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The LLM benchmark aggregator.

Llama 3.2 90B Vision Instruct Turbo

Meta · 90B

Strongest in Fairness (#13 of 299, on 1 of its 2 boards), weakest in Secure code (#218 of 274). Above par in 5 of 6 scopes. Among the models it meets almost everywhere, it finishes behind Claude Sonnet 5 and MiniMax M2.7 and ahead of Qwen3.5 397B A17B and Qwen 2.5 Instruct Turbo (72B).

§ 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.

Safety55.7#40/336
Fairness69.3#13/299
Jailbreak resistance60.4#39/272
Harm refusal54.2#85/299
Toxicity avoidance56#85/272
Secure code40.7#218/274

§ 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 Llama 3.2 90B Vision Instruct Turbo, left for the other.

OLMoE 1B-7B Instructwon 6 · lost 0
OLMo 7B Instruct Hfwon 6 · lost 0
RakutenAI 7B Chatwon 6 · lost 0
SmolLM 1.7B Instructwon 6 · lost 0
PowerMoE 3Bwon 6 · lost 0
Phi 2won 6 · lost 0
Qwen2.5 7B Instructwon 6 · lost 0
Gemma 7B Itwon 6 · lost 0
H2o Danube3 500M Chatwon 6 · lost 0
PowerLM 3Bwon 6 · lost 0
Smollm2 1.7B Instructwon 6 · lost 0
LFM2 350Mwon 6 · lost 0
RakutenAI 7B Instructwon 6 · lost 0
SmolLM2 360M Instructwon 6 · lost 0
Qwen2.5 0.5B Instructwon 6 · lost 0
Jamba Mini 1.7won 6 · lost 0
Magistral Smallwon 6 · lost 0
Aya 23 8Bwon 6 · lost 0
SEA LION V1 7B ITwon 6 · lost 0
H2o Danube3 4B Chatwon 6 · lost 0
Jamba 1.6 Miniwon 6 · lost 0
LFM2 700Mwon 6 · lost 0
Command R Pluswon 6 · lost 0
NexusRaven V2 13Bwon 6 · lost 0
Jamba 1.5 Largewon 6 · lost 0
Open Codestral Mambawon 6 · lost 0
LFM2 1.2Bwon 6 · lost 0
Sarvam 1won 6 · lost 0
WizardLM 2.8x22Bwon 6 · lost 0
Llama 3.2 3B Instructwon 6 · lost 0
Gemini 1.5 Prowon 6 · lost 0
DBRX Instructwon 5 · lost 1
Grok 4.1 Fastwon 5 · lost 1
Laguna Xs 2.1won 5 · lost 1
GLM-4 9B Chatwon 5 · lost 1
Qwen2 7B Instructwon 5 · lost 1
OLMo 2 7B Instructwon 5 · lost 1
Grok 4won 5 · lost 1
Gemma 2B Itwon 5 · lost 1
Grok 3won 5 · lost 1
Laguna S 2.1won 5 · lost 1
GPT-3.5 Turbowon 5 · lost 1
Llama 3.1 8B Instructwon 5 · lost 1
Grok Build 0.1won 5 · lost 1
Qwen1.5 14B Chatwon 5 · lost 1
DeepSeek R1won 5 · lost 1
Zephyr 7B Betawon 5 · lost 1
Ling 3.0 Flash VLwon 5 · lost 1
Mimo V2.6 Flashwon 5 · lost 1
Magistral Mediumwon 5 · lost 1
Gemini 3.6 Flashwon 5 · lost 1
Command R7Bwon 5 · lost 1
DeepSeek V4 Flashwon 5 · lost 1
Command Awon 5 · lost 1
Jamba Large 1.7won 5 · lost 1
SmolLM 360M Instructwon 5 · lost 1
Ling 3.0 Flashwon 5 · lost 1
Open Mixtral 8x7bwon 5 · lost 1
Gemma 2 9B Itwon 5 · lost 1
GLM-5.2won 5 · lost 1
DeepSeek LLM 67B Chatwon 5 · lost 1
Gemini 3.5 Flashwon 5 · lost 1
GLM-4.6won 5 · lost 1
Grok 4.3won 5 · lost 1
Phi-4 Mini Instructwon 5 · lost 1
Jamba 1.5 Miniwon 5 · lost 1
DeepSeek V3won 5 · lost 1
Qwen2.5 3B Instructwon 5 · lost 1
Aya 23 35Bwon 5 · lost 1
MiniMax M3won 5 · lost 1
DeepSeek V3p1won 5 · lost 1
Gemma 4 E2Bwon 5 · lost 1
QwQ 32Bwon 5 · lost 1
Jamba 1.6 Largewon 5 · lost 1
GLM-4.5won 5 · lost 1
Kimi K2.6won 5 · lost 1
Jamba Instructwon 5 · lost 1
Qwen3.7 Maxwon 5 · lost 1
SeaLLMs V3 7B Chatwon 5 · lost 1
Qwen2.5 7B Instruct 1Mwon 5 · lost 1
Mistral Smallwon 5 · lost 1
Kimi K2.5won 5 · lost 1
DeepSeek Reasonerwon 5 · lost 1
Qwen2.5 1.5B Instructwon 5 · lost 1
DeepSeek V3.2won 5 · lost 1
Llama 4 Scout Instructwon 5 · lost 1
Gemma 2 2B Itwon 5 · lost 1
Kimi K2.7 Codewon 5 · lost 1
Gemma 4 12Bwon 5 · lost 1
Hunyuan A13B Instructwon 5 · lost 1
Fuguwon 5 · lost 1
Gemini 2.0 Flash Litewon 5 · lost 1
Gemma 3 4B Itwon 5 · lost 1
Gemma 4 31Bwon 5 · lost 1
Open Mixtral 8x22bwon 5 · lost 1
Gemini 2.5 Prowon 5 · lost 1
GLM-5.3 Flashwon 5 · lost 1
Gemma 4 26B A4Bwon 5 · lost 1
DeepSeek Chatwon 5 · lost 1
DeepSeek V4 Prowon 5 · lost 1
LongWriter Glm4 9Bwon 5 · lost 1
Gemini 2.5 Flashwon 5 · lost 1
Gemini 1.5 Flashwon 5 · lost 1
Qwen2 VL 72Bwon 5 · lost 1
GLM-5 Turbowon 5 · lost 1
Llama 3 8B Instructwon 5 · lost 1
Qwen2.5 14B Instructwon 5 · lost 1
Gemini 2.0 Flashwon 5 · lost 1
Gemma 3 27B Itwon 5 · lost 1
GPT-5.6 Astrawon 5 · lost 1
Mimo V2.6 Prowon 5 · lost 1
Kimi K2 Thinkingwon 5 · lost 1
GPT OSS Safeguard 20Bwon 5 · lost 1
Gemini 3.8 Flashwon 5 · lost 1
GPT-6 Solwon 5 · lost 1
K2 Chatwon 5 · lost 1
GPT-4.1 Nanowon 5 · lost 1
Phi 3 Mini 4k Instructwon 5 · lost 1
Qwen3.8 Maxwon 5 · lost 1
GLM-5.1won 5 · lost 1
GPT-4.1 Miniwon 5 · lost 1
Qwen3.5 27Bwon 5 · lost 1
GPT-6 Lunawon 5 · lost 1
Qwen3.5 122B A10Bwon 5 · lost 1
GPT-5.6 Lunawon 5 · lost 1
Internlm2 Chat 20Bwon 5 · lost 1
Fugu Ultra V2won 5 · lost 1
Grok 4.5won 5 · lost 1
Qwen3 30B A3B Instructwon 4 · lost 2
Qwen3 8Bwon 4 · lost 2
Yi 1.5 9B Chatwon 4 · lost 2
Gemma 4 E4Bwon 4 · lost 2
Komodo 7B Basewon 4 · lost 2
IBM Granite 4.0 Microwon 4 · lost 2
Mimo V2.5 Prowon 4 · lost 2
Starling Lm 7B Betawon 4 · lost 2
Apertus 8B Instructwon 4 · lost 2
GPT OSS 20Bwon 4 · lost 2
Qwen2.5 32B Instructwon 4 · lost 2
Sarvam 30Bwon 4 · lost 2
Kimi K2won 4 · lost 2
Gemma 3 12B Itwon 4 · lost 2
SeaLLM 7B V2won 4 · lost 2
Apertus 70B Instructwon 4 · lost 2
Granite 4.0 H Microwon 4 · lost 2
Llama 3 70B Instructwon 4 · lost 2
Mistral Largewon 4 · lost 2
Phi 3.5 Mini Instructwon 4 · lost 2
Nova Prowon 4 · lost 2
GPT-4o Miniwon 4 · lost 2
GPT-5.6 Solwon 4 · lost 2
Qwen3.5 35B A3Bwon 4 · lost 2
EAI SageAlign 8B LoRAwon 4 · lost 2
Qwen Maxwon 4 · lost 2
GPT-4won 4 · lost 2
O4 Miniwon 4 · lost 2
Phi 3.5 MoE Instructwon 4 · lost 2
Nova Microwon 4 · lost 2
Nova Litewon 4 · lost 2
C4ai Aya Expanse 32Bwon 4 · lost 2
GPT OSS 120Bwon 4 · lost 2
Qwen2.5 72B Instructwon 4 · lost 2
Nova Premierwon 4 · lost 2
GPT-5.5won 4 · lost 2
GPT-5.4won 4 · lost 2
O3 Miniwon 4 · lost 2
Qwen3.5 397B A17Bwon 4 · lost 2
Claude Opus 4.6won 2 · lost 2
Claude Sonnet 4.6won 2 · lost 2
Llama 3.2 1B Instructwon 3 · lost 3
Claude 3 Haikuwon 3 · lost 3
Llama 3 70B Chat HFwon 3 · lost 3
Granite 4.0 H Tinywon 3 · lost 3
Krutrim 2 Instructwon 3 · lost 3
Llama 3.1 Tulu 3 8Bwon 3 · lost 3
Llama 3 8B Chat Hfwon 3 · lost 3
C4ai Aya Expanse 8Bwon 3 · lost 3
Granite 4.0 H Smallwon 3 · lost 3
Claude 3.7 Sonnetwon 3 · lost 3
Sarvam 105Bwon 3 · lost 3
GPT-4owon 3 · lost 3
GPT-4.1won 3 · lost 3
GPT-5.6 Terrawon 3 · lost 3
Gemma 2 27B Itwon 3 · lost 3
GPT-5.1won 3 · lost 3
Claude Opus 5won 3 · lost 3
Grok 4.6won 3 · lost 3
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
Llama 3 8B Instruct RRwon 2 · lost 4
Llama 2 7B Chat Hfwon 2 · lost 4
Qwen2 72B Instructwon 2 · lost 4
Claude Haiku 4.5won 2 · lost 4
Claude 3 Sonnetwon 2 · lost 4
Cogito 671B V2 P1won 2 · lost 4
Claude Sonnet 4.5won 2 · lost 4
O1 Miniwon 2 · lost 4
Apriel 1.5 15B Thinkerwon 2 · lost 4
O3won 2 · lost 4
GPT-5.2won 2 · lost 4
O1won 2 · lost 4
GPT-5 Miniwon 2 · lost 4
MiniMax M2won 2 · lost 4
GPT-5won 2 · lost 4
GPT-5 Nanowon 2 · lost 4
Muse Spark 1.1won 2 · lost 4
Hy3won 2 · lost 4
Claude Opus 5.5won 2 · lost 4
Claude Opus 4won 1 · lost 5
Claude Opus 4.7won 1 · lost 5
Claude Sonnet 4won 1 · lost 5
Meta SecAlign 8Bwon 1 · lost 5
GPT-4 Turbowon 1 · lost 5
Phi 4won 1 · lost 5
Claude 3 Opuswon 1 · lost 5
Claude Opus 4.8won 1 · lost 5
Inklingwon 1 · lost 5
Claude 3.5 Sonnetwon 1 · lost 5
Claude 3.5 Haikuwon 0 · lost 6
Grok 4.7won 0 · lost 6
MiniMax M2.7won 0 · lost 6
Claude Sonnet 5won 0 · lost 6

§ 3 · Sources

Where the numbers come from

1 publication, 6 figures. Every one links to the page it was read from.

Enkrypt AI Safety Leaderboard ↗6 measuresread 2026-09-27
Enkrypt · Jailbreak risk 4%Enkrypt · Harmful content risk 25%Enkrypt · CBRN risk 8.3%Enkrypt · Toxicity risk 2.1%Enkrypt · Bias risk 47.8%Enkrypt · Insecure code risk 52.4%

Badge

PublicAI Index badge for Llama 3.2 90B Vision Instruct Turbo[![PublicAI Index](https://publicai.io/model-index/badge?model=llama-3-2-90b-vision-instruct-turbo)](https://publicai.io/model-index/m/llama-3-2-90b-vision-instruct-turbo)