Artificial Analysis: Independent Benchmarks for AI Models and API Providers

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Artificial Analysis offers independent performance and cost evaluations for language models, coding agents, and multimodal APIs. The release of Intelligence Index v4.2 updates testing suites and weightings for frontier models.

Key points

  1. Intelligence Index v4.2 incorporates ten evaluation suites, including AA-Briefcase, GDP.pdf, Terminal-Bench v2.1, and Humanity's Last Exam.
  2. GPQA Diamond was removed from the index, while AA-LCR was upgraded to v1.1 and SciCode grading was revised.
  3. The platform measures intelligence alongside output speed (tokens per second) and weighted cost per task across prompt, cache, reasoning, and answer tokens.
  4. Dedicated benchmarks evaluate coding agents, image and video models, speech interfaces, and industry-specific domains such as finance.
  5. Recently evaluated models include Claude Fable 5.1, GPT-6 Astra, Muse Spark 1.3, and Gemini 3.8 Flash.

Why it matters

Evaluating AI models based purely on vendor claims is challenging for teams deploying LLMs in production. Standardised third-party data on task intelligence, generation speed, and token economics enables technical leaders to make data-driven infrastructure decisions.

Analysis

The methodology changes in v4.2 highlight an industry shift away from static multiple-choice tests towards long-horizon, agentic evaluations. By factoring in specific token types—such as cache read/write and reasoning overhead—the index provides a realistic cost-to-performance frontier for developers balancing quality with operating expenses.

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Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.

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