Visual Reasoning Benchmark

Clock Bench

ClockBench evaluates whether models can read analog clocks - a task that is trivial for humans, but current frontier models struggle with.

Clock Faces
36
Clocks
180
Questions
720
Human Accuracy
90.7%
Top Model Accuracy
66.7%

Leaderboard

RankModelAccuracyLab
Human Baseline90.7%
1GPT-5.6 Sol Max66.7%OpenAI
2Claude Opus 5.5 Max66.7%Anthropic
3GPT-6 Astra Max65.6%OpenAI
4Gemini 3.8 Flash62.8%Google
5Claude Opus 5 Max60.7%Anthropic
6Claude Fable 5.1 Max53.3%Anthropic
7GPT-5.4 High50.6%OpenAI
8Gemini 3.7 Flash48.3%Google
9GPT-5.5 High46.1%OpenAI
10Muse Spark 1.342.2%Meta
11Qwen 3-VL 235B Instruct39.4%Alibaba
12Claude Fable 535%Anthropic
13Gemini 3.1 Pro32.2%Google
14Gemini 3.5 Flash31.1%Google
15Gemini 3 Pro28.9%Google
16Grok 4.521.7%xAI
17Grok 4.620%xAI
18Gemini 2.5 Pro18.9%Google
19GPT-5.2 High15%OpenAI
20Gemini Robotics ER 1.515%Google
21Claude Opus 4.715%Anthropic
22o3 Pro14.4%OpenAI
23Qwen 3-VL 235B Thinking14.4%Alibaba
24o3 High12.2%OpenAI
25Gemini 2.5 Flash11.1%Google
26GPT-5 High11.1%OpenAI
27GPT-5 Pro11.1%OpenAI
28Mistral Medium 3.110%Mistral
29Claude Opus 4.68.9%Anthropic
30GPT-5 Mini8.9%OpenAI
31Claude Opus 4.18.3%Anthropic
32Claude Sonnet 4.57.2%Anthropic
33Qwen 2.5-VL 72B6.1%Alibaba
34Claude Sonnet 46.1%Anthropic
35GTP-4o5%OpenAI
36GTP-5 Nano3.9%OpenAI
37Grok 4 Fast3.9%xAI
ClockBench AI Benchmark
Human Baseline
90.7%
GPT-5.6 Sol Max
66.7%
Claude Opus 5.5 Max
66.7%
GPT-6 Astra Max
65.6%
Gemini 3.8 Flash
62.8%
Claude Opus 5 Max
60.7%
Claude Fable 5.1 Max
53.3%
GPT-5.4 High
50.6%
Gemini 3.7 Flash
48.3%
GPT-5.5 High
46.1%
Muse Spark 1.3
42.2%
Qwen 3-VL 235B Instruct
39.4%
Claude Fable 5
35.0%
Gemini 3.1 Pro
32.2%
Gemini 3.5 Flash
31.1%
Gemini 3 Pro
28.9%
Grok 4.5
21.7%
Grok 4.6
20.0%
Gemini 2.5 Pro
18.9%
GPT-5.2 High
15.0%
Gemini Robotics ER 1.5
15.0%
Claude Opus 4.7
15.0%
o3 Pro
14.4%
Qwen 3-VL 235B Thinking
14.4%
o3 High
12.2%
Gemini 2.5 Flash
11.1%
GPT-5 High
11.1%
GPT-5 Pro
11.1%
Mistral Medium 3.1
10.0%
Claude Opus 4.6
8.9%
GPT-5 Mini
8.9%
Claude Opus 4.1
8.3%
Claude Sonnet 4.5
7.2%
Qwen 2.5-VL 72B
6.1%
Claude Sonnet 4
6.1%
GTP-4o
5.0%
GTP-5 Nano
3.9%
Grok 4 Fast
3.9%

Results Summary

Despite frontier models showing strong reasoning skills, mathematical ability, and visual understanding on multiple benchmarks, they seem to be struggling at reading analog clocks for now.

One hypothesis might be that this task sets a high bar for doing reasoning within the visual space (as opposed to text space).

More research is likely needed to understand if these capabilities can be obtained by scaling existing paradigms, or a novel approach is required.

Dataset

Sample Clocks

Few examples of clocks that we used in the benchmark.

Sample clocks from ClockBench

Questions

  1. Reading Time
    Models are asked to determine whether a given clock shows a valid time. If valid, they should report the hours, minutes, seconds, date, month, and day of the week (based on what is present), in a structured JSON format.
  2. Adding or Subtracting Time
    Models are asked to add or subtract varying amounts of time.
  3. Rotating Hands
    Models are asked to rotate one of the hands (hour, minute, or second) by a specified angle, clockwise or counterclockwise.
  4. Shifting Time Zone
    Models are asked to assume they are in New York during summer and report the corresponding time in various locations worldwide.

Try Yourself

Interesting in trying out ClockBench?
A small public dataset and sample evaluation code is available to everyone.

Public Dataset
Alek Safar
LinkedInX.com

Please reach out to [email protected] with ideas, suggestions, questions or any other inquiries.