AndroidBench
Android development tasks from popular open-source apps: agent resolves issues or builds features, verified by test suites in emulator environments.
Android Bench 1.0 has 100 tasks drawn from real pull requests in open-source Android apps. Frontier models reached about 90%, and Google replaced it with 2.0 on September 17th, 2026. The GitHub repo is archived in favor of Harbor Hub.
- Made by
- Google (Android Developers)
- Released
- March 2026
- Kind
- Fixing real repositories
- Level
- Expert
- Scored
- Checked automatically
- Answers
- Code
- Tasks
- All public
- Strongest model tested
- Claude Opus 5 #10 on the capability index
- Last in a launch post
- August 2026
- Last model added
- August 2026
- Scores refreshed
- November 12th, 2026
Claude Opus 5 leads with 91.8% among models released by August 2026
Best score per model from the benchmark's own leaderboard, read on November 12th, 2026. Where a model ran with several settings or harnesses, we keep its best. Every run is in the next chart.
The newest model with a score here came out in August 2026, and nobody has tested 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 11 more
- 91.8 Claude Opus 5
- 90.9 Claude Fable 5
- 90.8 GPT-5.6 Sol
- 90.2 Kimi K3
- 87.6 GPT-5.6 Luna
- 87.0
Qwen 3.8 Max
- 86.8 GPT-5.6 Terra
- 80.2 GPT-5.5
- 76.2 Claude Sonnet 5
- 75.6
Gemini 3.6 Flash
- 74.3
Gemini 3.1 Pro
- 74.1 GPT-5.4
- 72.4 Claude Opus 4.8
- 72.2 GLM-5.2
- 71.1
Gemini 3.5 Flash
- 70.4 Kimi K2.7 Code
- 68.7 Claude Opus 4.7
- 67.6 Kimi K2.6
- 67.0 Claude Sonnet 4.6
- 63.6
MiniMax-M3
- 63.2 GLM-5.1
- 62.5
Gemini 3 Flash
- 60.8 MiMo-V2.5-Pro
- 59.5
DeepSeek-V4-Pro
- 57.7
Qwen3.7-Plus
- 54.7
DeepSeek-V4-Flash
- 54.2
Qwen3.7-Max
- 50.6
Gemini 3.5 Flash-Lite
- 45.1
Qwen3.6 27B
- 41.6
MiniMax-M2.7
- 37.1
Gemma 4 31B IT
- 37.0
Qwen 3.6 35B-A3B
- 1 Claude Opus 5 91.8%
- 2 Claude Fable 5 90.9%
- 3 GPT-5.6 Sol 90.8%
- 4 Kimi K3 90.2%
- 5 GPT-5.6 Luna 87.6%
- 6
Qwen 3.8 Max 87%
- 7 GPT-5.6 Terra 86.8%
- 8 GPT-5.5 80.2%
- 9 Claude Sonnet 5 76.2%
- 10
Gemini 3.6 Flash 75.6%
- 11
Gemini 3.1 Pro 74.3%
- 12 GPT-5.4 74.1%
- 13 Claude Opus 4.8 72.4%
- 14 GLM-5.2 72.2%
- 15
Gemini 3.5 Flash 71.1%
- 16 Kimi K2.7 Code 70.4%
- 17 Claude Opus 4.7 68.7%
- 18 Kimi K2.6 67.6%
- 19 Claude Sonnet 4.6 67%
- 20
MiniMax-M3 63.6%
- 21 GLM-5.1 63.2%
- 22
Gemini 3 Flash 62.5%
- 23 MiMo-V2.5-Pro 60.8%
- 24
DeepSeek-V4-Pro 59.5%
- 25
Qwen3.7-Plus 57.7%
- 26
DeepSeek-V4-Flash 54.7%
- 27
Qwen3.7-Max 54.2%
- 28
Gemini 3.5 Flash-Lite 50.6%
- 29
Qwen3.6 27B 45.1%
- 30
MiniMax-M2.7 41.6%
- 31
Gemma 4 31B IT 37.1%
- 32
Qwen 3.6 35B-A3B 37%
Source: Official AndroidBench leaderboard
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Claude Opus 5 | 91.8% | Jul 2026 | Official leaderboard | |
| 2 | Claude Fable 5 | 90.9% | Jun 2026 | Official leaderboard | |
| 3 | GPT-5.6 Sol | 90.8% | Jul 2026 | Official leaderboard | |
| 4 | Kimi K3 | 90.2% | Jul 2026 | Official leaderboard | |
| 5 | GPT-5.6 Luna | 87.6% | Jul 2026 | Official leaderboard | |
| 6 | | 87% | Aug 2026 | Official leaderboard | |
| 7 | GPT-5.6 Terra | 86.8% | Jul 2026 | Official leaderboard | |
| 8 | GPT-5.5 | 80.2% | Apr 2026 | Official leaderboard | |
| 9 | Claude Sonnet 5 | 76.2% | Jun 2026 | Official leaderboard | |
| 10 | | 75.6% | Jul 2026 | Official leaderboard | |
| 11 | | 74.3% | Feb 2026 | Official leaderboard | |
| 12 | GPT-5.4 | 74.1% | Mar 2026 | Official leaderboard | |
| 13 | Claude Opus 4.8 | 72.4% | May 2026 | Official leaderboard | |
| 14 | GLM-5.2 | 72.2% | Jun 2026 | Official leaderboard | |
| 15 | | 71.1% | May 2026 | Official leaderboard | |
| 16 | Kimi K2.7 Code | 70.4% | Jun 2026 | Official leaderboard | |
| 17 | Claude Opus 4.7 | 68.7% | Apr 2026 | Official leaderboard | |
| 18 | Kimi K2.6 | 67.6% | Apr 2026 | Official leaderboard | |
| 19 | Claude Sonnet 4.6 | 67% | Feb 2026 | Official leaderboard | |
| 20 | | 63.6% | Jun 2026 | Official leaderboard | |
| 21 | GLM-5.1 | 63.2% | Apr 2026 | Official leaderboard | |
| 22 | | 62.5% | Dec 2025 | Official leaderboard | |
| 23 | MiMo-V2.5-Pro | 60.8% | Apr 2026 | Official leaderboard | |
| 24 | | 59.5% | Apr 2026 | Official leaderboard | |
| 25 | | 57.7% | Jun 2026 | Official leaderboard | |
| 26 | | 54.7% | Apr 2026 | Official leaderboard | |
| 27 | | 54.2% | May 2026 | Official leaderboard | |
| 28 | | 50.6% | Jul 2026 | Official leaderboard | |
| 29 | | 45.1% | Apr 2026 | Official leaderboard | |
| 30 | | 41.6% | Mar 2026 | Official leaderboard | |
| 31 | | 37.1% | Apr 2026 | Official leaderboard | |
| 32 | | 37% | Apr 2026 | Official leaderboard |
Labs report scores for 1 model in their cards. We plot every source in the charts below.
The best score, 91.8%, costs $175 for the full run
Runs on the cost frontier score higher than any run that's cheaper. GPT-5.6 Luna comes within 4.2 points of the best for $7.20 for the full run, 24 times cheaper. The costs are the leaderboard's own estimates, so compare them only within this benchmark.
Source: Official AndroidBench leaderboard
| Model | Effort | Harness | Score | Cost of the full run | Output tokens |
|---|---|---|---|---|---|
| Claude Opus 5 | 91.8% | $175 | |||
| Claude Fable 5 | 90.9% | $144 | |||
| GPT-5.6 Sol | 90.8% | $150 | |||
| Kimi K3 | 90.2% | $113 | |||
| GPT-5.6 Luna | 87.6% | $7.20 | |||
| | 87% | $197 | |||
| GPT-5.6 Terra | 86.8% | $48 | |||
| GPT-5.5 | 80.2% | $138 | |||
| Claude Sonnet 5 | 76.2% | $100 | |||
| | 75.6% | $136 | |||
| | 74.3% | $87 | |||
| GPT-5.4 | 74.1% | $83 | |||
| Claude Opus 4.8 | 72.4% | $88 | |||
| GLM-5.2 | 72.2% | $117 | |||
| | 71.1% | $166 | |||
| Kimi K2.7 Code | 70.4% | $48 | |||
| Claude Opus 4.7 | 68.7% | $97 | |||
| Kimi K2.6 | 67.6% | $49 | |||
| Claude Sonnet 4.6 | 67% | $128 | |||
| | 63.6% | $42 | |||
| GLM-5.1 | 63.2% | $54 | |||
| | 62.5% | $30 | |||
| MiMo-V2.5-Pro | 60.8% | $9.20 | |||
| | 59.5% | $3.70 | |||
| | 57.7% | $19 | |||
| | 54.7% | $1.50 | |||
| | 54.2% | $58 | |||
| | 50.6% | $34 | |||
| | 45.1% | $97 | |||
| | 41.6% | $15 | |||
| | 37.1% | $10 | |||
| | 37% | $18 |
4 models have held number one on the official leaderboard since its release in March 2026
Lab claims count from the day the card came out, independent runs from the model’s release.
Source: Official AndroidBench leaderboard
Claude Fable 5 on the official leaderboard passed 85% in June 2026
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Sources: Official AndroidBench leaderboard and labs’ model cards
One lab put it in a launch post in the last six months
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| Aug 3, 2026 | 75.1 | AndroidBench |
One lab claim next to the leaderboard
Each score a lab reported for its own model, beside the same model's best score on the leaderboard. Gaps mostly come from different harnesses, effort levels and task subsets. Every benchmark.
| Model | Lab says | Board says | Gap |
|---|---|---|---|
| | 75.1% AndroidBench | 87% Leaderboard | −12 pts |
Versions
We switch the top row to each new version when it comes out. Open it for one row per version. An old version stays in play while people still test it, so whoever first takes it past 85% gets credit for closing it out.
Source: Official leaderboards
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