MM-MT-Bench

A multi-turn LLM-as-a-judge evaluation benchmark for testing multimodal instruction-tuned models' ability to follow user instructions in multi-turn dialogues and answer open-ended questions in a zero-shot manner.

Mistral Large 3 from Mistral AI currently leads the MM-MT-Bench leaderboard with a score of 84.900 across 17 evaluated AI models.

Mistral AIMistral Large 3 leads with 84.9%, followed by Alibaba Cloud / Qwen TeamQwen3 VL 235B A22B Instruct at 8.5% and Alibaba Cloud / Qwen TeamQwen3 VL 235B A22B Thinking at 8.5%.

Progress Over Time

Interactive timeline showing model performance evolution on MM-MT-Bench

State-of-the-art frontier
Open
Proprietary

MM-MT-Bench Leaderboard

17 models
ContextCostLicense
1
Mistral AI
Mistral AI
675B
2
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
236B262K$0.30 / $1.49
2
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
236B262K$0.45 / $3.49
4
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
33B
5
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
33B
6
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
31B
7
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
9B262K$0.18 / $2.09
8
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
31B
9
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
9B262K$0.08 / $0.50
9
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
4B262K$0.10 / $1.00
11
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
4B262K$0.10 / $0.60
12
Mistral AI
Mistral AI
124B
13
Mistral AI
Mistral AI
12B
1414B
158B
163B
17
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
7B
Notice missing or incorrect data?

FAQ

Common questions about MM-MT-Bench.

What is the MM-MT-Bench benchmark?

A multi-turn LLM-as-a-judge evaluation benchmark for testing multimodal instruction-tuned models' ability to follow user instructions in multi-turn dialogues and answer open-ended questions in a zero-shot manner.

What is the MM-MT-Bench leaderboard?

The MM-MT-Bench leaderboard ranks 17 AI models based on their performance on this benchmark. Currently, Mistral Large 3 by Mistral AI leads with a score of 84.900. The average score across all models is 9.832.

What is the highest MM-MT-Bench score?

The highest MM-MT-Bench score is 84.900, achieved by Mistral Large 3 from Mistral AI.

How many models are evaluated on MM-MT-Bench?

17 models have been evaluated on the MM-MT-Bench benchmark, with 0 verified results and 17 self-reported results.

What categories does MM-MT-Bench cover?

MM-MT-Bench is categorized under communication and multimodal. The benchmark evaluates multimodal models.

More evaluations to explore

Related benchmarks in the same category

View all communication
MMMU

MMMU (Massive Multi-discipline Multimodal Understanding) is a benchmark designed to evaluate multimodal models on college-level subject knowledge and deliberate reasoning. Contains 11.5K meticulously collected multimodal questions from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering across 30 subjects and 183 subfields.

multimodalmultimodal
62 models
MMMU-Pro

A more robust multi-discipline multimodal understanding benchmark that enhances MMMU through a three-step process: filtering text-only answerable questions, augmenting candidate options, and introducing vision-only input settings. Achieves significantly lower model performance (16.8-26.9%) compared to original MMMU, providing more rigorous evaluation that closely mimics real-world scenarios.

multimodalmultimodal
49 models
CharXiv-R

CharXiv-R is the reasoning component of the CharXiv benchmark, focusing on complex reasoning questions that require synthesizing information across visual chart elements. It evaluates multimodal large language models on their ability to understand and reason about scientific charts from arXiv papers through various reasoning tasks.

multimodalmultimodal
36 models
MathVista

MathVista evaluates mathematical reasoning of foundation models in visual contexts. It consists of 6,141 examples derived from 28 existing multimodal datasets and 3 newly created datasets (IQTest, FunctionQA, and PaperQA), combining challenges from diverse mathematical and visual tasks to assess models' ability to understand complex figures and perform rigorous reasoning.

multimodalmultimodal
36 models
AI2D

AI2D is a dataset of 4,903 illustrative diagrams from grade school natural sciences (such as food webs, human physiology, and life cycles) with over 15,000 multiple choice questions and answers. The benchmark evaluates diagram understanding and visual reasoning capabilities, requiring models to interpret diagrammatic elements, relationships, and structure to answer questions about scientific concepts represented in visual form.

multimodalmultimodal
32 models
Tau2 Telecom

τ²-Bench telecom domain evaluates conversational agents in a dual-control environment modeled as a Dec-POMDP, where both agent and user use tools in shared telecommunications troubleshooting scenarios that test coordination and communication capabilities.

communication
30 models