multimodal benchmark
TIR-Bench Leaderboard
A tool-calling and multimodal interaction benchmark for testing visual instruction following and execution reliability.
Updated Aug 17, 2026
Models4
Model coverage4
MetricScore
EvidenceB
TIR-Bench Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelAC | Score61.6% | Percentile100.0% | Participants4 | EvidenceC | Evaluated |
| Rank02 | ModelAC | Score59.8% | Percentile66.7% | Participants4 | EvidenceC | Evaluated |
| Rank03 | ModelAC | Score55.5% | Percentile33.3% | Participants4 | EvidenceC | Evaluated |
| Rank04 | ModelAC | Score53.2% | Percentile0.0% | Participants4 | EvidenceC | Evaluated |
TIR-Bench Highlights
The leading models and scores on this benchmark.
TIR-Bench Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for TIR-Bench
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis tir-bench AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
- 01ACQwen3.6 PlusAlibaba Cloud / Qwen Team
- Score
- 61.6%
- Price
- $0.50 input / $3.0 output per 1M tokens
- Speed
- Up to 16 tok/s via Together
Strengths
- Ranks #1 of 4 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures TIR-Bench, not total model capability
- 02ACQwen3.5-27BAlibaba Cloud / Qwen Team
- Score
- 59.8%
- Price
- $0.30 input / $2.4 output per 1M tokens
- Speed
- Up to 6.7 tok/s via Novita
Strengths
- Ranks #2 of 4 compared models
- 67th percentile on this benchmark
- C evidence result
Considerations
- This result measures TIR-Bench, not total model capability
- 03ACQwen3.5-35B-A3BAlibaba Cloud / Qwen Team
- Score
- 55.5%
- Price
- $0.25 input / $2.0 output per 1M tokens
Strengths
- Ranks #3 of 4 compared models
- 33th percentile on this benchmark
- C evidence result
Considerations
- This result measures TIR-Bench, not total model capability
- 04ACQwen3.5-122B-A10BAlibaba Cloud / Qwen Team
- Score
- 53.2%
- Price
- $0.40 input / $3.2 output per 1M tokens
Strengths
- Ranks #4 of 4 compared models
- 0th percentile on this benchmark
- C evidence result
Considerations
- This result measures TIR-Bench, not total model capability
Selection summary
Best AI Models for TIR-Bench
Qwen3.6 Plus currently leads TIR-Bench with 61.6%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What is TIR-Bench?
What TIR-Bench measures and how its scores work.
A tool-calling and multimodal interaction benchmark for testing visual instruction following and execution reliability.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- TIR-Bench
- Modality
- multimodal
- Primary category
- multimodal
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- tir-bench|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about TIR-Bench.
Which model scores highest on TIR-Bench?
Qwen3.6 Plus is currently ranked first with 61.6%.
What does TIR-Bench measure?
A tool-calling and multimodal interaction benchmark for testing visual instruction following and execution reliability.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
4 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.