[whatmodel.sh](../../README.md) / Qwen3.5 35B-A3B # Qwen3.5 35B-A3B Alibaba · released 2026-02 · 36B params (3B active) · apache-2.0 **score 65.7** ±0.4 · #134 of 732 · from 136 benchmark results across 19 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Qwen3.5 35B-A3B among every model on that task or test, worst to best. 
task                vs the others         rank  counts  tests
agent tasks         ▁▁▃▆█▄▅●▄▄▂▁   #105 of 313     22%     18
coding              ▁▁▁▃▅▇█●▅▄▃▁   #149 of 469     19%      9
chat & writing      ▁▁▁▃▆▄▆█▆●▇▃   #123 of 402    2.8%      3
math                ▁▁▃▅█▆▄●▄▂▁▁   #139 of 431     18%     14
vision & documents  ▁▁▂▂▁▃▄▂▆●█▄    #56 of 153     10%     19
web search          ▁▁▁●▄█▃▄·▁·▁     #69 of 84    3.5%      5
agent tasks · #105 of 313 · 22% of its score Beats [Grok 4.20 (0309 reasoning)](grok-4-20.md), [o3-pro](o3-pro.md), [Grok 4.3](grok-4-3.md) here, though they rank higher overall. Trails [Nova 2 Pro (Preview)](nova-2-pro.md), [Mistral Medium 3.5 (128B)](mistral-medium-3-5.md) here, though they rank lower overall. 
test                                result  vs the others in its run    difficulty            counts  why
SWE-bench Verified                   58.6%  ▄▄·····▄●▄▄█ beats 3/7      hard · avg 16%          6.4%  sharp · at its level · several runs
Terminal-Bench 2.1                   26.4%  ▄······●▄▄█▄ beats 1/7      very hard · avg 7.2%    1.9%  sharp · at its level
SWE-bench Multilingual               47.7%  ▄·▄·····●▄█▄ beats 2/7      hard · avg 18%          1.9%  at its level · several runs
SWE-bench Pro (split unspecified)    38.0%  ▄···▄···▄▄●█ beats 4/7      very hard · avg 6.2%    1.7%  sharp · at its level
τ²-bench aggregate (maker)           81.2%  ▁▁▁·▁▁▃▃▃●█▁ beats 25/39    medium · avg 41%        1.6%  several runs
Terminal-Bench 2.0 (maker)           40.5%  ▂▂▅▅█●██▆▂▃▃ beats 13/36    very hard · avg 11%     1.0%  at its level · small run
WideSearch (maker)                   57.1%  ●·▂▆··▂···▂▂ beats 1/10     hard · avg 33%          0.9%  at its level · small run · several runs
OSWorld-Verified (maker)             54.5%  █▃·●▅▂▃▂▅▃▆▆ beats 7/27     hard · avg 20%          0.9%  at its level · small run
BrowseComp-ZH (maker)                69.5%  ▁▁▁▁▁▃·▁▄▅▄● beats 20/23    hard · avg 25%          0.9%  at its level · small run
MCP-Atlas (public set) (maker)       62.4%  ▂·▁▂▂·▁▃●▆▆▂ beats 13/39    hard · avg 30%          0.7%  at its level · small run
BrowseComp (maker)                   61.0%  ▂·▃▁▂▂▆▅●▂▂▄ beats 26/46    hard · avg 27%          0.7%  at its level · small run
Humanity's Last Exam with tools (…   47.4%  ▁·▁▂▂▁▄▄●█▄▅ beats 28/62    hard · avg 18%          0.6%  at its level · small run
Toolathlon (Tool Decathlon) (make…   28.7%  ▂▂▄●▄▄▆▂█▆██ beats 4/27     very hard · avg 11%     0.6%  at its level · small run
MCPMark (maker)                      27.0%  ▃·····█··●█▃ beats 4/8      very hard · avg 11%     0.6%  at its level · small run
EEGAgentBench overall (tool-based…   45.9%  ▂·▁▁··▇●█▂·▁ beats 11/25    hard · avg 28%          0.5%  blunt · at its level
BFCL V4 (December 2025 archive) (…   67.3%  ▁▁▁▂▆▆▅▆█▂●▃ beats 102/114  medium · avg 41%        0.5%  at its level · small run
τ³-bench (maker)                     68.9%  ▁▁▂▁·▁▁·▁▁▂● beats 17/25    medium · avg 40%        0.4%  at its level · small run
SWE-bench Pro public set, Qwen-re…   44.6%  ▂·····▂·●█▃▅ beats 2/12     hard · avg 16%          0.4%  at its level
coding · #149 of 469 · 19% of its score Beats [Qwen3-Coder-Next (80B-A3B)](qwen3-coder-next.md), [Gemini 2.5 Pro](gemini-2-5-pro.md), [Gemini 3.1 Flash-Lite](gemini-3-1-flash-lite.md) here, though they rank higher overall. Trails [Phi-4-reasoning 14B](phi-4-reasoning.md), [Llama 3.1 Nemotron Ultra 253B v1](llama-3-1-nemotron-ultra-253b.md), [Devstral Small 2 24B](devstral-small-2.md) here, though they rank lower overall. 
test                                result  vs the others in its run   difficulty            counts  why
SWE-bench Verified                   58.6%  ▄▄·····▄●▄▄█ beats 3/7     hard · avg 16%          6.4%  sharp · at its level · several runs
Arena WebDev (Code Arena)           1254.2  ▁▁●▂▅█▃▆▅▅▁▂ beats 13/129  avg 981                 3.1%  sharp
LiveCodeBench v6 (Feb–May 2025) (…   74.6%  ▁▁▃▁█▅▄▇●▅▃█ beats 24/38   medium · avg 44%        2.3%  several runs
SWE-bench Multilingual               47.7%  ▄·▄·····●▄█▄ beats 2/7     hard · avg 18%          1.9%  at its level · several runs
SWE-bench Pro (split unspecified)    38.0%  ▄···▄···▄▄●█ beats 4/7     very hard · avg 6.2%    1.7%  sharp · at its level
LiveCodeBench Pro 2025Q2 (Medium)    17.8%  █··▄▂▂██▄·●▆ beats 19/22   very hard · avg 3.5%    1.4%  sharp
SciCode (level unspecified)          27.7%  ▄···▄●█▄·▄·▄ beats 2/7     very hard · avg 13%     0.8%  at its level
LiveCodeBench Pro 2025Q2 (Easy)      81.1%  ▂··▃▂▃▆▅█·●▂ beats 18/20   medium · avg 47%        0.7%  typical
SWE-bench Pro public set, Qwen-re…   44.6%  ▂·····▂·●█▃▅ beats 2/12    hard · avg 16%          0.4%  at its level
chat & writing · #123 of 402 · 2.8% of its score Beats [LongCat 2.0 (1.6T-A48B)](longcat-2-0.md), [Step 3.5 Flash (196B-A11B)](step-3-5-flash.md), [GPT-5 mini](gpt-5-mini.md) here, though they rank higher overall. Trails [LFM2.5 2.6B](lfm2-5-2-6b.md), [Nemotron-Cascade-8B](nemotron-cascade-8b.md), [DeepSeek V3 0324](deepseek-v3-0324.md) here, though they rank lower overall. 
test                                result  vs the others in its run    difficulty      counts  why
LMArena (text)                      1393.8  ▁▁▁▂▃▃▃▆▆●█▃ beats 238/385  avg 1260          1.8%  blunt
Arena-Hard v2 aggregate              65.4%  ▁▁··▄▃▁▃●▁▁▇ beats 13/22    hard · avg 30%    0.6%  sharp · at its level · small run
IFEval (aggregation unspecified) …   91.9%  ▁▂▄▂▂▄▆██●▂▁ beats 123/145  easy · avg 81%    0.4%  maxed out · small run
math · #139 of 431 · 18% of its score Beats [Claude Sonnet 4.6](claude-sonnet-4-6.md), [MiniMax-M3](minimax-m3.md), [MiMo-V2.5 (310B-A15B)](mimo-v2-5.md) here, though they rank higher overall. Trails [Olmo 3 32B Think](olmo-3-32b-think.md), [Nemotron-Cascade-8B](nemotron-cascade-8b.md), [Olmo 3.1 32B Think](olmo-3-1-32b-think.md) here, though they rank lower overall. 
test                                result  vs the others in its run    difficulty            counts  why
AIME 2026                            91.1%  ▁▁▁▂▂▂▆●▆▂·▁ beats 26/52    medium · avg 58%        2.5%  sharp · small run · several runs
IMO-AnswerBench                      74.8%  ▁▁·▁▁▁▄●█▄▂▃ beats 18/44    medium · avg 37%        2.2%  small run · several runs
MathArena ArXivMath February 2026…   30.5%  ▁▁▁▁●▇▄▃▁▃·▃ beats 4/24     very hard · avg 9.4%    1.9%  at its level
HMMT February 2026 (maker)           78.7%  ▃▃▂▅·██●█▅▃▅ beats 19/35    medium · avg 35%        1.6%  sharp · small run
MathArena ArXivMath January 2026 …   50.0%  ▂··▄▂●█▆▆▄▄█ beats 5/23     hard · avg 23%          1.5%  at its level
MATH-Vision (maker)                  83.9%  ▁▁·▁··▄▁▃▇●█ beats 18/24    medium · avg 55%        1.4%  small run · several runs
OTIS mock AIME 2024–2025             54.4%  ▃▃▄▃▃▃●▇█▃▂▅ beats 81/197   very hard · avg 7.2%    1.1%  sharp · at its level · noisy
HMMT February 2025 (maker)           89.0%  ▁▁▁▃▁▅▅▃●█▁▄ beats 16/31    medium · avg 63%        1.0%  small run
AIME 2025                            91.9%  ▁·▁▁▁▂▅▆█▇●▁ beats 120/133  medium · avg 64%        1.0%  sharp · maxed out
MathArena ArXivMath December 2025…   39.7%  ▃·▃▃●▁·▄·▁▁▁ beats 7/18     hard · avg 19%          1.0%  at its level
MathVista (testmini) (maker)         86.2%  ▁··▁▄▄▆▇▄▅●▄ beats 43/48    medium · avg 64%        0.7%  small run
HMMT November 2025 (maker)           89.2%  ▁▁····▁▅▃●▇▁ beats 12/23    easy · avg 69%          0.6%  small run
PolyMATH (maker)                     64.4%  ▁···▃▁·▇█▄●▄ beats 19/23    medium · avg 35%        0.6%  at its level · small run
DynaMath (maker)                     85.0%  ▂·····▂··█●▆ beats 6/10     easy · avg 66%          0.5%  small run
vision & documents · #56 of 153 · 10% of its score Beats [GPT-6 Luna](gpt-6-luna.md), [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md), [GPT-5](gpt-5.md) here, though they rank higher overall. Trails [Qwen3.6 Flash](qwen3-6-flash.md) here, though they rank lower overall. 
test                                result  vs the others in its run   difficulty           counts  why
MATH-Vision (maker)                  83.9%  ▁▁·▁··▄▁▃▇●█ beats 18/24   medium · avg 55%       1.4%  small run · several runs
OmniDocBench 1.5 overall (higher …   89.3%  ▂··▂▂▂··▂▂●▅ beats 7/13    easy · avg 71%         1.0%  small run · several runs
ChartArena chart parsing (English…   54.1%  ▁·▁·▃▃▁▇▃█·● beats 20/22   hard · avg 27%         1.0%  blunt · at its level
CharXiv (reasoning questions) (ma…   77.5%  ▂▅▃▅▅▆▆▅●█▆▂ beats 70/101  medium · avg 43%       0.8%  small run
MathVista (testmini) (maker)         86.2%  ▁··▁▄▄▆▇▄▅●▄ beats 43/48   medium · avg 64%       0.7%  small run
MMMU (val) (maker)                   81.4%  ▁····▁▁▃█▆▆● beats 48/53   medium · avg 58%       0.7%  small run
MMMU Pro (maker)                     75.1%  ▂▃▁▃▆▇▃▇▇●█▅ beats 39/52   medium · avg 47%       0.6%  at its level · small run
MMStar (maker)                       81.9%  ▃▅▂▃▂▂▆██▆█● beats 33/35   medium · avg 63%       0.5%  at its level · small run
HallusionBench (maker)               67.9%  ▂▁·▂▂▂▃▅▃█●▅ beats 29/33   medium · avg 54%       0.4%  blunt · at its level · small run · seve…
CountBench (maker)                   97.8%  ▂▂▃▂·▂▂▃▃·▅● beats 17/18   very easy · avg 92%    0.4%  maxed out · small run
PMC-VQA (maker)                      62.0%  ▃···▃··▃▃·●█ beats 4/7     medium · avg 39%       0.4%  at its level · small run
SimpleVQA (maker)                    58.3%  ▃·▃·▃·▃·●▅▃▃ beats 6/10    hard · avg 35%         0.4%  blunt · at its level · small run
MMBench-EN dev v1.1 (maker)          91.5%  █▄···▂··▄●▄▆ beats 9/14    easy · avg 82%         0.3%  blunt · small run
ScreenSpot-Pro (maker)               68.6%  ▂▃▅▆█●▅▂···▂ beats 17/24   medium · avg 48%       0.3%  blunt · at its level · small run
AI2D test (maker)                    92.6%  ▁▁▅▇▃▇·▄·●·▃ beats 21/28   easy · avg 86%         0.2%  blunt · small run · several runs
RealWorldQA (maker)                  84.1%  ▁▃▁▇▃▇▇▅█▁●▅ beats 35/39   easy · avg 72%         0.2%  blunt · small run
CC-OCR (maker)                       80.7%  ▁·▂··▂▂▃▃▂●█ beats 19/26   easy · avg 71%         0.2%  blunt · small run
OCRBench (maker)                     91.0%  ▁·▃▁▆▅▂█▅█●▃ beats 34/37   easy · avg 83%         0.1%  blunt · small run
PureDocBench document parsing (Av…   65.7%  ▂·▁·▂●▅█·▁▂▂ beats 6/22    medium · avg 62%       0.0%  blunt · at its level
web search · #69 of 84 · 3.5% of its score Beats [DeepSeek V4 Flash (Preview, Apr 2026)](deepseek-v4-flash-preview.md), [Nemotron 3 Ultra 550B-A55B](nemotron-3-ultra-550b-a55b.md), [GPT-5.4 nano](gpt-5-4-nano.md) here, though they rank higher overall. Trails [GPT-4.1 nano](gpt-4-1-nano-2025-04-14.md), [GPT-4o mini (2024-07-18)](gpt-4o-mini-2024-07-18.md), [Mistral Small 3.1 24B (2503)](mistral-small-3-1.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty      counts  why
WideSearch (maker)                   57.1%  ●·▂▆··▂···▂▂ beats 1/10   hard · avg 33%    0.9%  at its level · small run · several runs
BrowseComp-ZH (maker)                69.5%  ▁▁▁▁▁▃·▁▄▅▄● beats 20/23  hard · avg 25%    0.9%  at its level · small run
BrowseComp (maker)                   61.0%  ▂·▃▁▂▂▆▅●▂▂▄ beats 26/46  hard · avg 27%    0.7%  at its level · small run
Humanity's Last Exam with tools (…   47.4%  ▁·▁▂▂▁▄▄●█▄▅ beats 28/62  hard · avg 18%    0.6%  at its level · small run
SealQA Seal-0 (maker)                41.4%  ●····█·····█ beats 0/2    hard · avg 20%    0.4%  at its level · small run
other tests (53) 
test                                result  vs the others in its run    difficulty            counts  why
GPQA Diamond                         83.5%  ▁▁▁▁▂▄▆▄▅●█▅ beats 140/211  hard · avg 43%          7.4%  sharp · several runs
Humanity's Last Exam (maker)         22.4%  ▁▁▁▄▆▆▇●█▆▁▃ beats 56/111   very hard · avg 5.1%    3.0%  several runs
MMLU-Pro (maker)                     85.3%  ▁▁·▁▁▁▃▂▄▅●▁ beats 174/188  medium · avg 57%        3.0%  several runs
KINA (Knowledge Index of Noah's A…   35.4%  ▁▁▂▁▃▄█▆●▅▂▃ beats 27/39    very hard · avg 21%     1.2%  at its level
MedXPertQA MM (maker)                61.4%  ▂···▂·▃··▆▂● beats 10/13    very hard · avg 31%     1.2%  sharp · at its level · small run
VitaBench (maker)                    31.9%  ▄··▂▄▆▆█●▄▄▂ beats 15/21    very hard · avg 12%     1.2%  at its level · small run · several runs
Polish medical board exams, bias-…   72.2%  ▄·▂▂▂··█▂▄·● beats 12/13    hard · avg 46%          1.1%  at its level
LongBench v2 (maker)                 59.0%  ▂·▁·▂▃▁▄▄▃●▄ beats 22/33    very hard · avg 35%     1.0%  at its level · small run
AgentWorldBench (open-ended, over…   47.7%  ▄●··▄▄·▄█▄██ beats 1/11     hard · avg 26%          0.9%  blunt · at its level
LVBench (maker)                      71.4%  ▇·█▅▅●▄··▃▁▁ beats 21/28    medium · avg 53%        0.9%  at its level · small run · several runs
C-Eval (maker)                       90.2%  ▁···▁▁▂▁▂▂▃● beats 23/33    medium · avg 71%        0.8%  small run
INCLUDE (maker)                      79.7%  ▂····▂▂▄▄▄●▄ beats 27/36    medium · avg 55%        0.8%  at its level · small run
MMLU-Redux (maker)                   93.3%  ▁▁▃▁▂▃▄▅▇▅●▄ beats 55/63    easy · avg 78%          0.8%  small run
DeepPlanning (maker)                 22.8%  ▂·····▂··▄●▄ beats 5/9      very hard · avg 9.2%    0.8%  small run · several runs
ZEROBench subquestions (maker)       34.1%  ▄·▄···▄·▄●█▄ beats 4/8      hard · avg 16%          0.8%  at its level · small run · several runs
MMMLU (maker)                        85.2%  ▂▁▁▂▃▂▄▄▅█●▃ beats 36/46    medium · avg 63%        0.7%  small run
CPTUB average (SpeakLeash, 0-5 ju…   84.5%  ▁▂▂▂▃█▃▆▅▄●▃ beats 89/95    medium · avg 65%        0.7%  typical
Chess Puzzles (Epoch)                 1.0%  ●▁▁▁▁▂▃▃▃▂▁▁ beats 44/137   very hard · avg 5.8%    0.7%  sharp · noisy
NL2Repo (maker)                      20.5%  ▂▂●·▃▃▅█▃▂▅▃ beats 2/22     very hard · avg 7.0%    0.7%  typical
OJBench (maker)                      36.0%  ▁···▁▃·▅▄▅█● beats 21/26    very hard · avg 13%     0.6%  at its level · small run
Video-MME (without subtitles) (ma…   82.5%  ▁▃▁▃▁·█▄▄▅·● beats 24/27    medium · avg 68%        0.6%  blunt · small run · several runs
QwenClawBench (Qwen internal) (ma…   47.7%  ▄·▄····●···█ beats 2/4      hard · avg 23%          0.6%  at its level
IFBench (aggregation unspecified)…   70.2%  ▂▆▂▄▄▄▂▄▄●█▇ beats 56/86    medium · avg 43%        0.6%  at its level · small run
Global PIQA (maker)                  86.6%  ▃▃··▃··▃▃▅●▅ beats 8/11     medium · avg 73%        0.6%  at its level · small run
MAXIFE (maker)                       86.6%  ▂·▂·▂···▆·▄● beats 8/11     easy · avg 67%          0.5%  small run
MultiChallenge (maker)               60.0%  ▁·▁·▁▃▆▅▃▂●▅ beats 20/30    hard · avg 31%          0.5%  at its level · small run
SuperGPQA (maker)                    63.4%  ▂▁▂▂▅▆▃▇▇▇●▃ beats 38/46    medium · avg 37%        0.5%  at its level · small run
MMLU-ProX (maker)                    81.0%  ▁·▁▁▁▂▁▃▃█●▄ beats 26/36    medium · avg 58%        0.5%  small run
VideoMMMU (maker)                    80.4%  ▂···▂···▂▂●▆ beats 5/10     medium · avg 60%        0.5%  small run
VLMs Are Blind (maker)               97.0%  ▂·▂·····▃··● beats 8/8      very easy · avg 90%     0.5%  maxed out · small run
LingoQA (maker)                      79.2%  ▄······●·█·█ beats 1/5      medium · avg 58%        0.5%  small run
SLAKE (maker)                        78.7%  ▄·····▄▄·●█▄ beats 3/7      medium · avg 60%        0.4%  small run
Video-MME (with subtitles) (maker)   86.6%  ▁▁▁▁▁·▁▃▁▁▁● beats 12/16    medium · avg 71%        0.4%  small run
Next Steps data preparation (bala…   73.9%  ▄·▄···●·▄▄·▄ beats 2/6      medium · avg 52%        0.4%  typical
MMVU (maker)                         72.3%  ▃···▃···▃▃●█ beats 5/8      medium · avg 49%        0.4%  at its level · small run
Claw-Eval Pass^3 (maker)             51.0%  █▄●·▄·▄▄·▄·▄ beats 3/8      hard · avg 25%          0.4%  at its level · small run
MLVU (maker)                         85.6%  ▂···▂···▂▄●█ beats 5/10     easy · avg 69%          0.4%  small run
RefSpatial-Bench (maker)             63.5%  ▃·▃····▃▃●▅█ beats 4/10     medium · avg 41%        0.4%  at its level · small run
FullStackBench (English) (maker)     58.1%  ●····█·····█ beats 0/2      hard · avg 33%          0.4%  at its level · small run
FullStackBench (Chinese) (maker)     55.0%  ●······█···█ beats 0/2      hard · avg 30%          0.4%  at its level · small run
ERQA (maker)                         64.8%  ▃··▃··█·▃█●▅ beats 9/12     medium · avg 37%        0.4%  at its level · small run
MMLongBench-Doc (maker)              59.5%  ▃···▃···▃▅●▅ beats 6/9      medium · avg 36%        0.4%  at its level · small run
EmbSpatial-Bench (maker)             83.1%  ▁·····▁·▁·●█ beats 4/10     easy · avg 67%          0.4%  small run
AndroidWorld (maker)                 71.1%  ▁·▁▁▂▃█▅▂●·▂ beats 22/24    medium · avg 42%        0.3%  at its level · small run
NOVA-63 (maker)                      57.1%  ▃▃·▃···▃█▃●█ beats 8/11     medium · avg 36%        0.3%  blunt · at its level · small run
CL-bench                             15.5%  █··········● beats 2/2      very hard · avg 7.2%    0.3%  blunt · small run
SkillsBench 78-task subset (OpenC…    4.4%  ●·····▄···▄█ beats 0/4      very hard · avg 7.4%    0.3%  typical
NTCIR SciClaimEval Subtask 1 pair…   61.1%  ▃▃···●·▃▃█·▃ beats 2/8      medium · avg 45%        0.3%  blunt · at its level
Claw-Eval average score (maker)      65.4%  ▄·▄··█▄●▄▄▄▄ beats 5/9      medium · avg 47%        0.3%  blunt · at its level · small run
MVBench (maker)                      74.8%  ▁··▃▄▁▃▄▄▄●▄ beats 21/26    medium · avg 65%        0.2%  blunt · at its level · small run
LIBRA Mini (Russian long context,…   43.7%  ▄···▄█·█●··▄ beats 6/8      medium · avg 38%        0.1%  blunt · at its level
WMT24++ (maker)                      76.3%  ▁·▁▁·▁·▁▂●█▃ beats 7/27     easy · avg 68%          0.1%  blunt · small run
Vectara hallucination leaderboard…   89.5%  ▁▂▁▂▅●▂▃▄▃▁▁ beats 40/98    very easy · avg 90%     0.0%  blunt
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
Qwen/Qwen3.5-122B-A10B model card (subm…       71   41% ▇▇▇▇▇
Qwen/Qwen3.6-35B-A3B model card (submit…       27   18% ▇▇
Qwen/Qwen3.5-27B model card (submitted …        7    9% ▇
Kwaipilot/KAT-Coder-V2.5-Dev model card         5    7% ▇
NVIDIA                                          7    6% ▇
LMArena leaderboard dataset                     2    5% ▇
MathArena ArXivMath / BrokenArXiv / ArX…        3    4% ▇
Epoch AI                                        3    4% ▇
Knowledge Index of Noah's Ark                   1    1% ▇
Reassessing High-Performing LLMs on Pol…        1    1% ▇
ChartArena: Benchmarking Chart Parsing …        1    1% ▇
Qwen/Qwen-AgentWorld-35B-A3B model card…        1    1% ▇
SpeakLeash CPTUB                                1    1% ▇
EEGAgentBench: Benchmarking LLM Agents …        1    1% ▇
Agentic coding without the cloud: evalu…        1    0% ▇
SciTrue: Reliable Scientific Claim Vali…        1    0% ▇
ai-forever LIBRA leaderboard                    1    0% ▇
How Far Is Document Parsing from Solved…        1    0% ▇
Vectara hallucination leaderboard               1    0% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 65.7
Licences: Qwen/Qwen3.5-122B-A10B model card ([apache-2.0](../../licenses/Qwen--Qwen3.5-122B-A10B.txt)) · Qwen/Qwen3.6-35B-A3B model card ([apache-2.0](../../licenses/Qwen--Qwen3.6-35B-A3B.txt)) · Qwen/Qwen3.5-27B model card ([apache-2.0](../../licenses/Qwen--Qwen3.5-27B.txt)) · Kwaipilot/KAT-Coder-V2.5-Dev model card ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · NVIDIA ([CC BY 4.0](../../licenses/report-2603.19220v2.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · MathArena ArXivMath / BrokenArXiv / ArXivLean model-output datasets ([CC BY-SA 4.0](../../licenses/matharena-sharealike-NOTICE.txt)) · Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Knowledge Index of Noah's Ark ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Reassessing High-Performing LLMs on Polish Medical Exams: True Competence or Bias-Driven Performance? ([CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)) · ChartArena: Benchmarking Chart Parsing across Languages ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Qwen/Qwen-AgentWorld-35B-A3B model card ([Apache-2.0](https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B/blob/60d2b0434a53d2e62a7c00a489586815d94ebffb/README.md)) · 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/)) · Agentic coding without the cloud: evaluating open-weight LLMs on longitudinal data preparation tasks ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · SciTrue: Reliable Scientific Claim Validation with Frontier and Open Language Models at the NTCIR SciClaimEval Task ([CC BY 4.0](../../licenses/paper-2609.00654v1-CC-BY-4.0.txt)) · ai-forever LIBRA leaderboard ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · 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/)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
Not counted (5) 
source                  test                            score  why
Qwen/Qwen3.5-35B-A3B …  MMLU-Pro                        85.3%  only model in this source's run
Qwen/Qwen3.5-35B-A3B …  Humanity's Last Exam            22.4%  only model in this source's run
Qwen/Qwen3.5-35B-A3B …  GPQA Diamond                    84.2%  only model in this source's run
Qwen/Qwen3.5-35B-A3B …  SWE-bench Verified              69.2%  only model in this source's run
Qwen/Qwen3.5-35B-A3B …  LiveCodeBench v6 (Feb–May 202…  74.6%  only model in this source's run
## How to run it ### Through an API api (Deep Infra) · $0.35/M blended · $0.14 in / $1 out per 1M tokens · [deepinfra.com](https://deepinfra.com/models) [price source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/providers/deepinfra/models/Qwen/Qwen3.5-35B-A3B.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/qwen3.5-35b-a3b.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 Qwen3.5 35B-A3B (Q4_K_M, needs ~21.2 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Qwen3.5-35B-A3B-GGUF/tree/529ae8d8705097e646043ff81181c6564b6a8449)) ```sh llama cli -hf lmstudio-community/Qwen3.5-35B-A3B-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf lmstudio-community/Qwen3.5-35B-A3B-GGUF:Q4_K_M ``` Other versions: Q8_0 (~36.9 GB) `llama cli -hf lmstudio-community/Qwen3.5-35B-A3B-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 Qwen3.5 35B-A3B (Q4_K_M, needs ~21.2 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Qwen3.5-35B-A3B-GGUF/tree/529ae8d8705097e646043ff81181c6564b6a8449)) ```sh lms get https://huggingface.co/lmstudio-community/Qwen3.5-35B-A3B-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 (~36.9 GB) `lms get https://huggingface.co/lmstudio-community/Qwen3.5-35B-A3B-GGUF@Q8_0 -y` · 4bit (~20.4 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen3.5-35B-A3B-4bit -y` · 8bit (~37.7 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen3.5-35B-A3B-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 Qwen3.5 35B-A3B (Q4_K_M, needs ~22.1 GB, from [ollama.com](https://ollama.com/library/qwen3.5/tags)) ```sh ollama run qwen3.5:35b-a3b ``` Other versions: Q8_0 (~43.7 GB) `ollama run qwen3.5:35b-a3b-q8_0` · nvfp4 (~22.1 GB, Mac only) `ollama run qwen3.5:35b-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 Qwen3.5 35B-A3B (4bit, needs ~20.4 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Qwen3.5-35B-A3B-4bit/tree/1e20fd8d42056f870933bf98ca6211024744f7ec)) ```sh mlx_lm.chat --model mlx-community/Qwen3.5-35B-A3B-4bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model mlx-community/Qwen3.5-35B-A3B-4bit ``` Other versions: 8bit (~37.7 GB, Mac only) `mlx_lm.chat --model mlx-community/Qwen3.5-35B-A3B-8bit` Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen3.5-35B-A3B) ## 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
AMD Ryzen AI Max+ 395 (Stri…  IQ4_XS            75.22 tok/s  llama.cpp 1fd5f4803 (build_number…
AMD Ryzen AI Max+ 395 (Stri…  Q4_K_M            64.85 tok/s  llama.cpp b8460, Vulkan (RADV) · …
AMD Ryzen AI Max+ 395 (Stri…  Q4_K_M               48 tok/s  Ollama 0.20.4, Ollama Vulkan RADV…
AMD Ryzen AI Max+ 395 (Stri…  Q4_K_M             47.5 tok/s  Ollama 0.20.4, Ollama Vulkan RADV…
AMD Ryzen AI Max+ 395 (Stri…  Q4_K_M             47.4 tok/s  Ollama 0.20.4, Ollama Vulkan RADV…
AMD Ryzen AI Max+ 395 (Stri…  Q4_K_M            52.06 tok/s  llama.cpp b8298, Vulkan (RADV) · …
AMD Ryzen AI Max+ 395 (Stri…  Q4_K_M            54.67 tok/s  llama.cpp b8460, ROCm (HIP) · lla…
AMD Ryzen AI Max+ 395 (Stri…  Q4_K_M            47.87 tok/s  llama.cpp b8301, ROCm (HIP) · lla…
AMD Ryzen AI Max+ 395 (Stri…  Q4_K_M             48.8 tok/s  llama.cpp b8301, ROCm (HIP) · lla…
Apple M5 Pro (48GB; CPU/GPU…  Q4 GGUF (4-bit;…   72.5 tok/s  llama.cpp b9960, Metal backend, d…
Apple M5 Pro (48GB; CPU/GPU…  MLX 4-bit (grou…  104.8 tok/s  mlx-lm 0.31.3 (MLX 0.32.0), defau…
Apple M5 Max (40-core GPU),…  mlx-nvfp4         14.51 tok/s  ollama (version not recorded) · l…
Each speed links to its source, which has the full settings. ## Quantization evidence How much of the full-precision result a quantized version kept, measured within one study. Rows from the same study share a table and aren't independent. They don't describe every download of that precision. 
precision                       test                                full → quantized    kept
FP8 W8A8 dynamic (E4M3; weigh…  LiveCodeBench v6 code generation …     0.754 → 0.764  101.3%
FP8 W8A8 dynamic (E4M3; weigh…  GPQA Diamond 0-shot (lighteval vi…      0.83 → 0.832  100.2%
FP8 W8A8 dynamic (E4M3; weigh…  GSM8K-Platinum 0-shot (lm-evaluat…      0.95 → 0.951  100.1%
FP8 W8A8 dynamic (E4M3; weigh…  MMLU-Pro 0-shot (lm-evaluation-ha…     0.857 → 0.857  100.0%
FP8 W8A8 dynamic (E4M3; weigh…  AIME 2025 0-shot (lighteval via l…     0.912 → 0.912  100.0%
FP8 W8A8 dynamic (E4M3; weigh…  IFEval prompt-level strict 0-shot…      0.91 → 0.904   99.4%
FP8 W8A8 dynamic (E4M3; weigh…  MATH-500 0-shot (lighteval via li…     0.849 → 0.843   99.3%
Each kept value links to its source. [interactive version](https://whatmodel.sh/model/qwen3-5-35b-a3b) · [JSON](https://whatmodel.sh/api?format=json&all=1)