TIR-Bench
TIR-Bench - tool-integrated reasoning benchmark for visual mathematical problem solving. Reports with CI / without CI scores.
Category
Vision & Language
Score type
percent
Scale
relative
higher is better
Models tested
5
What is TIR-Bench?
TIR-Bench is a benchmark for understanding images combined with text. The official description says: "TIR-Bench - tool-integrated reasoning benchmark for visual mathematical problem solving". Below we describe in simple words what benchmarks of this type generally examine.
What does it test?
TIR-Bench measures understanding images combined with text. In detail, it checks:
- answering questions about images (figures, photos, charts, memes)
- reading text inside images and connecting it to the question
- visual reasoning such as counting, locating and comparing objects
- combining what is seen with general knowledge
How to read the score
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.
Official methodology note: TIR-Bench - tool-integrated reasoning benchmark for visual mathematical problem solving. Reports with CI / without CI scores.
Frequently asked questions
What is the TIR-Bench benchmark?
TIR-Bench is a benchmark for understanding images combined with text. The official description says: 'TIR-Bench - tool-integrated reasoning benchmark for visual mathematical problem solving'. Below we describe in simple words what benchmarks of this type generally examine.
What does TIR-Bench test?
TIR-Bench mainly measures understanding images combined with text. Concretely, it checks: answering questions about images (figures, photos, charts, memes); reading text inside images and connecting it to the question; visual reasoning such as counting, locating and comparing objects.
How is the TIR-Bench score calculated?
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.
Where do I get TIR-Bench data and scores?
The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/tir-bench/scores) or pull it directly from the Open LLM Radar API with any HTTP client.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Benchmark dataset
The question/task data itself — download and evaluate locally
Benchmark metadata (JSON)
This page's benchmark record from the Open LLM Radar API
All model scores (JSON download)
Full score table as JSON; paginate with limit/offset params
Open LLM Radar API docs
Swagger UI to explore and reuse all radar API endpoints
Model scores 5 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
Qwen3.5-27B
Qwen Team (Alibaba Cloud)
|
59.8%
|
01.02.2026 | Report | Unverified |
| 2 |
Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
|
55.5%
|
01.02.2026 | Report | Unverified |
| 3 |
Qwen3.5-122B-A10B
Qwen Team (Alibaba Cloud)
|
53.2%
|
01.02.2026 | Report | Verified |
| 4 |
Qwen3.5-4B
Qwen
|
38.9%
|
01.02.2026 | Report | Verified |
| 5 |
Qwen3-235B-A22B
Qwen Team (Alibaba Cloud)
|
29.8%
|
01.02.2026 | Report | Unverified |