[whatmodel.sh](../../README.md) / Qwen3.6 27B
# Qwen3.6 27B
Alibaba · released 2026-04 · 27.8B params · apache-2.0
**score 68.3** ±0.5 · #97 of 732 · from 79 benchmark results across 10 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Qwen3.6 27B among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅●▄▄▂▁ #81 of 313 20% 12
coding ▁▁▁▃▅▇█▇●▄▃▁ #84 of 469 15% 7
math ▁▁▃▅█▆▄●▄▂▁▁ #84 of 431 25% 9
vision & documents ▁▁▂▂▁▃▄▂▆●█▄ #52 of 153 10% 14
web search ▁▁▁▅●█▃▄·▁·▁ #54 of 84* 0.8% 1
agent tasks · #81 of 313 · 20% of its score
Beats [GPT-5](gpt-5.md), [Gemini 3 Flash (Preview)](gemini-3-flash-preview.md), [Grok 4.20 (0309 reasoning)](grok-4-20.md) here, though they rank higher overall. Trails [MiMo-V2.5 (310B-A15B)](mimo-v2-5.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SWE-bench Verified (maker) 77.2% ▁▁▁▁▁▁▂▄█●▁▁ beats 118/138 hard · avg 25% 2.9% sharp
SWE-bench Pro public set, Qwen-re… 53.5% ▄······▄█·●▄ beats 4/5 hard · avg 19% 2.7% at its level · several runs
SWE-bench Pro (split unspecified) 50.2% ▂·▁▁▁▁▂▃●▇▂▁ beats 27/58 very hard · avg 8.0% 2.5% sharp · at its level · small run
OSWorld-Verified (maker) 63.9% █▃·▂▅▂▃▂●▃▆▆ beats 17/27 hard · avg 26% 2.5% at its level · small run · several runs
Terminal-Bench 2.1 (maker) 63.4% ▁·▁·▁▁▄▃▆●▅█ beats 28/63 hard · avg 17% 2.0% sharp · at its level · small run
SWE-bench Multilingual (maker) 71.3% ▁··▁▃▁▅█●▂▂▁ beats 39/65 hard · avg 29% 1.4% at its level · small run
Terminal-Bench 2.0 (maker) 59.3% ▂▂▅▅███●▆▂▃▃ beats 26/36 hard · avg 15% 1.3% at its level · small run
Thinkingbox-bench pass@1 (507 sta… 32.9% ▅··▃·▃·●▃▅██ beats 5/15 very hard · avg 5.1% 1.1% sharp · at its level
EEGAgentBench overall (tool-based… 43.3% ▂·▁▁··●▇█▂·▁ beats 6/25 hard · avg 28% 0.9% blunt · at its level
MCP-Atlas (public set) 62.5% ▂·▁▂▂·▁▃●▆▆▂ beats 14/39 hard · avg 29% 0.9% at its level · small run
DeepSearchQA 71.1% ▄·●▄···▄▄··█ beats 1/6 medium · avg 42% 0.8% at its level · small run
WebArena-Verified (maker) 48.8% ●···█······█ beats 0/2 hard · avg 24% 0.7% at its level · small run
coding · #84 of 469 · 15% of its score
Beats [GPT-5.2](gpt-5-2.md), [Grok 4.20 (0309 reasoning)](grok-4-20.md), [GPT-5.4 mini](gpt-5-4-mini.md) here, though they rank higher overall. Trails [Llama 3.1 Nemotron Ultra 253B v1](llama-3-1-nemotron-ultra-253b.md), [GLM-4.7](glm-4-7.md), [Gemini 3.5 Flash-Lite](gemini-3-5-flash-lite.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SWE-bench Verified (maker) 77.2% ▁▁▁▁▁▁▂▄█●▁▁ beats 118/138 hard · avg 25% 2.9% sharp
SWE-bench Pro public set, Qwen-re… 53.5% ▄······▄█·●▄ beats 4/5 hard · avg 19% 2.7% at its level · several runs
Luau Leetcode fully solved (Torpe… 51.9% ▁▁▁▁·▁●▃▅▃█▇ beats 9/49 hard · avg 17% 2.6% sharp · at its level
SWE-bench Pro (split unspecified) 50.2% ▂·▁▁▁▁▂▃●▇▂▁ beats 27/58 very hard · avg 8.0% 2.5% sharp · at its level · small run
LiveCodeBench v6 (window unspecif… 83.9% ▂▃▄▄▅██▆▄●▄▆ beats 50/67 medium · avg 47% 1.8% small run
SWE-bench Multilingual (maker) 71.3% ▁··▁▃▁▅█●▂▂▁ beats 39/65 hard · avg 29% 1.4% at its level · small run
LiveCodeBench v6 (Feb–May 2025) (… 83.9% ▁▁▃▁█▅▄▇▅▅▃● beats 34/38 medium · avg 48% 1.4% typical
math · #84 of 431 · 25% of its score
Beats [Claude Sonnet 4.6](claude-sonnet-4-6.md), [Claude Opus 4.5](claude-opus-4-5.md), [Qwen3.5 397B-A17B](qwen3-5-397b-a17b.md) here, though they rank higher overall. Trails [Gemini 2.5 Flash](gemini-2-5-flash.md), [Granite 4.2 30B](granite-4-2-30b.md), [Qwen3-Next 80B-A3B Instruct](qwen3-next-80b-a3b-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
FrontierMath tiers 1–3 v2 35.1% ▂··▁▁▃▄●█▆▄▃ beats 25/86 very hard · avg 1.2% 13% sharp · at its level
HMMT February 2026 (maker) 84.3% ▃▃▂▅·███●▅▃▅ beats 26/35 medium · avg 36% 3.2% sharp
AIME 2026 (maker) 94.1% ▁▁▁▂▂▂▆█●▂·▁ beats 38/52 medium · avg 59% 2.4% sharp · maxed out · small run
IMO-AnswerBench (maker) 80.8% ▁▁·▁▁▁▄▆●▄▂▃ beats 25/44 medium · avg 36% 1.9% small run
OTIS mock AIME 2024–2025 91.1% ▃▃▄▃▃▃▇▇●▃▂▅ beats 147/197 very hard · avg 7.2% 1.3% sharp · noisy
HMMT February 2025 (maker) 93.8% ▁▁▁▃▁▅▅▃█●▁▄ beats 23/31 easy · avg 65% 1.1% maxed out · small run
MathVista (testmini) (maker) 87.4% ▁··▁▄▄▆▇▄▅█● beats 45/48 medium · avg 63% 0.8% small run
HMMT November 2025 (maker) 90.7% ▁▁····▁▅▃●▇▁ beats 15/23 easy · avg 68% 0.8% small run
DynaMath (maker) 85.6% ▂·····▂··█●▆ beats 7/10 medium · avg 63% 0.6% small run
vision & documents · #52 of 153 · 10% of its score
Beats [MiMo-V2.6-Pro](mimo-v2-6-pro.md), [MiMo-V2.6-Flash (309B-A15B)](mimo-v2-6-flash.md), [GPT-6 Luna](gpt-6-luna.md) here, though they rank higher overall. Trails [Qwen3.6 Flash](qwen3-6-flash.md), [Gemma 4 31B](gemma-4-31b.md), [Gemini 3.5 Flash-Lite](gemini-3-5-flash-lite.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
CharXiv (reasoning questions) (ma… 78.4% ▂▅▃▅▅▆▆▅▆●▆▂ beats 74/101 medium · avg 43% 2.3% small run · several runs
OmniDocBench 1.5 overall (higher … 89.4% ▂··▂▂▂··▂▂●▅ beats 8/13 easy · avg 70% 1.7% small run · several runs
MathVista (testmini) (maker) 87.4% ▁··▁▄▄▆▇▄▅█● beats 45/48 medium · avg 63% 0.8% small run
MMMU (val) (maker) 82.9% ▁····▁▁▃█▆▆● beats 51/53 medium · avg 57% 0.8% small run
MMMU Pro (maker) 75.8% ▂▃▁▃▆▇▃▇▇●█▅ beats 41/52 medium · avg 46% 0.8% at its level · small run
MMStar (maker) 81.4% ▃▅▂▃▂▂▆██▆●▅ beats 32/35 medium · avg 62% 0.6% small run
RealWorldQA (maker) 84.1% ▁▃▁▇▃▇▇▅█▁●▅ beats 34/39 easy · avg 72% 0.5% blunt · small run · several runs
CountBench (maker) 97.8% ▂▂▃▂·▂▂▃▃·▅● beats 16/18 very easy · avg 91% 0.5% maxed out · small run
ScreenSpot-Pro 76.1% ▂▃▅▆██●▂···▂ beats 22/24 medium · avg 52% 0.5% blunt · at its level · small run
SimpleVQA (maker) 56.1% ▃·▃·▃·▃·●▅▃▃ beats 5/10 hard · avg 34% 0.5% blunt · at its level · small run
MMBench-EN dev v1.1 (maker) 92.3% █▄···▂··▄▂●▆ beats 10/14 easy · avg 82% 0.4% blunt · maxed out · small run
CC-OCR (maker) 81.2% ▁·▂··▂▂▃▃▂▆● beats 21/26 easy · avg 72% 0.2% blunt · small run
OCRBench (maker) 89.4% ▁·▃▁▆▅▂█▅●▁▃ beats 28/37 easy · avg 83% 0.2% blunt · small run
PureDocBench document parsing (Av… 65.6% ▂·▁·▂●▅█·▁▂▂ beats 5/22 medium · avg 62% 0.1% blunt · at its level
web search · #54 of 84 · 0.8% of its score
Beats [GPT-5.2](gpt-5-2.md), [Kimi K2.6](kimi-k2-6.md), [GPT-5 Pro](gpt-5-pro.md) here, though they rank higher overall. Trails [GPT-4o mini (2024-07-18)](gpt-4o-mini-2024-07-18.md), [Mistral Small 3.1 24B (2503)](mistral-small-3-1.md), [Llama 4 Scout (109B-A17B)](llama-4-scout.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
DeepSearchQA 71.1% ▄·●▄···▄▄··█ beats 1/6 medium · avg 42% 0.8% at its level · small run
other tests (30)
test result vs the others in its run difficulty counts why
GPQA Diamond 85.9% ▁▁▁▁▂▄▆▄▅●█▅ beats 150/211 hard · avg 44% 11% sharp · several runs
Humanity's Last Exam (maker) 24.0% ▁▁▁▄▆▆▇●█▆▁▃ beats 58/111 very hard · avg 5.1% 3.5% several runs
NL2Repo (maker) 36.2% ▄·····█·▄●·▄ beats 4/5 very hard · avg 8.9% 2.4% at its level · several runs
Mystery Game Puzzles (Epoch) 7.0% ●··▂▃▇▄█▂▄▂▁ beats 10/77 very hard · avg 9.4% 1.9% sharp · too hard for it · noisy
MMLU-Pro (maker) 86.2% ▁▁·▁▁▁▃▂▄▅●▁ beats 180/188 medium · avg 56% 1.8% typical
IFBench (aggregation unspecified)… 69.1% ▂▆▂▄▄▄▂▄▄●█▇ beats 57/86 medium · avg 41% 1.6% at its level · small run · several runs
Chess Puzzles (Epoch) 22.0% █▁▁▁▁▂▃●▃▂▁▁ beats 90/137 very hard · avg 5.8% 1.6% sharp · noisy
ERQA (maker) 62.5% ▃··▃··█·▃●▅▅ beats 8/12 medium · avg 36% 1.2% at its level · small run · several runs
CPTUB average (SpeakLeash, 0-5 ju… 88.2% ▁▂▂▂▃█▃▆▅▄▂● beats 94/95 medium · avg 65% 1.1% typical
AndroidWorld (maker) 70.3% ▁·▁▁▂▃█▅▂●·▂ beats 21/24 medium · avg 47% 1.1% at its level · small run · several runs
WildClawBench 43.2% ▂·▄▄▆▂▄●▆▂▂▂ beats 13/20 very hard · avg 14% 1.0% at its level · small run
RobloxQA v2.0 multiple choice (To… 89.9% ▁▁▃▁●▄▃▃▆▂▂▂ beats 15/49 easy · avg 77% 1.0% typical
C-Eval (maker) 91.4% ▁···▁▁▂▁▂▂▃● beats 28/33 medium · avg 70% 1.0% small run
QwenClawBench (Qwen internal) (ma… 53.4% █·█··█·····● beats 3/3 hard · avg 24% 0.9% at its level
MMLU-Redux (maker) 93.5% ▁▁▃▁▂▃▄▅▇▅●▄ beats 57/63 easy · avg 78% 0.9% maxed out · small run
Claw-Eval Pass^3 (maker) 60.6% █▄▄·▄·▄●·▄·▄ beats 6/8 hard · avg 25% 0.7% at its level
Gaia2 40.0% █·····●····█ beats 1/2 hard · avg 17% 0.7% at its level · small run
SkillsBench 78-task subset (OpenC… 48.2% █▄·········● beats 3/3 very hard · avg 11% 0.7% at its level
SuperGPQA (maker) 66.0% ▂▁▂▂▅▆▃▇▇▇●▃ beats 43/46 medium · avg 36% 0.6% at its level · small run
SkillsBench (with skills) 46.6% █·········█● beats 2/2 hard · avg 17% 0.6% at its level · small run
VideoMMMU (maker) 84.4% ▂···▂···▂▂█● beats 9/10 medium · avg 62% 0.6% small run
VLMs Are Blind (maker) 97.0% ▂·▂·····▃··● beats 7/8 very easy · avg 89% 0.6% maxed out · small run
Video-MME (with subtitles) (maker) 87.7% ▁▁▁▁▁·▁▃▁▁▁● beats 16/16 medium · avg 71% 0.5% small run
Claw-Eval average score (maker) 72.4% ▄·▄··█▄▄▄▄▄● beats 9/9 medium · avg 48% 0.5% blunt · at its level
RefSpatial-Bench (maker) 70.0% ▃·▃····▃▃▅▅● beats 9/10 medium · avg 41% 0.5% at its level · small run
MLVU (maker) 86.6% ▂···▂···▂▄▄● beats 8/10 easy · avg 70% 0.5% small run
EmbSpatial-Bench (maker) 84.6% ▁·····▁·▁·▃● beats 10/10 easy · avg 68% 0.5% small run
WMT25 Terminology Translation (te… 89.2% ▂·▂··▂·▄▂·●▆ beats 6/12 medium · avg 63% 0.4% noisy
MVBench (maker) 75.5% ▁··▃▄▁▃▄▄▄●▄ beats 23/26 medium · avg 65% 0.2% blunt · at its level · small run
WMT26 General MT (GEMBA-ESA, LLM … 61.9% ●·▄▄▄·▄██▄█▄ beats 0/12 medium · avg 65% 0.2% blunt
source results share of the score
Qwen/Qwen3.6-27B model card (submitted … 42 44% ▇▇▇▇▇
Epoch AI 5 21% ▇▇▇
Qwen/Qwen3.8-27B model card (submitted … 14 18% ▇▇
meta-models/Muse-Glimmer-30B model card 10 10% ▇
Roblox LLM Leaderboard 2 4% ▇
One Success Isn't Reliability: Thinking… 1 1% ▇
SpeakLeash CPTUB 1 1% ▇
EEGAgentBench: Benchmarking LLM Agents … 1 1% ▇
Cohere 2 1% ▇
How Far Is Document Parsing from Solved… 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 68.3
Licences: Qwen/Qwen3.6-27B model card ([apache-2.0](../../licenses/Qwen--Qwen3.6-27B.txt)) · Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Qwen/Qwen3.8-27B model card ([apache-2.0](../../licenses/Qwen--Qwen3.8-27B.txt)) · meta-models/Muse-Glimmer-30B model card ([Apache-2.0](../../licenses/speed-meta-muse-glimmer-30b-Apache-2.0.txt)) · Roblox LLM Leaderboard ([MIT](../../licenses/roblox-llm-leaderboard-MIT.txt)) · One Success Isn't Reliability: Thinkingbox ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · EEGAgentBench: Benchmarking LLM Agents on Short- and Long-Horizon EEG Analysis ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Cohere ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · How Far Is Document Parsing from Solved? PureDocBench: A Source-Traceable Benchmark across Clean ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))
## How to run it
### Through an API
api (Groq) · $1.20/M blended · $0.6 in / $3 out per 1M tokens · [console.groq.com](https://console.groq.com/docs/models)
[price source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/providers/groq/models/qwen/qwen3.6-27b.toml): Standard base token rate from models.dev. Provider-specific tiers, long-context surcharges, caching and promotions may differ. · [context source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/models/alibaba/qwen3.6-27b.toml)
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run Qwen3.6 27B (Q4_K_M, needs ~19.1 GB, from [huggingface.co/ggml-org](https://huggingface.co/ggml-org/Qwen3.6-27B-GGUF/tree/8a7ee08e8b9bfb857107ecc25a5599d2f38b76f8))
```sh
llama cli -hf ggml-org/Qwen3.6-27B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf ggml-org/Qwen3.6-27B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~28.6 GB) `llama cli -hf ggml-org/Qwen3.6-27B-GGUF:Q8_0`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run Qwen3.6 27B (Q4_K_M, needs ~19.1 GB, from [huggingface.co/ggml-org](https://huggingface.co/ggml-org/Qwen3.6-27B-GGUF/tree/8a7ee08e8b9bfb857107ecc25a5599d2f38b76f8))
```sh
lms get https://huggingface.co/ggml-org/Qwen3.6-27B-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~28.6 GB) `lms get https://huggingface.co/ggml-org/Qwen3.6-27B-GGUF@Q8_0 -y` · 4bit (~16.1 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Qwen3.6-27B-MLX-4bit -y` · 8bit (~29.5 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Qwen3.6-27B-MLX-8bit -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run Qwen3.6 27B (Q4_K_M, needs ~17.18 GB, from [ollama.com](https://ollama.com/library/qwen3.6/tags))
```sh
ollama run qwen3.6:27b
```
Other versions: Q8_0 (~33.86 GB) `ollama run qwen3.6:27b-q8_0` · nvfp4 (~17.18 GB, Mac only) `ollama run qwen3.6:27b-mlx`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run Qwen3.6 27B (4bit, needs ~16.1 GB, Mac only, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Qwen3.6-27B-MLX-4bit/tree/bd83f6fe15b171f1549475db2348389c0f541c21))
```sh
mlx_lm.chat --model lmstudio-community/Qwen3.6-27B-MLX-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model lmstudio-community/Qwen3.6-27B-MLX-4bit
```
Other versions: 8bit (~29.5 GB, Mac only) `mlx_lm.chat --model lmstudio-community/Qwen3.6-27B-MLX-8bit`
Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen3.6-27B)
## Published speed measurements
Reference results for the hardware, engine and settings below. These are not verified speeds of the downloads above or predictions for your machine.
hardware precision speed engine and settings
Apple M5 Pro (48GB; CPU/GPU… Q4 GGUF (4-bit;… 15.4 tok/s llama.cpp b9960, Metal backend, d…
Apple M5 Pro (48GB; CPU/GPU… MLX 4-bit (grou… 18.4 tok/s mlx-lm 0.31.3 (MLX 0.32.0), defau…
Apple M1 Max, 32 GB q4_k_m 9.74 tok/s ollama (version not recorded) · l…
Apple M5 Max (40-core GPU),… mlx-nvfp4 28.62 tok/s ollama (version not recorded) · l…
Apple M5 Pro, 64 GB mlx-nvfp4 16.18 tok/s ollama (version not recorded) · l…
Apple M5 Pro, 64 GB mlx-nvfp4 16.07 tok/s ollama (version not recorded) · l…
Apple M5 Pro, 64 GB mlx-nvfp4 15.85 tok/s ollama (version not recorded) · l…
Each speed links to its source, which has the full settings.
[interactive version](https://whatmodel.sh/model/qwen3-6-27b) · [JSON](https://whatmodel.sh/api?format=json&all=1)