The LLM benchmark aggregator.

QwQ 32B

Alibaba · 32B

Strongest in Toxicity avoidance (#119 of 268), weakest in Fairness (#269 of 292). Above par in 4 of 10 scopes. Among the models it meets almost everywhere, it finishes behind Claude Opus 4.7 and Claude Opus 4.8 and ahead of Grok 4.1 Fast and NVIDIA Nemotron 3 Ultra 550B A55B.

§ 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.

Coding45.1−21.1#127/1791/6

Agentic coding44.6−22.3#124/1731/5

Human preference50.5−18.3#191/3541/1

Safety47.9−13.5#235/3291/3

Toxicity avoidance54.5−4.5#119/2681/1

Jailbreak resistance54.7−10.3#120/2681/1

Secure code46.8−19.4#185/2701/1

Harm refusal46.4−15.1#213/2921/2

Fairness40.2−29.8#269/2921/2

§ 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 QwQ 32B, left for the other.

OLMoE 1B-7B Instructwon 6 · lost 0

SmolLM 1.7B Instructwon 6 · lost 0

Qwen2.5 0.5B Instructwon 6 · lost 0

SmolLM2 1.7B Instructwon 8 · lost 0

OLMo 7B Instructwon 7 · lost 1

Gemma 2B ITwon 7 · lost 1

Zephyr 7B Betawon 7 · lost 1

Gemma 7B ITwon 7 · lost 1

Nous Hermes 2 Mixtral 8x7B DPOwon 7 · lost 1

DBRX Instructwon 7 · lost 1

Mixtral 8x7B Instructwon 7 · lost 1

GPT-3.5 Turbowon 7 · lost 1

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

RakutenAI 7B Chatwon 5 · lost 1

PowerMoE 3Bwon 5 · lost 1

Phi 2won 5 · lost 1

H2o Danube3 500M Chatwon 5 · lost 1

PowerLM 3Bwon 5 · lost 1

Laguna Xs 2.1won 5 · lost 1

RakutenAI 7B Instructwon 5 · lost 1

Falcon Mamba 7B Instructwon 5 · lost 1

SmolLM2 360M Instructwon 5 · lost 1

CodeLlama 7B Instructwon 5 · lost 1

Magistral Smallwon 5 · lost 1

Jamba Mini 1.7won 5 · lost 1

Magistral Small 1.2won 5 · lost 1

Falcon H1 0.5B Instructwon 5 · lost 1

Command R7Bwon 5 · lost 1

LFM2 700Mwon 5 · lost 1

SmolLM 360M Instructwon 5 · lost 1

Open Codestral Mambawon 5 · lost 1

Grok Build 0.1won 6 · lost 2

Jamba 1.5 Miniwon 6 · lost 2

Gemma 2 9B ITwon 6 · lost 2

Mistral Small 3 24B Instructwon 6 · lost 2

Jamba 1.5 Largewon 6 · lost 2

Llama 3.1 70B Instructwon 6 · lost 2

Magistral Mediumwon 6 · lost 2

Command Awon 7 · lost 3

LFM2 350Mwon 4 · lost 2

GLM-4 9B Chatwon 4 · lost 2

SEA LION V1 7B ITwon 4 · lost 2

DeepSeek R1 Distill Llama 8Bwon 4 · lost 2

Jamba 1.6 Miniwon 4 · lost 2

Jamba Large 1.7won 4 · lost 2

NexusRaven V2 13Bwon 4 · lost 2

Qwen3 8Bwon 4 · lost 2

Mimo V2.6 Pro Ultraspeedwon 4 · lost 2

Qwen2.5 3B Instructwon 4 · lost 2

LFM2 1.2Bwon 4 · lost 2

Granite 3.0 1B A400M Instructwon 4 · lost 2

Sarvam 1won 4 · lost 2

Ling 3.0 Flashwon 5 · lost 3

Mistral 7B Instruct v0.2won 5 · lost 3

DeepSeek LLM 67B Chatwon 5 · lost 3

Qwen1.5 14B Chatwon 5 · lost 3

Command R+won 5 · lost 3

Gemma 3 4B ITwon 5 · lost 3

GPT-4.1 nanowon 6 · lost 4

GLM-4.6won 6 · lost 4

NVIDIA Nemotron 3 Ultra 550B A55Bwon 6 · lost 4

Grok 4.1 Fastwon 6 · lost 4

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

Qwen2 7B Instructwon 3 · lost 3

OLMo 2 7B Instructwon 3 · lost 3

Aya 23 8Bwon 3 · lost 3

Laguna S 2.1won 3 · lost 3

DeepSeek R1 Distill Qwen 7Bwon 3 · lost 3

Ling 3.0 Flash VLwon 3 · lost 3

Phi-4 Mini Instructwon 3 · lost 3

Jamba 1.6 Largewon 3 · lost 3

Gemma 4 E2Bwon 3 · lost 3

Komodo 7B Basewon 3 · lost 3

Fuguwon 3 · lost 3

Qwen 2.5 Instruct (7B)won 3 · lost 3

Sarvam 30Bwon 3 · lost 3

EAI Llama 3.1 8B Instruct SageAlignwon 3 · lost 3

Llama 3.2 1B Instructwon 4 · lost 4

Llama 3.2 3B Instructwon 4 · lost 4

Gemma 2 2B ITwon 4 · lost 4

Mixtral 8x22B Instructwon 4 · lost 4

Amazon Nova Prowon 4 · lost 4

Gemini 1.5 Flashwon 4 · lost 4

GPT OSS 20Bwon 4 · lost 4

Llama 3.1 405B Instructwon 4 · lost 4

Llama 4 Maverick Instructwon 5 · lost 5

Grok 3won 5 · lost 5

Grok 4.3won 5 · lost 5

Qwen3.7 Maxwon 5 · lost 5

Claude 3 Haikuwon 4 · lost 5

GPT-4o miniwon 4 · lost 6

GPT OSS 120Bwon 4 · lost 6

DeepSeek V3won 4 · lost 6

Grok 4won 4 · lost 6

DeepSeek R1won 4 · lost 6

MiMo V2.6 Flashwon 4 · lost 6

Kimi K2.5won 4 · lost 6

Gemma 4 31Bwon 4 · lost 6

Gemini 3.6 Flashwon 4 · lost 6

Phi-3 Mini 128K Instructwon 3 · lost 5

Phi-3 Mini 4K Instructwon 3 · lost 5

Starling LM 7B Betawon 3 · lost 5

Granite 3.1 2B Instructwon 3 · lost 5

Granite 3.1 8B Instructwon 3 · lost 5

Llama 3.1 8B Instructwon 3 · lost 5

Amazon Nova Litewon 3 · lost 5

GPT-4won 3 · lost 5

Llama 3 70B Instructwon 3 · lost 5

Granite 4.0 H Smallwon 3 · lost 5

Llama 3.1 Nemotron 70B Instructwon 3 · lost 5

Llama 3.3 70B Instructwon 3 · lost 5

Llama 4 Scout Instructwon 3 · lost 5

NVIDIA Nemotron 3 Super 120B A12Bwon 3 · lost 5

Qwen3 30B A3B Instructwon 3 · lost 5

GLM-4.5won 3 · lost 5

Granite 3.2 8B Instructwon 2 · lost 4

Aya 23 35Bwon 2 · lost 4

Jamba Instructwon 2 · lost 4

Yi 1.5 9B Chatwon 2 · lost 4

Gemma 4 E4Bwon 2 · lost 4

Qwen2.5 7B Instruct 1Mwon 2 · lost 4

SeaLLMs V3 7B Chatwon 2 · lost 4

DeepSeek Reasonerwon 2 · lost 4

Qwen2.5 1.5B Instructwon 2 · lost 4

WizardLM 2.8x22Bwon 2 · lost 4

Hunyuan A13B Instructwon 2 · lost 4

Dolphin 2.5 Mixtral 8x7bwon 2 · lost 4

Gemma2 9B Cpt Sea Lionv3 Instructwon 2 · lost 4

DeepSeek Chatwon 2 · lost 4

Qwen2.5 14B Instruct 1Mwon 2 · lost 4

GLM-5 Turbowon 2 · lost 4

Phi 3 Small 128k Instructwon 2 · lost 4

GPT-5.6 Astrawon 2 · lost 4

EAI SageAlign 8B LoRAwon 2 · lost 4

Fugu Ultra V2won 2 · lost 4

GPT-4 Turbowon 3 · lost 6

Claude 3.5 Haikuwon 3 · lost 7

GPT-5 miniwon 3 · lost 7

Claude Haiku 4.5won 3 · lost 7

DeepSeek V3.2won 3 · lost 7

MiniMax M3won 3 · lost 7

DeepSeek V4 Flashwon 3 · lost 7

Kimi K2.6won 3 · lost 7

GLM-5.2won 3 · lost 7

Gemini 3.5 Flashwon 3 · lost 7

GPT-4owon 2 · lost 6

Llama 2 7B Chatwon 2 · lost 6

Llama 2 13B Chatwon 2 · lost 6

Granite 3.0 2B Instructwon 2 · lost 6

Llama 2 70B Chatwon 2 · lost 6

Phi-3 Small 8K Instructwon 2 · lost 6

Granite 3.0 8B Instructwon 2 · lost 6

Phi-3 Medium 4K Instructwon 2 · lost 6

Llama 3.1 Tulu 3 8Bwon 2 · lost 6

Aya Expanse 8Bwon 2 · lost 6

Llama 3 8B Instructwon 2 · lost 6

Amazon Nova Microwon 2 · lost 6

Phi 4won 2 · lost 6

Qwen2 72B Instructwon 2 · lost 6

Aya Expanse 32Bwon 2 · lost 6

Claude 3 Sonnetwon 2 · lost 6

Gemma 2 27B ITwon 2 · lost 6

Qwen2.5 72B Instructwon 2 · lost 6

Qwen Maxwon 2 · lost 6

Claude 3 Opuswon 2 · lost 6

Gemini 1.5 Prowon 2 · lost 6

Gemini 2.0 Flash-Litewon 2 · lost 6

Qwen3.5 27Bwon 2 · lost 6

Qwen3 VL 235B A22B Instructwon 2 · lost 6

DeepSeek V3.1won 2 · lost 6

Qwen3.5 122B A10Bwon 2 · lost 6

Gemini 2.0 Flashwon 2 · lost 8

Gemma 3 27B ITwon 2 · lost 8

GPT-4.1 miniwon 2 · lost 8

Gemini 2.5 Flashwon 2 · lost 8

MiniMax M2.7won 2 · lost 8

Kimi K2won 2 · lost 8

Qwen3 235B A22B Instructwon 2 · lost 8

GPT-6 Lunawon 2 · lost 8

Inklingwon 2 · lost 8

Gemini 2.5 Prowon 2 · lost 8

GPT-5.6 Lunawon 2 · lost 8

GPT-6 Solwon 2 · lost 8

DeepSeek V4 Prowon 2 · lost 8

GLM-5.1won 2 · lost 8

Grok 4.5won 2 · lost 8

MiMo V2.5 Prowon 2 · lost 8

GLM-5.3 Flashwon 2 · lost 8

GPT-5.5won 2 · lost 8

Qwen3.8 Maxwon 2 · lost 8

GPT-5.6 Solwon 2 · lost 8

Gemini 3.8 Flashwon 2 · lost 8

IBM Granite 4.0 Microwon 1 · lost 5

Granite 3.2 2B Instructwon 1 · lost 5

Reflection Llama 3.1 70Bwon 1 · lost 5

Apertus 8B Instructwon 1 · lost 5

Qwen2.5 32B Instructwon 1 · lost 5

LongWriter Glm4 9Bwon 1 · lost 5

Qwen2 VL 72Bwon 1 · lost 5

Granite 3.1 3B A800M Instructwon 1 · lost 5

Qwen2 57B A14B Instructwon 1 · lost 5

Smaug Llama 3 70B Instructwon 1 · lost 5

Granite 4.0 H Microwon 1 · lost 5

Granite 4.0 H Tinywon 1 · lost 5

K2 Chatwon 1 · lost 5

Krutrim 2 Instructwon 1 · lost 5

Internlm2 Chat 20Bwon 1 · lost 5

Nova Premierwon 1 · lost 5

Llama 3 8B Instruct MopeyMulewon 1 · lost 5

Llama 3.2 Vision (90B)won 1 · lost 5

Cogito 671B V2 P1won 1 · lost 5

Kimi K2 Thinkingwon 1 · lost 7

Kimi K2.7 Codewon 1 · lost 7

Mercury 2won 1 · lost 7

Qwen3.5 35B A3Bwon 1 · lost 7

Gemma 4 26B A4Bwon 1 · lost 7

Qwen3.5 397B A17Bwon 1 · lost 7

Claude Opus 4.6won 1 · lost 7

O3 Miniwon 1 · lost 9

Claude 3.7 Sonnetwon 1 · lost 9

O4 Miniwon 1 · lost 9

GPT-4.1won 1 · lost 9

Mistral Large 4won 1 · lost 9

O3won 1 · lost 9

GPT-5.2won 1 · lost 9

GPT-5.1won 1 · lost 9

Grok 4.6won 1 · lost 9

Claude Sonnet 4.5won 1 · lost 9

GPT-5.6 Terrawon 1 · lost 9

Claude Opus 5won 1 · lost 9

Muse Spark 1.1won 1 · lost 9

Qwen2.5 14B Instructwon 0 · lost 6

Phi 3 Medium 128k Instructwon 0 · lost 6

SeaLLM 7B V2won 0 · lost 6

Apertus 70B Instructwon 0 · lost 6

Phi 3.5 Mini Instructwon 0 · lost 6

Llama 3.1 Tulu 3 8B SFTwon 0 · lost 6

Llama 3.1 Tulu 3 8B DPOwon 0 · lost 6

Phi 3.5 MoE Instructwon 0 · lost 6

Llama 3.2 Vision (11B)won 0 · lost 6

Titan Text Express V1won 0 · lost 6

Llama 3 8B Instruct RRwon 0 · lost 6

Sarvam 105Bwon 0 · lost 6

Foundation Sec 8B Instructwon 0 · lost 6

Meta SecAlign 8Bwon 0 · lost 6

Apriel 1.5 15B Thinkerwon 0 · lost 6

Mistral NeMo Minitron 8B Instructwon 0 · lost 6

GPT-5 nanowon 0 · lost 7

Grok 3 Miniwon 0 · lost 7

GPT-4.5won 0 · lost 7

Hy3won 0 · lost 7

Gemini 3 Prowon 0 · lost 7

Gemma 3 12B ITwon 0 · lost 8

MiniMax M2won 0 · lost 8

Claude Sonnet 4.6won 0 · lost 8

Claude Opus 5.5won 0 · lost 9

O1 Miniwon 0 · lost 10

Claude 3.5 Sonnetwon 0 · lost 10

O1won 0 · lost 10

Claude Sonnet 4won 0 · lost 10

Claude Opus 4won 0 · lost 10

GPT-5won 0 · lost 10

Grok 4.7won 0 · lost 10

Claude Sonnet 5won 0 · lost 10

GPT-5.4won 0 · lost 10

MiMo V2.6 Prowon 0 · lost 10

Claude Opus 4.8won 0 · lost 10

Claude Opus 4.7won 0 · lost 10

§ 3 · Sources

Where the numbers come from

3 publications, 8 figures. Every one links to the page it was read from.

LMArena Text ↗1 measureread 2026-11-10

LMArena Text 1336

Aider polyglot ↗1 measureread 2026-11-10

Aider polyglot 20.9%

Enkrypt AI Safety Leaderboard ↗6 measuresread 2026-11-10

Enkrypt · Jailbreak risk 8.9%Enkrypt · Harmful content risk 66.1%Enkrypt · CBRN risk 7%Enkrypt · Toxicity risk 3.1%Enkrypt · Bias risk 93%Enkrypt · Insecure code risk 39.6%

Badge

[![PublicAI Index](https://publicai.io/model-index/badge?model=qwq-32b)](https://publicai.io/model-index/m/qwq-32b)