Best model for browser agents.
Browser Use Bench v2.1: 180 real tasks on live websites. Updated Oct 29, 2026.
How we test.
Same tasks and judge for every model.
180 real tasks; the agent works through each on live websites; a judge checks a weighted rubric; every model gets a score from 0 to 100, plus cost and time per task.
Score vs. cost.
Up and to the left is better.
Model cost only. Lab models ran in the open-source BrowserCode harness, ours in Browser Use.
Leaderboard.
Best setting per model.
| # | Model | Score | Cost / task | Minutes |
|---|---|---|---|---|
| Browser UseBU fast | 72.9 | 11¢ | 9.2 | |
| 1 | GPT-6.1 Solhigh | 81.3 | $1.92 | 14.3 |
| 2 | GPT-6 Solhigh | 79.4 | $1.86 | 11.0 |
| 3 | GPT-6.1 Terrahigh | 79.0 | $1.26 | 8.8 |
| 4 | GPT-6 Astramedium | 77.9 | $10.77 | 8.7 |
| 5 | Kimi K3.1 Prohigh | 76.7 | $1.89 | 8.8 |
| 6 | Claude Opus 5.5high | 75.0 | $2.35 | 11.8 |
| 6 | DeepSeek V4.1 Flash 1017max | 75.0 | 22¢ | 16.8† |
| 8 | Grok 4.7high | 73.3 | $3.46 | 17.6 |
| 9 | Claude Sonnet 5.5high | 72.7 | $1.53 | 9.4 |
| 10 | Claude Fable 5.1high | 72.0 | $5.19 | 17.2 |
| 11 | DeepSeek V4.1 Flashmax | 70.9 | 18¢ | 17.5 |
| 12 | GPT-5.6 Lunaxhigh | 69.6 | 28¢ | 14.6 |
| 13 | Claude Haiku 5.5xhigh | 69.5 | 46¢ | 14.2 |
| 14 | GPT-6 Lunamax | 69.2 | 22¢ | 16.6 |
| 15 | Claude Opus 5 | 66.8 | $4.72 | 19.0 |
| 16 | GLM 5.3 FlashXmax | 59.7 | 55¢ | 14.1 |
| 17 | MiniMax M3.1 Flashmedium | 50.6 | 13¢ | 47.1 |
Show all 40 settings
| # | Model | Score | Cost / task | Minutes |
|---|---|---|---|---|
| Browser UseBU fast | 72.9 | 11¢ | 9.2 | |
| 1 | GPT-6.1 Solhigh | 81.3 | $1.92 | 14.3 |
| 2 | GPT-6.1 Solmedium | 79.6 | $1.30 | 7.9 |
| 3 | GPT-6 Solhigh | 79.4 | $1.86 | 11.0 |
| 3 | GPT-6 Solxhigh | 79.4 | $3.00 | 15.7 |
| 5 | GPT-6.1 Terrahigh | 79.0 | $1.26 | 8.8 |
| 6 | GPT-6.1 Solxhigh | 78.5 | $2.23 | 18.5 |
| 7 | GPT-6 Astramedium | 77.9 | $10.77 | 8.7 |
| 8 | Kimi K3.1 Prohigh | 76.7 | $1.89 | 8.8 |
| 9 | DeepSeek V4.1 Flash 1017max | 75.0 | 22¢ | 16.8† |
| 9 | Claude Opus 5.5high | 75.0 | $2.35 | 11.8 |
| 11 | GPT-6.1 Sollow | 74.7 | 75¢ | 4.6 |
| 12 | GPT-6 Solmedium | 74.4 | $1.02 | 5.9 |
| 13 | DeepSeek V4.1 Flash 1017low | 74.0 | 12¢ | 11.6† |
| 14 | DeepSeek V4.1 Flash 1017high | 73.6 | 15¢ | 12.6† |
| 15 | Grok 4.7high | 73.3 | $3.46 | 17.6 |
| 16 | Claude Sonnet 5.5high | 72.7 | $1.53 | 9.4 |
| 17 | Claude Fable 5.1high | 72.0 | $5.19 | 17.2 |
| 18 | GPT-6 Solmax | 71.5 | $4.71 | 21.6 |
| 19 | DeepSeek V4.1 Flashmax | 70.9 | 18¢ | 17.5 |
| 20 | GPT-5.6 Lunaxhigh | 69.6 | 28¢ | 14.6 |
| 21 | Claude Haiku 5.5xhigh | 69.5 | 46¢ | 14.2 |
| 22 | GPT-6 Lunamax | 69.2 | 22¢ | 16.6 |
| 23 | GPT-6 Lunaxhigh | 69.0 | 16¢ | 11.3 |
| 24 | GPT-6 Lunahigh | 68.9 | 12¢ | 12.0 |
| 25 | Claude Opus 5 | 66.8 | $4.72 | 19.0 |
| 26 | Claude Haiku 5.5max | 65.3 | $1.07 | 21.0 |
| 27 | GPT-6 Lunamedium | 64.3 | 7.7¢ | 7.8 |
| 27 | Claude Haiku 5.5high | 64.3 | 23¢ | 9.3 |
| 29 | GPT-6 Solnone | 63.5 | $1.21 | 5.4 |
| 30 | GPT-6 Sollow | 63.3 | 44¢ | 3.5 |
| 31 | GLM 5.3 FlashXmax | 59.7 | 55¢ | 14.1 |
| 32 | Claude Haiku 5.5medium | 53.4 | 9.2¢ | 5.2 |
| 33 | MiniMax M3.1 Flashmedium | 50.6 | 13¢ | 47.1 |
| 34 | MiniMax M3.1 Flashhigh | 49.5 | 13¢ | 47.5 |
| 35 | MiniMax M3.1 Flashlow | 48.6 | 11¢ | 44.3 |
| 36 | Claude Haiku 5.5low | 45.6 | 2.7¢ | 2.4 |
| 37 | GPT-6 Lunanone | 37.6 | 9.1¢ | 3.7 |
| 38 | GPT-6 Lunalow | 36.2 | 1.3¢ | 1.4 |
| 39 | GPT-5.6 Lunanone | 31.0 | 4.0¢ | 2.1 |
† Ran on different CI runners, so its time is not comparable.
- Tasks
- 180 long tasks, live sites
- Judge
- GPT-5.6 Luna xhigh, weighted rubric
- Cost
- Model tokens only
- Time
- Median minutes per task
FAQ
Which LLM is best for browser agents?
GPT-6.1 Sol high: 81.3 of 100 at $1.92 a task, on 180 tasks on live websites. Next is GPT-6 Sol high at 79.4.
Which model is the best value?
GPT-6.1 Terra high: 79.0 at $1.26 a task, 2.3 points under the top for 35% less. It is the cheapest setting within 3 points of the top score.
How does Browser Use's own agent score?
72.9 at 11¢ a task, 18× less than the top score. No setting in the test scores higher at that price or below.
How is it scored?
An LLM judge (GPT-5.6 Luna xhigh) checks each task against a weighted rubric. Mean weighted rubric score per task, 0-100. Estimated model cost per task in USD. Excludes search, judge, browser and runner costs.
What about earlier benchmarks?
Internal Bench Hard and BU Bench V1 came before Browser Use Bench v2.1. Browser Use's own agent scored highest on both.
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