Evals & Safety
SWE-Lancer
An OpenAI benchmark of over 1,400 real freelance software engineering tasks from Upwork, together worth $1 million in actual payouts.
In depth
An OpenAI benchmark of over 1,400 real freelance software engineering tasks from Upwork, together worth $1 million in actual payouts. It includes independent coding tasks, from $50 bug fixes to $32,000 features, graded by end-to-end tests that experienced engineers triple-verified, plus managerial tasks where the model picks the best of several implementation proposals. Scoring by dollars earned ties model performance to economic value, and the open evaluation split is called SWE-Lancer Diamond.
Primary source: arxiv.org
Example
In practice, developers reach for SWE-Lancer when they need the capability described above as part of an AI feature or workflow.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover Evals & Safety.
FAQ
Common questions
What is SWE-Lancer?
An OpenAI benchmark of over 1,400 real freelance software engineering tasks from Upwork, together worth $1 million in actual payouts.
Why does SWE-Lancer matter for AI developers?
SWE-Lancer sits in the Evals & Safety part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about SWE-Lancer?
Developers Digest publishes tutorials and videos that cover Evals & Safety topics including SWE-Lancer. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Evals & Safety
SimpleQA
A factuality benchmark from OpenAI that tests whether a model can answer short, fact-seeking questions that each have one indisputable answer.
Evals & Safety
SWE-bench Pro
A software engineering benchmark from Scale AI with 1,865 long-horizon tasks drawn from 41 actively maintained repositories, built as a harder successor to SWE-bench.
Evals & Safety
System Card
A system card is a detailed safety and capability report that a frontier lab publishes with a model release, extending the model card idea from the model to the whole deployed system.
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