reasoning benchmark
SWE-Lancer
A benchmark for evaluating large language models on real-world freelance software engineering tasks from Upwork. Contains over 1,400 tasks valued at $1 million USD total, ranging from $50 bug fixes to $32,000 feature implementations. Includes both independent engineering tasks graded via end-to-end tests and managerial tasks assessed against original engineering managers' choices.
Updated Aug 11, 2026
Models4
Model coverage4
MetricScore
EvidenceB
SWE-Lancer Ranking
Higher score ranks better on this benchmark.
| 01 | OP | 66.3% | 100.0% | 4 | C | |
| 02 | OP | 37.3% | 66.7% | 4 | C | |
| 03 | OP | 32.6% | 33.3% | 4 | C | |
| 04 | OP | 18.0% | 0.0% | 4 | C |
SWE-Lancer Score Distribution
A closer view of the leading scores on this benchmark.
SWE-Lancer Highlights
The leading models and scores on this benchmark.
What is SWE-Lancer?
What SWE-Lancer measures and how its scores work.
A benchmark for evaluating large language models on real-world freelance software engineering tasks from Upwork. Contains over 1,400 tasks valued at $1 million USD total, ranging from $50 bug fixes to $32,000 feature implementations. Includes both independent engineering tasks graded via end-to-end tests and managerial tasks assessed against original engineering managers' choices.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- SWE-Lancer
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- swe-lancer|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about SWE-Lancer.
Which model scores highest on SWE-Lancer?
GPT-5.1 Codex is currently ranked first with 66.3%.
What does SWE-Lancer measure?
A benchmark for evaluating large language models on real-world freelance software engineering tasks from Upwork. Contains over 1,400 tasks valued at $1 million USD total, ranging from $50 bug fixes to $32,000 feature implementations. Includes both independent engineering tasks graded via end-to-end tests and managerial tasks assessed against original engineering managers' choices.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
4 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.