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

Explore LLM Use Cases, Analyses & Benchmarks

HALC-Bench: LLM Hallucination on Long-Context Retrieval Benchmark

Benchmark

Sep 15

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…

Sep 2

LLM Observability Tools: Weights & Biases, Langsmith

LLM applications have expanded from single-turn chats into multi-step agents that use tools, query databases, and coordinate with other models, making their behavior harder to interpret. LLM observability provides continuous visibility into these complex workflows, helping organizations monitor quality, detect failures, troubleshoot issues, and manage performance and costs. W&B Weave is Weights & Biases‘ LLM…

Sep 1

LLM Fine-Tuning Guide for Enterprises

Follow the links for the specific solutions to your LLM output challenges. If your LLM: The widespread adoption of large language models (LLMs) has improved our ability to process human language. However, their generic training often results in suboptimal performance for specific tasks. To overcome this limitation, fine-tuning methods are employed to tailor LLMs to…

Aug 27

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…

Aug 19

ChatGPT for Customer Service: Top 10 Use Cases

ChatGPT has moved from novelty to infrastructure in customer service. Companies are using it to cut response times, handle volume their teams can’t absorb, and reduce the cost of routine interactions. But results vary sharply depending on how it’s implemented. OpenAI launched GPT-5.6, a materially more capable model that is better at instruction-following, reasoning across…

Benchmark

Aug 16

Intelligence Density of 71 LLMs for Smarter & 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…