multimodal benchmark
MMSearch evaluates multimodal models on search-based retrieval and question answering tasks using visual and textual information from search results.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelZA | Score72.90% | Percentile100.00% | Participants1 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis mmsearch AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
GLM-5V-Turbo currently leads MMSearch with 72.90%. 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 MMSearch measures and how its scores work.
MMSearch is a multimodal benchmark for search-based retrieval and question answering tasks.
It measures performance on tasks that require processing both visual and textual information from search results, reported as a Score ratio.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about MMSearch.
GLM-5V-Turbo is currently ranked first with 72.90%.
The current leaders are GLM-5V-Turbo (72.90%).
GLM-5V-Turbo has the lowest matched official input price at $5.0 input / $22 output per 1M tokens.
No matched runtime record is currently available.
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures performance on tasks that require processing both visual and textual information from search results, reported as a Score ratio.
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.