SWE-Lancer Diamond (SWE Manager)
Models review competing implementation proposals for real freelance tasks and pick the best one. Scored against the original choice as pass@1.
Manager split of SWE-Lancer. The swe-lancer record covers the IC SWE split.
- Made by
- OpenAI
- Released
- February 2025
- Kind
- Fixing real repositories
- Level
- Expert
- Scored
- Checked automatically
- Answers
- Agent task
- Tasks
- Public, plus a private test set
- Strongest model tested
- o3 #83 on the capability index
- Last in a system card
- April 2025
- Last model added
- April 2025
In the authors’ words
We introduce SWE-Lancer, a benchmark of over 1,400 freelance software engineering tasks from Upwork, valued at $1 million USD total in real-world payouts. SWE-Lancer encompasses both independent engineering tasks–ranging from $50 bug fixes to $32,000 feature implementations–and managerial tasks, where models choose between technical implementation proposals. Independent tasks are graded with end-to-end tests triple-verified by experienced software engineers, while managerial decisions are assessed against the choices of the original hired engineering managers. We evaluate model performance and find that frontier models are still unable to solve the majority of tasks. To facilitate future research, we open-source a unified Docker image and a public evaluation split, SWE-Lancer Diamond (https://github.com/openai/SWELancer-Benchmark). By mapping model performance to monetary value, we hope SWE-Lancer enables greater research into the economic impact of AI model development.
OpenAI deep research leads with 51% among models released by April 2025
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
The newest model with a score here came out in April 2025, and nobody has reported a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:
- Claude Fable 5.2
-
Gemini 4 Carbon - GPT-6 Astra
- Claude Opus 5.5
- and 16 more
- 1 OpenAI deep research 51%
- 2 GPT-4.5 44%
- 3 o3 41%
- 4 o4-mini 23%
Source: Labs’ model cards
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | OpenAI deep research | 51% | Feb 2025 | Lab model card | |
| 2 | GPT-4.5 | 44% | Feb 2025 | Lab model card | |
| 3 | o3 | 41% | Apr 2025 | Lab model card | |
| 4 | o4-mini | 23% | Apr 2025 | Lab model card |
Number one over time
Lab claims count from the day the card came out, independent runs from the model’s release.
Source: Labs’ model cards
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Source: Labs’ model cards
Labs report it only in system cards
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark. Some system cards do, below.
4 system cards report it, from 1 lab
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| Apr 16, 2025 | o4-mini system card | 23 | SWE-Lancer Diamond (SWE Manager), pass@1, o4-mini no browsing launch candidate, printed chart label (Figure 21) | AI R&D risk |
| Apr 16, 2025 | o3 system card | 41 | SWE-Lancer Diamond (SWE Manager), pass@1, o3 no browsing launch candidate, printed chart label (Figure 21) | AI R&D risk |
| Feb 27, 2025 | GPT-4.5 system card | 44 | SWE-Lancer Diamond SWE Manager tasks, pass@1, post-mitigation, earned $144,500 (4.6.6) | Autonomy |
| Feb 25, 2025 | OpenAI deep research system card | 51 | SWE-Lancer Diamond (SWE Manager), pass@1 averaged over 3 runs, post-mitigation with browsing, $151,500 earned (without browsing 47%, $147,500) (3.4.7) | Autonomy |
More in Coding
- SWE-bench Verified In use 500 human-validated GitHub issues from 12 Python repos. Agent writes a patch that must pass hidden tests. Claude Opus 4.7 83.5%
- LiveCodeBench v6 In use Competitive programming problems collected after model cutoffs from LeetCode, AtCoder, Codeforces. Scored pass@1 on hidden tests.
DeepSeek-V4-Pro 93.5% - SWE-bench Multilingual In use 300 GitHub-issue tasks across nine programming languages in SWE-bench format. Patch graded by repository tests. Claude Fable 5.2 96.6%
- DeepSWE In use 113 original long-horizon engineering tasks across 91 repos and five languages, written from scratch. Hand-written verifiers accept alternative solutions. GPT-6 Astra 74.1%
- SciCode In use Scientist-curated research coding problems split into subproblems across natural sciences. Code checked against test cases.
Seed2.1 Pro 59.8% - Codeforces rating In use Model solves recent Codeforces contest problems. An Elo-style rating is computed from simulated contest performance. DeepSeek V4.1 Flash 3,471