AIM LLM Index

AIM-Agentic-RAGMulti-database routing & SQL query generation across 5 databases (BIRD-SQL, 500 questions).

AIM-AI-BiasPer model per question per repetition bias evaluation results across 64 questions in both multiple choice and open ended formats, 27 models in the multiple choice condition and 26 in the open ended condition

AIM-FinanceReasoningComplex multi-step financial reasoning across 238 hard FinanceReasoning questions.

AIM-Text-to-SQLNatural-language-to-SQL query generation accuracy across 35+ LLMs.

CritPtResearch-level physics problems requiring multi-step derivation.

FrontierMathUnpublished, expert-authored research math problems (Tiers 1–4).

MMMU-ProCollege-level multimodal understanding across disciplines, hardened against text-only shortcuts.

SciCodeResearch-level scientific coding across physics, math, biology & chemistry (338 subproblems)

Terminal-Bench 4.0The current Terminal-Bench generation: agentic terminal tasks scored by resolution rate.

Recent Updates

Latest changes to the AIM LLM Index, model coverage and AIMultiple benchmark methodology.

Oct 13, 2026

Google

Gemini 4 Argon

New model added to the AIM LLM Index.

Oct 12, 2026

Muse Wonder

New model added to the AIM LLM Index.

Oct 7, 2026

Anthropic

Claude Haiku 5.5

New model added to the AIM LLM Index.

Sep 29, 2026

OpenAI

GPT-6.1 Sol

New model added to the AIM LLM Index.

LLM Release TimelineThe pace of AI innovation

DeepSeek

DeepSeek V4 Flash Vision Exp

21 Aug

DeepSeek V4.1 Flash

10 Sep

DeepSeek V4.1 Flash 1017

17 Oct

Explore LLM Use Cases, Analyses & Benchmarks

LLM Orchestration: 22 Frameworks and Gateways

Open World Evaluation

Oct 19

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…

Benchmark

Oct 15

Compare Multimodal AI Models on Visual Reasoning

We benchmarked 40 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. See our benchmark methodology to learn our testing procedures. Every model scores lower on visual logic…

Oct 15

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…

10+ Large Language Model Examples

We have gathered open-source benchmarks to compare leading proprietary and open-source large language models. Choose your use case to find the right model. You can evaluate large language models by examining their benchmark performance and real-world latency (available by clicking each model’s name in the table), and by reviewing their pricing to assess overall efficiency…

Feature Comparison

Top LLMOps Tools & Compare them to MLOPs

LLMOps platforms handle the operational side of running large language models: deployment, monitoring, evaluation, and cost management. We examined top LLMOps tools, their core features, pricing models, and how they differ from each other to help identify the best fit for various use cases. A breakdown of each metric is provided below: LLMOps platforms support…

Benchmark

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…

Benchmark

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…

Compare GPT-5 Models: Reasoning, Speed & Cost

We explore the differences between OpenAI’s GPT-5 family (e.g., GPT-5, GPT-5-mini, and GPT-5-nano) by gathering benchmark performance and the costs to run the benchmarks. We used the GPT-5 family in our analysis. We used six benchmarks across various areas, including reasoning, coding, instruction-following, and math. Our analysis revealed: The three models differ primarily in reasoning…

Oct 13

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%.…

Oct 13

The Future of Large Language Models

See the future of large language models by comparing approaches, such as self-training, fact-checking, and sparse expertise, that could address LLM limitations. LLMs access external sources during conversations instead of relying on training data. The model queries external databases, retrieves current information, and provides citations. Limitation: Still makes errors. Citations don’t guarantee accuracy; models sometimes…