DeepSWE leaderboard
As of 6 Nov 2026, the top score on DeepSWE is 74.1% by GPT-6 Astra (OpenAI), ahead of Gemini 3.8 Flash at 73.8%, across 69 model configurations with a published result (Epoch AI Benchmarking Hub, result dated 3 Sep 2026). SWE-style repository tasks run in an agent loop, with the reasoning effort noted per run. DeepSWE is shown for reference only and does not count toward the BenchLeader Index.
Key facts (as of 6 Nov 2026)
- GPT-6 Astra holds the top DeepSWE v1.1 score at 74.1% (Epoch AI Benchmarking Hub, 3 Sep 2026).
- Gemini 3.8 Flash is second on DeepSWE v1.1 at 73.8% (Epoch AI Benchmarking Hub, 2 Sep 2026).
- DeepSWE v1.1: Software-engineering tasks in an agent harness with reasoning effort recorded (Epoch AI Benchmarking Hub).
- 69 model configurations have a published DeepSWE v1.1 result on BenchLeader (BenchLeader, 6 Nov 2026).
- DeepSWE v1.1 does not feed the BenchLeader Index; it is shown for reference (BenchLeader methodology).
Software-engineering tasks in an agent harness with reasoning effort recorded.
- Published by
- Epoch AI Benchmarking Hub
- Category
- Coding
- Index weight
- Reference only
- Models
- 69
- Data as of
- 6 Nov 2026
CC BY 4.0 — Epoch AI, ‘AI Benchmarking Hub’, epoch.ai/benchmarks. Mirrored boards credit their original publishers.
What the test looks like
SWE-style repository tasks run in an agent loop, with the reasoning effort noted per run.
How it is scored
Pass@1, as published.
What to keep in mind
Harness-dependent.
- 1GPT-6 Astra (xhigh)74.1%
- 2Gemini 3.8 Flash (high)73.8%
- 3Claude Opus 5 (max)73.7%
- 4GPT-6 Astra (max)73.2%
- 5GPT-6 Astra (high)73.2%
- 6Claude Opus 5 (xhigh)73.2%
- 7Claude Opus 5 (high)72.8%
- 8GPT-6 Astra (medium)72.8%
- 9GPT-5.6 Sol (max)72.7%
- 10Gemini 3.8 Flash (medium)71.0%
- 11GPT-5.6 Sol (xhigh)70.7%
- 12Claude Fable 5 (xhigh)69.9%
- 13Claude Fable 5 (max)69.7%
- 14GPT-5.6 Terra (max)69.6%
- 15GPT-5.6 Sol (high)69.4%
| # | ||||
|---|---|---|---|---|
| 1 | 74.1% | 66.8 | 2026-09-03 | |
| 2 | 73.8% | 62.0 | 2026-09-02 | |
| 3 | 73.7% | 65.4 | 2026-07-24 | |
| 4 | 73.2% | 67.8 | 2026-09-03 | |
| 5 | 73.2% | 67.0 | 2026-09-03 | |
| 6 | 73.2% | 60.2 | 2026-07-24 | |
| 7 | 72.8% | 64.3 | 2026-07-24 | |
| 8 | 72.8% | 65.1 | 2026-09-03 | |
| 9 | 72.7% | 65.4 | 2026-07-09 | |
| 10 | 71.0% | 60.7 | 2026-09-02 | |
| 11 | 70.7% | 64.2 | 2026-07-09 | |
| 12 | 69.9% | – | 2026-06-09 | |
| 13 | 69.7% | 65.3 | 2026-06-09 | |
| 14 | 69.6% | 60.7 | 2026-07-09 | |
| 15 | 69.4% | 63.6 | 2026-07-09 | |
| 16 | 69.0% | 59.0 | 2026-08-14 | |
| 17 | 68.9% | 57.4 | 2026-07-24 | |
| 18 | 68.6% | 64.3 | 2026-06-09 | |
| 19 | 68.5% | 62.1 | 2026-07-16 | |
| 20 | 67.5% | 59.6 | 2026-08-12 | |
| 21 | 67.2% | 56.3 | 2026-07-09 | |
| 22 | 67.0% | 64.3 | 2026-04-23 | |
| 23 | 67.0% | 62.7 | 2026-09-03 | |
| 24 | 66.7% | 57.9 | 2026-08-12 | |
| 25 | 65.5% | 60.3 | 2026-08-13 | |
| 26 | 65.4% | – | 2026-06-09 | |
| 27 | 65.3% | 62.2 | 2026-08-13 | |
| 28 | 65.2% | 60.5 | 2026-08-12 | |
| 29 | 64.4% | 63.7 | 2026-04-23 | |
| 30 | 63.4% | 54.2 | 2026-08-20 | |
| 31 | 61.1% | 61.5 | 2026-07-09 | |
| 32 | 60.2% | 60.3 | 2026-07-09 | |
| 33 | 59.6% | – | 2026-06-09 | |
| 34 | 59.0% | 60.2 | 2026-05-28 | |
| 35 | 58.1% | 57.0 | 2026-07-24 | |
| 36 | 57.5% | 57.1 | 2026-08-02 | |
| 37 | 56.9% | 58.5 | 2026-07-09 | |
| 38 | 54.9% | 61.7 | 2026-08-05 | |
| 39 | 54.4% | – | 2026-05-28 | |
| 40 | 54.0% | 61.0 | 2026-04-23 | |
| 41 | 53.9% | 53.8 | 2026-06-30 | |
| 42 | 53.8% | 57.5 | 2026-08-13 | |
| 43 | 53.8% | 57.0 | 2026-07-09 | |
| 44 | 53.8% | 56.9 | 2026-07-08 | |
| 45 | 53.3% | 62.2 | 2026-07-09 | |
| 46 | 51.8% | 63.2 | 2026-03-05 | |
| 47 | 51.8% | 60.6 | 2026-05-28 | |
| 48 | 49.7% | 55.6 | 2026-06-30 | |
| 49 | 48.7% | – | 2026-05-28 | |
| 50 | 48.2% | 59.3 | 2026-06-30 | |
| 51 | 46.7% | 61.2 | 2026-07-21 | |
| 52 | 45.4% | 60.4 | 2026-07-09 | |
| 53 | 44.3% | 54.8 | 2026-07-09 | |
| 54 | 43.8% | 59.5 | 2026-06-16 | |
| 55 | 41.6% | 55.2 | 2026-08-12 | |
| 56 | 40.8% | – | 2026-05-28 | |
| 57 | 39.8% | – | 2026-06-30 | |
| 58 | 37.4% | 61.9 | 2026-05-19 | |
| 59 | 36.3% | – | 2026-06-16 | |
| 60 | 36.1% | 62.5 | 2026-05-19 |
More coding benchmarks · All LLM benchmarks
Related benchmarks
Top head-to-heads on DeepSWE v1.1
Cite as: BenchLeader, “DeepSWE v1.1 leaderboard”, data as of 6 Nov 2026, https://www.benchleader.com/benchmarks/deepswe.
DeepSWE v1.1: questions
- What does DeepSWE v1.1 measure?
- SWE-style repository tasks run in an agent loop, with the reasoning effort noted per run. Scores are reported in percent of tasks solved; higher is better.
- What is the top score on DeepSWE?
- As of 6 Nov 2026, the highest score on DeepSWE v1.1 is 74.1% by GPT-6 Astra (result dated 3 Sep 2026), ahead of Gemini 3.8 Flash at 73.8%. Scores come from Epoch AI Benchmarking Hub and are re-read daily.
- Which AI model leads DeepSWE v1.1?
- GPT-6 Astra leads DeepSWE v1.1 with 74.1% as of 6 Nov 2026 (result dated 3 Sep 2026), ahead of Gemini 3.8 Flash at 73.8%.
- How many models have DeepSWE v1.1 results?
- 69 model configurations have a DeepSWE v1.1 result on BenchLeader, all taken from Epoch AI Benchmarking Hub.
- Who runs DeepSWE v1.1 and how often is it updated?
- DeepSWE v1.1 is published by Epoch AI Benchmarking Hub. BenchLeader re-reads the published results once a day and records the date each result was published.
- Does DeepSWE v1.1 count toward the BenchLeader Index?
- No. DeepSWE v1.1 is shown for reference but left out of the composite index.