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AI models predict based on their training data. They can work in any domain such as numbers, text or multimedia.

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Text-to-SQL Benchmark: SQL Accuracy Across 40+ LLMs

LLM
Benchmark
Sep 25

SQL accuracy is the percentage of scored queries that return the reference result. Incorrect routes and references flagged as broken are excluded. A reference query is the SQL supplied as the expected answer. Each model reaches a different set of SQL questions because scoring depends on its database choices. Differences in these subsets and execution…

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LLM
Insight
Sep 25

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 85–95%.…

AI Models
Benchmark
Sep 24

AIM-Decision: Jev vs Kev vs LLMs

Decision models, also called System One models,6 choose an agent’s next action in a single pass instead of generating text token by token. To see whether they can make browser automation cheaper than LLMs, we ran three decision models and two LLMs, Gemini 3.8 Flash and GPT-6 Astra, on the same 50 browser tasks, for…

LLM
Benchmark
Sep 24

Benchmark of 40+ LLMs in Finance: Claude Opus 5.5 & GPT-6 Astra

We evaluated LLMs on 238 hard questions from the FinanceReasoning benchmark (Tang et al.).12 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…

LLM
Benchmark
Sep 21

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…

LLM
Insight
Sep 21

Large Multimodal Models (LMMs) vs LLMs

Evaluate LLMs and LMMs by comparing their benchmark scores and real-world latency by clicking the model’s name in the table below. You can also weigh their input and output pricing to judge overall efficiency and value. *Audio is native on the E2B, E4B and 12B models only. Available in five sizes: E2B, E4B, 12B, 26B…

LLM
Insight
Sep 21

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
Benchmark
Sep 18

Agentic IT: Can AI Agents Design a Benchmark

We tested 16 models on a benchmark design in text-to-SQL and tool calling. Each model built one benchmark per topic, for a total of 32 submissions. No submission passed every rubric criterion. The agents could build and run tests, but none demonstrated both a blank-answer test and a correct-answer test of their own scorer. Text-to-SQL…

LLM
Benchmark
Sep 17

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 that passed the checks. On 32.7% of the individual scores the two…

LLM
Insight
Sep 17

The Future of Large Language Models

See the future of large language models by delving into promising approaches, such as self-training, fact-checking, and sparse expertise that could address LLM limitations. Success rate comparison of LLM’s Claude Sonnet 4.6 led the benchmark with an overall score of 0.748, with base and thinking variants tied to three decimal places. Claude Opus 4.8 (0.702),…

AI Models
Insight
Sep 15

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…