DeepSWE
We show this table for reference; we do not rank on it.
A long-horizon software engineering benchmark from Datacurve for measuring frontier coding agents on original tasks drawn from active open-source repositories.
Pass@1 on DeepSWE — September 1, 2026
We mirror the published pass@1 view for DeepSWE. Gemini 3.8 Flash leads the public snapshot at 73.8%, followed by Claude Opus 5 (73.6%) and GPT-6 Astra (73.2%). We do not use these results to rank models overall.
Gemini 3.8 Flash
mini-swe-agent · high reasoning
Claude Opus 5
Anthropic
mini-swe-agent · max reasoning
GPT-6 Astra
OpenAI
mini-swe-agent · max reasoning
28 modelsCoding15% of Coding reference weightCurrentUpdated September 1, 2026
Pass@1 table (28 models)
ScoreHow we show DeepSWE
We mirror the public DeepSWE leaderboard JSON from Datacurve. The snapshot shows the best available mini-swe-agent configuration per model, while preserving 70 underlying effort-level rows in the source metadata.
DeepSWE evaluates coding agents on 113 original, long-horizon software engineering tasks across 91 repositories and 5 languages, using isolated task environments and program-based verifiers.
Each row keeps the harness and settings the source published. BenchLM shows the published table for reference; the per-model scores it stores feed the ranking.
Snapshot
The published DeepSWE snapshot places Gemini 3.8 Flash first at 73.8%. The third row is 0.6 points behind. The broader top-10 range is 6.8 points, so many of the published results sit in a relatively narrow band.
28 models have been evaluated on DeepSWE. The benchmark falls in the Coding category. BenchLM shows the published table for reference; the per-model scores it stores feed the ranking. BenchAlign v5.8 gives DeepSWE 15% of the Coding reference weight, so it moves the Coding leaderboard and the overall ranking. Reference weights are relative weights in the calibrated model, not fixed shares of a score.
About DeepSWE
Year
2026
Tasks
113 software engineering tasks across 91 repositories and 5 languages
Format
Pass@1 with confidence interval, cost, time, and token metadata
Difficulty
Long-horizon software engineering
DeepSWE includes original tasks with isolated environments and program-based verifiers. BenchLM mirrors the public DeepSWE leaderboard JSON, using the best available mini-swe-agent configuration per model and preserving cost, time, token, and effort-level source metadata. Each row combines a model, agent harness, and reasoning-effort setting rather than a pure model-only benchmark score.
Freshness and provenance
Version
DeepSWE 2026
Refresh cadence
Quarterly
Staleness state
Current
Question availability
Public benchmark set
BenchLM uses freshness metadata to decide whether a benchmark should still be treated as a strong differentiator, a benchmark to watch, or a display-only reference. For the full scoring policy, see the BenchLM methodology page.
Questions
What does DeepSWE measure?
A long-horizon software engineering benchmark from Datacurve for measuring frontier coding agents on original tasks drawn from active open-source repositories.
Which model leads the published DeepSWE snapshot?
Gemini 3.8 Flash currently leads the published DeepSWE snapshot with 73.8% pass@1. BenchLM shows the published table for reference; the per-model scores it stores feed the ranking.
How many models are evaluated on DeepSWE?
The September 1, 2026 snapshot contains 28 AI models.
Know when it’s worth switching models
The model to choose, the cheaper alternative, and the release we would wait on.
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