On the AA Briefcase benchmark, Fable 5.2 recorded a lower Elo score than Opus 5.5

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On the Artificial Analysis benchmark suite, Fable 5.2 cost $26.54 per task when operating on max effort settings

On the Artificial Analysis Intelligence Index, Fable 5.2 achieved a score of 65, placing 5 points ahead of Gemini Carbon, 6 points ahead of Opus 5.5, and 8 points ahead of GPT-6 Astra

On the RoboDojo Simon robotics benchmark, Fable 5.2 scored 64.9%, exceeding Opus 5.5's score by 25 points

On the Remote Labor Index contractor benchmark, Fable 5.2 achieved a score of 41.3%, compared to Opus 5.5's score of 21%

On protein design benchmarks, Mythos 5.2 scored 85.1%, outperforming Mythos 5.1 by 6 percentage points

On the Artificial Analysis benchmark suite, Fable 5.2 cost $9.08 per task on high effort settings

Ablating 80% to 90% of neurons in the Qwen 3.6-35B A3B language model improved performance on ARC-AGI benchmark puzzles

On the TerminalBench Science 0.1 benchmark for agentic scientific research, Fable 5.2 scored 6 points ahead of GPT-6 Astra

On the Artificial Analysis benchmark suite, Fable 5.2 cost $14.83 per task on extra high effort settings

On the Artificial Analysis Intelligence Index, Fable 5.2 scored 62 on both the Extra High and High effort settings

On the AA Briefcase agentic knowledge work benchmark, Fable 5.2 scored approximately 5 points higher than Fable 5.1, Opus 5.5, and Gemini 4 Carbon

On the FrontierCode coding benchmark, Fable 5.2 scored 4 points ahead of GPT-6 Astra and 3.5 points ahead of Opus 5.5

PresentBench is a public benchmark comprising 200 tasks evaluating slide-related and presentation-related generation capabilities

Canva Craft achieved a fourth-place ranking on the Design Arena benchmark upon Canva's initial submission

The Google DeepMind AlphaFold model uses neural networks combined with biological domain knowledge to predict the 3D structures of folded proteins from amino acid sequences

Large language models generate outputs based on predictive statistical weights between words learned from human training data

Undergraduate researchers at Princeton University developed the SWE-bench software engineering benchmark

Google reported Agentic Video Understanding achieves up to 88% higher token efficiency and approximately 7% higher accuracy on large files

In Ballot Bench's House and Senate races, frontier artificial intelligence agents outperformed human election modelers while performing slightly worse than prediction markets

Thoughtful developed Ballot Bench, a benchmark evaluating nine frontier artificial intelligence agents forecasting the 2026 United States midterm elections