LLM Benchmarks
One transparent Intelligence Index combining public benchmarks with AIMultiple's own agentic, RAG and enterprise-reasoning evaluations.
Leaderboard
The highest-scoring models across all benchmarks.
# | Model | Index | LegalBench | FinanceReasoning | Text-to-SQL | Agentic RAG | ARC-AGI-2 | Agentic LLM Benchmark | FrontierMath | Swe-Bench |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 |  Claude Fable 5 Anthropic | 92 | 89 | 90 | 90 | 98 | - | 69 | - | - |
| 2 |  Kimi K3 Moonshot AI | 88 | 86 | 88 | 77 | 93 | - | 73 | - | - |
| 3 |  GPT-5.6 Sol OpenAI | 87 | 87 | 90 | 74 | 87 | 93 | 62 | - | - |
| 4 |  Grok 4.5 X | 86 | 86 | 88 | 79 | 83 | - | 73 | - | - |
| 5 | GPT-5.5 OpenAI | 83 | 87 | - | - | - | 85 | 59 | - | - |
| 6 | GPT-5.6 Terra OpenAI | 80 | 85 | 87 | 71 | 91 | 84 | 61 | - | - |
| 7 | GPT-5.6 Sol Pro OpenAI | 80 | - | 91 | 79 | 96 | - | 54 | - | - |
| 8 | Claude Opus 4.6 Anthropic | 78 | - | 88 | 68 | 80 | 69 | 72 | - | - |
| 9 |  Gemini 3 Pro Preview Google | 77 | 87 | 86 | 60 | 89 | 31 | - | - | - |
| 10 | Gemini 3.1 Pro Preview Google | 77 | 87 | 87 | 65 | 89 | 77 | 46 | 27 | - |
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Charts
Following "Best Performing" models
How The Index is Built
The Intelligence Index normalizes each benchmark to a 0-100 scale and averages a model's relative standing across the benchmarks it was evaluated on. Each benchmark below feeds that score.
Recent Updates
Latest changes to the Intelligence Index, model coverage and AIMultiple benchmark methodology.
Kimi K3
New model added to the AIMultiple Intelligence Index.
GPT-5.6 Sol
New model added to the AIMultiple Intelligence Index.
GPT-5.6 Terra
New model added to the AIMultiple Intelligence Index.
GPT-5.6 Sol Pro
New model added to the AIMultiple Intelligence Index.
Explore LLM Use Cases, Analyses & Benchmarks
LLM Pricing: Top 15+ Providers Compared
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Text-to-SQL: Comparison of LLM Accuracy
I have relied on SQL for data analysis for 18 years, beginning in my days as a consultant. Translating natural-language questions into SQL makes data more accessible, allowing anyone, even those without technical skills, to work directly with databases. We used our text-to-SQL benchmark methodology on 35+ large language models (LLMs) to assess their performance…
LLM Fine-Tuning Guide for Enterprises
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LLM Observability Tools: Weights & Biases, Langsmith
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LLM VRAM Calculator for Self-Hosting
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Benchmark of 40+ LLMs in Finance: Claude Fable 5 & GPT-5.6 Sol
We evaluated 40+ LLMs in finance on 238 hard questions from the FinanceReasoning benchmark to identify which models excel at complex financial reasoning tasks like statement analysis, forecasting, and ratio calculations. We evaluated LLMs on 238 hard questions from the FinanceReasoning benchmark (Tang et al.).81 This subset targets the most challenging financial-reasoning tasks, assessing complex,…
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…
LLM Latency Benchmark by Use Cases in 2026
We benchmarked 11 top large language models with a total of 1,320 requests, splitting reasoning and non-reasoning models, and measured first-token latency, per-token latency, and overall response time. 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…
HALC-Bench: LLM Hallucination on Long-Context Retrieval Benchmark
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…
Intelligence Density of 71 LLMs: Smarter and 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…