AI Models
AI models predict based on their training data. They can work in any domain such as numbers, text or multimedia.
LLM Pricing: Top 15+ Providers Compared
LLMFeature ComparisonNov 5We track the launch prices of more than 160 LLMs, from GPT-3.5 Turbo in March 2023 to the latest releases, grouped by size class. We also compare current models’ API prices with their benchmark results and review the subscription plans of the major providers. Prices are per million tokens, blended three parts input to one…
- LLMInsightNov 5
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 90–95%.…
- LLMBenchmarkNov 5
LLM Latency Benchmark by Use Cases
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 answer, so comparing them directly with non-reasoning models on latency would be misleading. Some models also change behavior by task: GPT-5.2 answers Q&A without reasoning but reasons on…
- System OneBenchmarkNov 4
AIM-Decision: Jev vs Clef vs LLMs
Decision models, also called System One models, score a fixed set of options instead of generating text token by token. We tested 62 decision models on 1,655 classification questions, such as rating a security flaw or a product recall, and ran 49 of them on 50 browser tasks. See AIM System One Index for combined…
- LLMFeature ComparisonNov 3
Compare 9 Large Language Models in Healthcare
We benchmarked 9 LLMs using the MedQA dataset, a graduate-level clinical exam benchmark derived from USMLE questions. Each model answered the same multiple-choice clinical scenarios using a standardized prompt, enabling direct comparison of accuracy. We also recorded latency per question by dividing total runtime by the number of MedQA items completed. Benchmark methodology: This benchmark…
- LLMBenchmarkNov 3
Benchmark of 90+ LLMs in Finance: Claude Opus 5.5 & Gemini 4 Argon
The test set is the hard subset of the FinanceReasoning benchmark (Tang et al.), with 238 questions. Accuracy is the percentage of questions answered correctly. Numerical answers receive a 0.2% relative tolerance. Output tokens are the tokens generated across the 238 answers. Input tokens are counted separately in the cost calculation. Cost is the calculated…
- LLMBenchmarkNov 3
AIM-Text-to-SQL Benchmark: SQL Accuracy Across 70+ LLMs
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…
- LLMInsightNov 2
LLM Parameters: Types, Scaling, Fine-Tuning Guide
Parameter count is one of the most cited specifications of a large language model, but it does not determine how well a model performs or how much it costs to run. The chart below compares 31 models by their LLM Index score and their average cost per task across seven benchmarks. Four models form the…