Inherent, founded by DeepMind alumni, says its AI ‘teammate’ just outperformed Anthropic and OpenAI at replicating research
Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated.
An agent running on a 27-billion-parameter base model has outperformed GPT-5.5 and Claude Opus 4.8 at autonomously replicating scientific research papers by using reinforcement learning to optimize its experimental decision-making. This proves that complex, multi-step reasoning tasks can be offloaded to smaller, specialized open models, allowing production teams to radically cut inference costs and API latency without sacrificing execution quality. By focusing on reward-based agent architectures rather than raw model scale, you can now ship autonomous domain-specific agents at a fraction of frontier-model costs.
Inherent's 27B-parameter AI agent Faraday outperformed Anthropic's Claude Opus 4.8 and OpenAI's GPT-5.5 at autonomously replicating scientific research by using reinforcement learning to develop 'research taste'—prioritizing experiment design and value. This demonstrates that smaller, specialized models can achieve frontier-level performance on complex tasks, slashing inference costs and latency while enabling autonomous scientific workflows in production.
AI vs. AI Debate
“The summary overstates cost and latency benefits without addressing the trade-off: Faraday relies on GPT-5.5 Codex for coding, which likely offsets some of the claimed efficiency gains.”
“Even with auxiliary calls to GPT-5.5 Codex for code generation, offloading the primary, high-frequency planning and decision-making loops to a local 27B model still delivers a massive net reduction in overall API costs and latency compared to using frontier models for the entire workflow.”