Top AI Models Q1 2025

AI Model Specifications

When you see "13B (Size) on 5.6T tokens (Training Data)", it means:

• 13B: 13 billion parameters (think of these as the AI's "brain cells")
• 5.6T: 5.6 trillion tokens of training data (each token ≈ 4 characters)

📊 Understanding the Benchmarks

• HLE (Humanity's Last Exam): Designed as the most difficult closed-ended academic exam for AI. Aims to rigorously test models at the frontier of human knowledge, as benchmarks like MMLU are becoming too easy (~90%+ scores for top models). Consists of 2,500 questions across >100 subjects from ~1000 experts. Current top models score ~20%, highlighting the gap to human expert level.
• MMLU-Pro: Advanced version of MMLU focusing on expert-level knowledge. Currently considered the most reliable indicator of model capabilities.
• MMLU: Tests knowledge across 57 subjects with a 90% theoretical ceiling and 9% error rate.
• GPQA: PhD-level science benchmark across biology, chemistry, physics, and astronomy. Has a 74% ceiling, with 20% error rate. Notable: even scientists only agree on ~78% of answers.

These models represent the current state-of-the-art in AI language technology (General Purpose Frontier Models).

Performance Milestones

As of Q1 2025, the theoretical performance ceilings were:

• GPQA: 74%
• MMLU: 90%
• HLE: 20%

These ceilings were notably surpassed:

• GPT-03 achieved 87.7% on GPQA
• OpenAI's o1 model surpassed both benchmarks in Q3 2025[¹]

In Q1 2025, these ceilings were surpassed with OpenAI's o1 model[¹].

Access & Details

For detailed information on each model, including:

• Technical specifications
• Use cases
• Access procedures
• Deployment guidelines

Please refer to our Models Access page.

Note: Performance metrics and rankings are based on publicly available data and may evolve as new models and evaluations emerge.

[1]: AI Research Community. "Language Model Leaderboard." Google Sheets, 2025. https://docs.google.com/spreadsheets/d/1kc262HZSMAWI6FVsh0zJwbB-ooYvzhCHaHcNUiA0_hY/