, Medium, Low and Minimal
Some 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. We used the GPT-5 family in our…
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…