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Rohan Paul· @rohanpaul_ai · X· 2026-11-14 03:55 · Original publication time AI score54

Singularity-Bench Evaluates AI Agents on Six End-to-End Development TasksMachine translation

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Singularity-Bench sets six end-to-end tasks, including model pre-training, inference engines and cluster orchestration. Agents design and build systems within fixed budgets, with results compared against human-built systems on the same hardware. A quoted post says Opus 5.5 scored 1.25 on the Singularity-Index, while the other 10 agents averaged 0.58; the figures should be read in the context of those evaluation conditions.

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Rohan Paul@rohanpaul_ai

Overview of singularity-bench: 6 end-to-end tasks span the agent-development stack.

Agents design and implement systems within fixed budgets, and the systems are evaluated against human-built references on the same hardware. https://t.co/je74YzJCcM

ReplyRohan Paul@rohanpaul_ai

New Carnegie Mellon paper finds that Opus 5.5 + Claude Code built a pre-training pipeline, an inference engine, and a cluster orchestrator that matched or beat production tools like SGLang and NVIDIA Dynamo on the same GPUs. Singularity-Bench gives agents 6 end-to-end jobs, from pre-training a model to building a serving engine. Each result is scored against a production system, such as SGLang or Claude Code, on the same GPUs. Opus 5.5 scored a Singularity-Index of 1.25, where 1 equals the human baselines, while the other 10 agents averaged 0.58. Its serving engine earned 11.52 USD per GPU-hour against 8.18 for SGLang.

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03:55 · 2026-11-14 · UTC+8

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