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The LLM benchmark aggregator.

Qwen2 VL 72B

Alibaba · 72B

Strongest in Knowledge (#1 of 138, on 1 of its 2 boards), weakest in Fairness (#194 of 299). 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 3.8 Flash and DeepSeek V4 Pro.

§ 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.

Knowledge63.5#1/138
Multimodal understanding66.2#4/19
Safety50.9#160/336
Secure code57.3#94/274
Toxicity avoidance55.6#95/272
Jailbreak resistance53.9#132/272
Harm refusal48.1#187/299
Fairness45.2#194/299

§ 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
Qwen2.5 0.5B 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
Open Mixtral 8x7bwon 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
Jamba 1.6 Miniwon 7 · lost 0
Mistral Smallwon 7 · lost 0
Command Awon 7 · lost 0
GLM-4.6won 7 · lost 0
Grok 4.1 Fastwon 7 · lost 0
Command R Pluswon 6 · lost 1
Jamba 1.6 Largewon 6 · lost 1
DeepSeek V3won 6 · lost 1
Grok 3won 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
Grok 4.3won 6 · lost 1
DeepSeek V4 Flashwon 6 · lost 1
GLM-5.2won 6 · lost 1
Gemini 3.6 Flashwon 6 · lost 1
OLMo 7B Instruct Hfwon 5 · lost 1
RakutenAI 7B Chatwon 5 · lost 1
PowerMoE 3Bwon 5 · lost 1
Phi 2won 5 · lost 1
Qwen2.5 7B Instructwon 5 · lost 1
Gemma 7B Itwon 5 · lost 1
PowerLM 3Bwon 5 · lost 1
LFM2 350Mwon 5 · lost 1
Qwen3 30B A3B Instructwon 5 · lost 1
GLM-4 9B Chatwon 5 · lost 1
SmolLM2 360M Instructwon 5 · lost 1
Jamba Mini 1.7won 5 · lost 1
Gemma 2B Itwon 5 · lost 1
Magistral Smallwon 5 · lost 1
Aya 23 8Bwon 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
Phi-4 Mini Instructwon 5 · lost 1
Open Codestral Mambawon 5 · lost 1
Jamba 1.5 Miniwon 5 · lost 1
LFM2 1.2Bwon 5 · lost 1
DeepSeek V3p1won 5 · lost 1
Gemma 4 E2Bwon 5 · lost 1
QwQ 32Bwon 5 · lost 1
Sarvam 1won 5 · lost 1
Hunyuan A13B Instructwon 5 · lost 1
Gemma 3 4B Itwon 5 · lost 1
Gemma 4 26B A4Bwon 5 · lost 1
Mistral Largewon 5 · lost 2
Gemma 4 31Bwon 5 · lost 2
Kimi K2 Thinkingwon 5 · lost 2
DeepSeek R1won 5 · lost 2
MiniMax M3won 5 · lost 2
DeepSeek V3.2won 5 · lost 2
GLM-5.3 Flashwon 5 · lost 2
Kimi K2.6won 5 · lost 2
Qwen3.7 Maxwon 5 · lost 2
Gemini 3.5 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
Llama 3.1 8B Instructwon 4 · lost 2
Grok Build 0.1won 4 · lost 2
Qwen1.5 14B Chatwon 4 · lost 2
NexusRaven V2 13Bwon 4 · lost 2
Gemma 2 9B Itwon 4 · lost 2
Gemma 4 E4Bwon 4 · lost 2
SeaLLMs V3 7B Chatwon 4 · lost 2
DeepSeek Reasonerwon 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
Gemini 2.0 Flash Litewon 4 · lost 2
Starling Lm 7B Betawon 4 · lost 2
Open Mixtral 8x22bwon 4 · lost 2
Apertus 8B Instructwon 4 · lost 2
DeepSeek Chatwon 4 · lost 2
Llama 3 8B Instructwon 4 · lost 2
Sarvam 30Bwon 4 · 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
DeepSeek V4 Prowon 4 · lost 3
Gemini 3.8 Flashwon 4 · lost 3
Aya 23 35Bwon 3 · lost 3
Yi 1.5 9B Chatwon 3 · lost 3
Gemma 2 2B Itwon 3 · lost 3
IBM Granite 4.0 Microwon 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
Granite 4.0 H Microwon 3 · lost 3
Phi 3.5 Mini Instructwon 3 · lost 3
Krutrim 2 Instructwon 3 · lost 3
EAI SageAlign 8B LoRAwon 3 · lost 3
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
GPT-5.6 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
Claude Sonnet 4.6won 2 · lost 3
Claude Opus 4.6won 2 · lost 3
GPT-5.6 Solwon 3 · lost 5
Claude Opus 5won 3 · lost 5
Claude 3 Haikuwon 2 · lost 4
Qwen2.5 14B Instructwon 2 · lost 4
Gemma 3 27B Itwon 2 · lost 4
Llama 3 70B Chat HFwon 2 · lost 4
GPT-5.6 Astrawon 2 · lost 4
Mimo V2.6 Prowon 2 · lost 4
GPT OSS Safeguard 20Bwon 2 · lost 4
Gemma 3 12B Itwon 2 · lost 4
GPT-6 Solwon 2 · lost 4
K2 Chatwon 2 · lost 4
Phi 3 Mini 4k Instructwon 2 · lost 4
Nova Prowon 2 · lost 4
Llama 3.1 Tulu 3 8Bwon 2 · lost 4
Qwen3.5 27Bwon 2 · lost 4
GPT-6 Lunawon 2 · lost 4
Qwen3.5 122B A10Bwon 2 · lost 4
Llama 3 8B Chat Hfwon 2 · lost 4
Nova Litewon 2 · lost 4
Llama 3.2 Vision (11B)won 2 · lost 4
Titan Text Express V1won 2 · lost 4
Llama 2 70B Chat Hfwon 2 · lost 4
Llama 2 13B Chat Hfwon 2 · lost 4
Fugu Ultra V2won 2 · lost 4
Nova Premierwon 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-5 Nanowon 2 · lost 5
O3 Miniwon 2 · lost 5
Claude Haiku 4.5won 2 · lost 5
GPT-4.1won 2 · lost 5
O4 Miniwon 2 · lost 5
GPT-5 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
GPT-5.6 Terrawon 2 · lost 5
Claude Sonnet 4.5won 2 · lost 5
GPT-5.5won 2 · lost 5
Grok 4.6won 2 · lost 5
Claude Opus 4.8won 2 · lost 5
GPT-4o Miniwon 2 · lost 6
Claude 3.5 Sonnetwon 2 · lost 6
SeaLLM 7B V2won 1 · lost 5
Apertus 70B Instructwon 1 · lost 5
Granite 4.0 H Tinywon 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
C4ai Aya Expanse 8Bwon 1 · lost 5
Internlm2 Chat 20Bwon 1 · lost 5
Qwen2.5 72B Instructwon 1 · lost 5
Llama 3 8B Instruct RRwon 1 · lost 5
Llama 2 7B Chat Hfwon 1 · lost 5
Sarvam 105Bwon 1 · lost 5
Qwen2 72B Instructwon 1 · lost 5
Claude 3 Sonnetwon 1 · lost 5
Cogito 671B V2 P1won 1 · lost 5
O1 Miniwon 1 · lost 5
Hy3won 1 · lost 5
Claude Opus 5.5won 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-4owon 1 · lost 7
Phi 3.5 MoE Instructwon 0 · lost 6
C4ai Aya Expanse 32Bwon 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
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 2026-09-27
MMMU-Pro 46.2%
Enkrypt AI Safety Leaderboard ↗6 measuresread 2026-09-27
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 badge for Qwen2 VL 72B[![PublicAI Index](https://publicai.io/model-index/badge?model=qwen2-vl-72b)](https://publicai.io/model-index/m/qwen2-vl-72b)