Most Expensive AI Models

Core AI Benchmarks Explained

Key Metrics to Consider

What is MMLU-Pro and why is it the standard AI intelligence benchmark?

MMLU-Pro (Massive Multitask Language Understanding - Professional) is the most comprehensive AI benchmark, testing models across 14 academic subjects including mathematics, science, history, law, and ethics. Scores range from 46% (basic competency) to 87% (near-expert level). Models scoring above 75% demonstrate strong general intelligence suitable for professional applications, while scores below 60% indicate limitations in complex reasoning tasks.

What does GPQA measure and which models score highest?

GPQA (Graduate-level Google-Proof Q&A) tests PhD-level reasoning with questions designed to be "Google-proof" - requiring deep understanding rather than simple fact retrieval. Top models like GPT-5.1 (87.3%), GPT-5 mini (82.8%), and o3 (82.7%) excel at GPQA, making them ideal for research, technical analysis, and complex problem-solving. Models below 50% GPQA struggle with advanced reasoning and may provide superficial answers to complex questions.

What is AIME 2025 and how does it evaluate AI mathematical ability?

AIME 2025 (American Invitational Mathematics Examination) is an elite math competition benchmark that tests advanced problem-solving, algebra, geometry, and number theory. Scores above 80% (like GPT-5 Codex at 98.7% or GPT-5.1 at 94%) indicate exceptional mathematical reasoning suitable for engineering, scientific computing, and quantitative analysis. Models scoring below 50% may struggle with multi-step mathematical problems or require explicit problem breakdown.

How is AI model pricing calculated and what's considered cost-effective?

AI model pricing is measured per 1 million tokens (approximately 750,000 words). Input pricing covers text you send, while output pricing covers generated responses. Budget models like Llama 3.3 70B cost $0.54/$0.71 per million tokens, mid-tier models like GPT-5 nano cost $0.05/$0.40, while premium models like GPT-5 cost $1.25/$10. For typical applications with 3:1 input-to-output ratio, budget models can be 10-20x cheaper than flagship models while maintaining 70-80% performance.

Which AI models are best for coding and programming tasks?

Sort by Coding Index to see top programming models. Our Coding Index combines LiveCodeBench, SciCode, and coding benchmarks. Top performers include GPT-5.1 (57.5 index), GPT-5 mini (51.4), and GPT-5 Codex (53.5). These models excel at code generation, debugging, refactoring, and explaining complex algorithms. For budget-conscious developers, models with 40+ coding index scores offer excellent value for routine programming tasks.

How often are AI model benchmarks and rankings updated?

Our leaderboard syncs daily with Artificial Analysis API to ensure benchmark scores (MMLU-Pro, GPQA, AIME 2025), pricing, and inference speed data reflect the latest model versions. New model releases appear immediately under the "Newest" sort option. Benchmark scores can change when providers release updated versions - for example, GPT-5.1 released in November 2025 achieved 69.7 intelligence compared to GPT-5's 68.5 from August 2025.

What inference speed (tokens/second) do I need for my application?

Inference speed determines how fast models generate responses. For real-time chatbots and interactive applications, target 100+ tokens/second (models like gpt-oss-120B at 340 tok/s). For background processing and batch jobs, 50-100 tok/s is sufficient. Premium reasoning models like GPT-5 (103 tok/s) balance speed and capability. Note that higher inference speed doesn't always mean better quality - slower models often deliver more thoughtful, detailed responses.

Can I test these AI models for free before committing?

Yes! Try our free AI chat interface to test different models instantly without creating an account. Many providers also offer free tiers: OpenAI (ChatGPT with daily limits), Anthropic (Claude with usage caps), Google (Gemini free tier), and open-source models like Llama 3.3. Compare performance on your specific use case before upgrading to paid plans.