# AA-LCR ![Category](https://img.shields.io/badge/Category-Long%20Context%20Reasoning-1f6feb) ![Metric](https://img.shields.io/badge/Metric-%25%20Accuracy-2da44e) ![Saturation](https://img.shields.io/badge/Saturation-Low-d29922) ![Created](https://img.shields.io/badge/Created-2026-555) [![Website](https://img.shields.io/badge/Website-artificialanalysis.ai-0969da)](https://artificialanalysis.ai) > **Quick answer:** AA-LCR (Artificial Analysis Long Context Reasoning) is Artificial Analysis's benchmark for evaluating multi-step reasoning and synthesis across very long input documents, part of their broader Intelligence Index suite used to independently compare frontier models. Kimi K3 scores 74.7% as of July 2026. Note: this is a distinct benchmark from Benchgen's general "Long Context Reasoning" page (based on Google DeepMind's Michelangelo benchmark). ## At a Glance **What it tests:** A model's ability to retain, retrieve, and reason across information scattered throughout very long input contexts, rather than relying on a single localized passage. **Why it matters:** As context windows have grown into the hundreds of thousands or millions of tokens, raw retrieval is no longer the bottleneck — multi-hop reasoning across that context is. AA-LCR specifically targets this harder capability, and being run by an independent evaluator (Artificial Analysis) makes cross-model comparisons more trustworthy. **Known limitations:** As with other Artificial Analysis benchmarks, detailed task construction methodology is published on their website rather than in an academic paper, so it should be treated as an industry-standard third-party evaluation rather than a peer-reviewed benchmark. ## What AA-LCR Measures AA-LCR evaluates a model's ability to perform multi-step reasoning and information synthesis across very long input documents — testing whether a model can connect facts, resolve references, and draw conclusions using information distributed across a large context window, rather than simply retrieving a single fact. It is maintained by Artificial Analysis as part of their independent, standardized model evaluation suite alongside benchmarks like their Intelligence Index and AA-Briefcase. ## Benchmark Specifications | Field | Value | |---|---| | Task category | Reasoning / long context | | Metric | % accuracy | | Saturation | Low | | Created by | Artificial Analysis | | Website | [artificialanalysis.ai](https://artificialanalysis.ai) | ## How AA-LCR Is Scored Models answer questions or complete synthesis tasks that require reasoning across information spread throughout a long input document, scored on % accuracy against Artificial Analysis's reference answers. ## State-of-the-Art Results | Rank | Model | Score | Source | Date | |---|---|---|---|---| | 1 | [Kimi K3](/models/moonshot/kimi-k3) | 74.7% | [Kimi K3 technical report](https://www.kimi.com/blog/kimi-k3) | 2026-07 | *Score sourced from Moonshot AI's Kimi K3 announcement, July 2026.* ## AA-LCR on Benchgen > No Benchgen results yet — [be the first to run AA-LCR](/benchmarks/artificial-analysis/aa-lcr). ## AA-LCR vs Other Benchmarks | Benchmark | What it tests | Saturation | |---|---|---| | **AA-LCR** | Long-context multi-step reasoning (Artificial Analysis) | Low | | [Long Context Reasoning](/benchmarks/google-deepmind/long-context-reasoning) | Long-context reasoning (Michelangelo, Google DeepMind) | Low | | [MRCR v2](/benchmarks/google-deepmind/mrcrv2) | Multi-round co-reference resolution | Low | | [GDPval-AA v2](/benchmarks/artificial-analysis/gdpval-aa-v2) | Economically valuable task performance (Artificial Analysis) | Low | ## Run AA-LCR on Your Model Benchgen lets you run AA-LCR-style long-context reasoning evaluations against your own model, tracking accuracy as you scale context window usage in production. ## Frequently Asked Questions

What is AA-LCR?

AA-LCR (Artificial Analysis Long Context Reasoning) is a benchmark from Artificial Analysis testing multi-step reasoning and synthesis across very long input documents.

What does a good score look like on AA-LCR?

Kimi K3 reports 74.7% as of July 2026, a strong result for long-context multi-hop reasoning among frontier models.

Who created AA-LCR?

AA-LCR is created and maintained by Artificial Analysis, an independent AI model evaluation and analysis platform.

Is AA-LCR the same as Benchgen's "Long Context Reasoning" benchmark?

No. Benchgen's separate "Long Context Reasoning" benchmark page is based on Google DeepMind's Michelangelo benchmark. AA-LCR is Artificial Analysis's own distinct long-context reasoning evaluation.

Rank	Model	Score
1	muse-glimmer	80
2	kimi-k3	74.7
3	solar-pro-4	71
4	nemotron-3-5-lightning-30b-a3b	49.2

AA-LCR — Results

AA-LCR — Evaluations