The LLM benchmark aggregator.

Qwen2 VL 72B

Alibaba · 72B

Strongest in Multimodal understanding (#4 of 19), weakest in Fairness (#186 of 290). Above par in 6 of 8 scopes. Among the models it meets almost everywhere, it finishes behind Grok 4.7 and Claude 3 Opus and ahead of Gemini 3.5 Flash and Kimi K2.5.

§ 1 · Profile

What it is good at

Bars run from 50 — the average of the models each source lists — so right of the line is above par. The middle column is the gap to whoever leads that scope.

Knowledge56.7−5.8#27/1971/3

Multimodal understanding66.2−5.9#4/191/1

Safety50.8−10.6#157/3271/3

Toxicity avoidance55.5−3.5#91/2661/1

Secure code57−9.2#94/2681/1

Jailbreak resistance54−11#129/2661/1

Harm refusal48.2−13.3#180/2901/2

Fairness45.2−24.8#186/2901/2

§ 2 · Head to head

What it beats, and what beats it

The same models turn up scope after scope. Counted once: where both were placed, who finished higher. Bars run right for Qwen2 VL 72B, left for the other.

OLMoE 1B-7B Instructwon 6 · lost 0

SmolLM 1.7B Instructwon 6 · lost 0

DBRX Instructwon 6 · lost 0

H2o Danube3 500M Chatwon 6 · lost 0

SmolLM2 1.7B Instructwon 6 · lost 0

Laguna Xs 2.1won 6 · lost 0

RakutenAI 7B Instructwon 6 · lost 0

Falcon Mamba 7B Instructwon 6 · lost 0

Nous Hermes 2 Mixtral 8x7B DPOwon 6 · lost 0

CodeLlama 7B Instructwon 6 · lost 0

Qwen2.5 0.5B Instructwon 6 · lost 0

Mixtral 8x7B Instructwon 6 · lost 0

Laguna S 2.1won 6 · lost 0

GPT-3.5 Turbowon 6 · lost 0

Ling 3.0 Flash VLwon 6 · lost 0

MiMo V2.6 Flashwon 6 · lost 0

Jamba 1.5 Largewon 6 · lost 0

DeepSeek LLM 67B Chatwon 6 · lost 0

Qwen2.5 3B Instructwon 6 · lost 0

Jamba Instructwon 6 · lost 0

Qwen2.5 7B Instruct 1Mwon 6 · lost 0

Qwen2.5 1.5B Instructwon 6 · lost 0

DeepSeek V4 Flashwon 6 · lost 0

Jamba 1.6 Miniwon 7 · lost 0

Command Awon 7 · lost 0

GLM-4.6won 7 · lost 0

Grok 4.1 Fastwon 7 · lost 0

Command R+won 6 · lost 1

Jamba 1.6 Largewon 6 · lost 1

Magistral Small 1.2won 6 · lost 1

DeepSeek V3won 6 · lost 1

Grok 3won 6 · lost 1

Gemma 4 26B A4Bwon 6 · lost 1

GLM-4.5won 6 · lost 1

Ling 3.0 Flashwon 6 · lost 1

Gemini 2.5 Prowon 6 · lost 1

Grok 4won 6 · lost 1

NVIDIA Nemotron 3 Ultra 550B A55Bwon 6 · lost 1

Grok 4.3won 6 · lost 1

GLM-5.2won 6 · lost 1

Mistral Instruct v0.1 (7B)won 5 · lost 1

OLMo 7B Instructwon 5 · lost 1

RakutenAI 7B Chatwon 5 · lost 1

PowerMoE 3Bwon 5 · lost 1

Phi 2won 5 · lost 1

Gemma 7B ITwon 5 · lost 1

PowerLM 3Bwon 5 · lost 1

Qwen3 30B A3B Instructwon 5 · lost 1

LFM2 350Mwon 5 · lost 1

GLM-4 9B Chatwon 5 · lost 1

SmolLM2 360M Instructwon 5 · lost 1

Magistral Smallwon 5 · lost 1

Jamba Mini 1.7won 5 · lost 1

Gemma 2B ITwon 5 · lost 1

Aya 23 8Bwon 5 · lost 1

Falcon H1 0.5B Instructwon 5 · lost 1

H2o Danube3 4B Chatwon 5 · lost 1

Zephyr 7B Betawon 5 · lost 1

Magistral Mediumwon 5 · lost 1

Command R7Bwon 5 · lost 1

LFM2 700Mwon 5 · lost 1

Jamba Large 1.7won 5 · lost 1

SmolLM 360M Instructwon 5 · lost 1

Qwen3 8Bwon 5 · lost 1

Mimo V2.6 Pro Ultraspeedwon 5 · lost 1

Phi-4 Mini Instructwon 5 · lost 1

Jamba 1.5 Miniwon 5 · lost 1

Open Codestral Mambawon 5 · lost 1

LFM2 1.2Bwon 5 · lost 1

Granite 3.0 1B A400M Instructwon 5 · lost 1

QwQ 32Bwon 5 · lost 1

Gemma 4 E2Bwon 5 · lost 1

Sarvam 1won 5 · lost 1

Mistral Small 3 24B Instructwon 5 · lost 1

Hunyuan A13B Instructwon 5 · lost 1

DeepSeek V3.1won 5 · lost 1

Gemma 3 4B ITwon 5 · lost 1

MiniMax M3won 5 · lost 1

Gemma 4 E4Bwon 5 · lost 2

Llama 3.3 70B Instructwon 5 · lost 2

Kimi K2 Thinkingwon 5 · lost 2

DeepSeek R1won 5 · lost 2

DeepSeek V3.2won 5 · lost 2

Kimi K2.6won 5 · lost 2

Gemma 4 31Bwon 5 · lost 2

Qwen3.7 Maxwon 5 · lost 2

Gemini 3.6 Flashwon 5 · lost 2

Qwen2 7B Instructwon 4 · lost 2

OLMo 2 7B Instructwon 4 · lost 2

SEA LION V1 7B ITwon 4 · lost 2

Grok Build 0.1won 4 · lost 2

Qwen1.5 14B Chatwon 4 · lost 2

DeepSeek R1 Distill Qwen 7Bwon 4 · lost 2

DeepSeek R1 Distill Llama 8Bwon 4 · lost 2

Llama 3.1 70B Instructwon 4 · lost 2

NexusRaven V2 13Bwon 4 · lost 2

Gemma 2 9B ITwon 4 · lost 2

Mistral 7B Instruct v0.2won 4 · lost 2

Mixtral 8x22B Instructwon 4 · lost 2

SeaLLMs V3 7B Chatwon 4 · lost 2

Komodo 7B Basewon 4 · lost 2

WizardLM 2.8x22Bwon 4 · lost 2

Llama 3.2 3B Instructwon 4 · lost 2

Kimi K2.7 Codewon 4 · lost 2

Gemma 4 12Bwon 4 · lost 2

Fuguwon 4 · lost 2

Mistral Large 4won 4 · lost 2

Gemini 2.0 Flash-Litewon 4 · lost 2

Granite 3.0 3B A800M Instructwon 4 · lost 2

Starling LM 7B Betawon 4 · lost 2

Granite 3.0 2B Instructwon 4 · lost 2

Gemma2 9B Cpt Sea Lionv3 Instructwon 4 · lost 2

Apertus 8B Instructwon 4 · lost 2

Qwen2.5 14B Instruct 1Mwon 4 · lost 2

Qwen 2.5 Instruct (7B)won 4 · lost 2

Sarvam 30Bwon 4 · lost 2

DeepSeek V4 Prowon 4 · lost 2

GLM-5.3 Flashwon 4 · lost 2

NVIDIA Nemotron 3 Super 120B A12Bwon 3 · lost 2

Yi 1.5 9B Chatwon 3 · lost 2

Mercury 2won 3 · lost 2

Gemini 1.5 Flashwon 4 · lost 3

GPT OSS 20Bwon 4 · lost 3

Gemini 2.5 Flashwon 4 · lost 3

MiMo V2.5 Prowon 4 · lost 3

Kimi K2.5won 4 · lost 3

Gemini 3.5 Flashwon 4 · lost 3

Mistral Instruct v0.3 (7B)won 3 · lost 3

Granite 3.2 8B Instructwon 3 · lost 3

Aya 23 35Bwon 3 · lost 3

Gemma 2 2B ITwon 3 · lost 3

Granite 3.1 2B Instructwon 3 · lost 3

IBM Granite 4.0 Microwon 3 · lost 3

Granite 3.2 2B Instructwon 3 · lost 3

Llama 3.1 8B Instructwon 3 · lost 3

Dolphin 2.5 Mixtral 8x7bwon 3 · lost 3

Llama 3.1 405B Instructwon 3 · lost 3

Llama 3.2 1B Instructwon 3 · lost 3

Qwen2.5 32B Instructwon 3 · lost 3

LongWriter Glm4 9Bwon 3 · lost 3

GLM-5 Turbowon 3 · lost 3

Phi 3 Medium 128k Instructwon 3 · lost 3

EAI Llama 3.1 8B Instruct SageAlignwon 3 · lost 3

Granite 4.0 H Microwon 3 · lost 3

Phi-3 Mini 128K Instructwon 3 · lost 3

Phi 3.5 Mini Instructwon 3 · lost 3

Llama 3.1 Tulu 3 8B SFTwon 3 · lost 3

Krutrim 2 Instructwon 3 · lost 3

EAI SageAlign 8B LoRAwon 3 · lost 3

Amazon Nova Microwon 3 · lost 3

Gemini 1.5 Prowon 4 · lost 4

GPT-4.1 nanowon 3 · lost 4

Llama 4 Scout Instructwon 3 · lost 4

GPT-4.1 miniwon 3 · lost 4

Gemini 2.0 Flashwon 3 · lost 4

GPT OSS 120Bwon 3 · lost 4

Kimi K2won 3 · lost 4

Qwen3 235B A22B Instructwon 3 · lost 4

GPT-6 Lunawon 3 · lost 4

GLM-5.1won 3 · lost 4

GPT-5.4won 3 · lost 4

Qwen3.8 Maxwon 3 · lost 4

Muse Spark 1.1won 3 · lost 4

Grok 4.5won 3 · lost 4

GPT-5.6 Lunawon 3 · lost 4

GPT-6 Solwon 3 · lost 4

Gemini 3.8 Flashwon 3 · lost 4

Phi-3 Mini 4K Instructwon 2 · lost 3

Claude Opus 4.6won 2 · lost 3

Claude Sonnet 4.6won 2 · lost 3

Reflection Llama 3.1 70Bwon 2 · lost 4

Granite 3.1 3B A800M Instructwon 2 · lost 4

Phi-3 Medium 4K Instructwon 2 · lost 4

Granite 3.1 8B Instructwon 2 · lost 4

Claude 3 Haikuwon 2 · lost 4

Qwen2.5 14B Instructwon 2 · lost 4

Qwen3 VL 235B A22B Instructwon 2 · lost 4

Gemma 3 27B ITwon 2 · lost 4

Qwen2 57B A14B Instructwon 2 · lost 4

Phi 3 Small 128k Instructwon 2 · lost 4

GPT-5.6 Astrawon 2 · lost 4

Granite 3.0 8B Instructwon 2 · lost 4

MiMo V2.6 Prowon 2 · lost 4

Llama 3 8B Instructwon 2 · lost 4

GPT OSS Safeguard 20Bwon 2 · lost 4

Gemma 3 12B ITwon 2 · lost 4

K2 Chatwon 2 · lost 4

Amazon Nova Prowon 2 · lost 4

Llama 3.1 Tulu 3 8B DPOwon 2 · lost 4

Llama 3.1 Tulu 3 8Bwon 2 · lost 4

Qwen3.5 27Bwon 2 · lost 4

Qwen3.5 122B A10Bwon 2 · lost 4

Amazon Nova Litewon 2 · lost 4

Llama 3.2 Vision (11B)won 2 · lost 4

Titan Text Express V1won 2 · lost 4

Llama 2 13B Chatwon 2 · lost 4

Llama 2 70B Chatwon 2 · lost 4

Nova Premierwon 2 · lost 4

Fugu Ultra V2won 2 · lost 4

Granite 4.0 H Smallwon 2 · lost 4

Qwen3.5 397B A17Bwon 2 · lost 4

Claude 3.5 Haikuwon 2 · lost 5

GPT-4o miniwon 2 · lost 5

Claude Haiku 4.5won 2 · lost 5

GPT-5 miniwon 2 · lost 5

GPT-5 nanowon 2 · lost 5

O3 Miniwon 2 · lost 5

Llama 4 Maverick Instructwon 2 · lost 5

GPT-4.1won 2 · lost 5

O4 Miniwon 2 · lost 5

Claude 3.7 Sonnetwon 2 · lost 5

O1won 2 · lost 5

O3won 2 · lost 5

Claude Opus 4won 2 · lost 5

GPT-5.2won 2 · lost 5

GPT-5.1won 2 · lost 5

GPT-5won 2 · lost 5

Claude Sonnet 4.5won 2 · lost 5

Grok 4.6won 2 · lost 5

Claude Opus 4.8won 2 · lost 5

Claude Opus 5won 2 · lost 5

GPT-5.6 Terrawon 2 · lost 5

GPT-5.5won 2 · lost 5

Claude 3.5 Sonnetwon 2 · lost 6

Smaug Llama 3 70B Instructwon 1 · lost 5

SeaLLM 7B V2won 1 · lost 5

Apertus 70B Instructwon 1 · lost 5

Granite 4.0 H Tinywon 1 · lost 5

Phi-3 Small 8K Instructwon 1 · lost 5

Llama 3 70B Instructwon 1 · lost 5

Qwen3.5 35B A3Bwon 1 · lost 5

Qwen Maxwon 1 · lost 5

GPT-4won 1 · lost 5

Aya Expanse 8Bwon 1 · lost 5

Llama 3.1 Nemotron 70B Instructwon 1 · lost 5

Internlm2 Chat 20Bwon 1 · lost 5

Llama 3 8B Instruct RRwon 1 · lost 5

Llama 2 7B Chatwon 1 · lost 5

Sarvam 105Bwon 1 · lost 5

Qwen2 72B Instructwon 1 · lost 5

Llama 3 8B Instruct MopeyMulewon 1 · lost 5

Claude 3 Sonnetwon 1 · lost 5

Llama 3.2 Vision (90B)won 1 · lost 5

Cogito 671B V2 P1won 1 · lost 5

Qwen2.5 72B Instructwon 1 · lost 5

O1 Miniwon 1 · lost 5

Hy3won 1 · lost 5

MiniMax M2.7won 1 · lost 6

Claude Sonnet 4won 1 · lost 6

Inklingwon 1 · lost 6

Claude Sonnet 5won 1 · lost 6

Claude Opus 4.7won 1 · lost 6

GPT-5.6 Solwon 1 · lost 6

Claude Opus 5.5won 1 · lost 6

GPT-4owon 1 · lost 7

Phi 3.5 MoE Instructwon 0 · lost 6

Aya Expanse 32Bwon 0 · lost 6

Foundation Sec 8B Instructwon 0 · lost 6

Gemma 2 27B ITwon 0 · lost 6

Meta SecAlign 8Bwon 0 · lost 6

Apriel 1.5 15B Thinkerwon 0 · lost 6

GPT-4 Turbowon 0 · lost 6

Mistral NeMo Minitron 8B Instructwon 0 · lost 6

Phi 4won 0 · lost 6

MiniMax M2won 0 · lost 6

Claude 3 Opuswon 0 · lost 6

Grok 4.7won 0 · lost 6

§ 3 · Sources

Where the numbers come from

2 publications, 7 figures. Every one links to the page it was read from.

MMMU-Pro ↗1 measureread 2026-11-11

MMMU-Pro 46.2%

Enkrypt AI Safety Leaderboard ↗6 measuresread 2026-11-11

Enkrypt · Jailbreak risk 9.5%Enkrypt · Harmful content risk 41.7%Enkrypt · CBRN risk 15.3%Enkrypt · Toxicity risk 2.4%Enkrypt · Bias risk 85.3%Enkrypt · Insecure code risk 18.7%

Badge

[![PublicAI Index](https://publicai.io/model-index/badge?model=qwen2-vl-72b)](https://publicai.io/model-index/m/qwen2-vl-72b)