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Qwen3.6 Plus

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Qwen3.6 Plus is a closed-weights language model from Alibaba, released April 2026.

70.0±0.8#83 of 719 LLMsfrom 14 benchmark results across 7 sources

$ how it compares

The LLMs ranked either side of Qwen3.6 Plus, out of 828 with a score, then more from Alibaba.

#modelscorewhereRAMreleased
87o3-pro OpenAI70±2api $35.00/M2025-06
88KAT-Coder-V2.5-Dev (35B-A3B) Kuaishou (Kwaipilot)70±2local~22GB2026-07
89Qwen3.6 Plus Alibaba70±1api $1.13/M2026-04
90Inkling Small (266B-A12B) Thinking Machines70±1api $0.64/M +local~86GB2026-07
91MiMo-V2-Pro Xiaomi70±1comparison only2026-03
more from Alibaba
18Qwen3.8 Max (2.4T-A95B) Alibaba78±1api $3.00/M +local~380GB2026-08
34Qwen3.8 Max (0902) Alibaba76±1api $3.00/M2026-09
37Qwen3.8-Flash-Next Alibaba75±1local~59GB2026-08

see all LLMs · find the ones that fit your machine

$ what it's good at

agent tasks▁▁▃▆█▆▅▅▅▄▃▁ #113 of 300 * · 18% of its score · 2 tests

Beats o3-pro here, though they rank higher overall. Trails SmolLM3 3B, Jamba 1.5 Large, Nova 2 Pro (Preview) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
SWE-bench Verified57.9% ✎▁··▁·▃▂▁▄█▄▁ beats 3/29hard · avg 19%▇▇▇▇▇ 13%sharp · at its level
Agents' Last Exam overall full-pass rate (fixed OpenClaw + GUI-as-Tool)8.6% ✎▄···█▄▄▄█▄·█ beats 1/10very hard · avg 1.7%▇▇ 4.3%typical
coding▁▁▁▃▆██▇▆▄▃▁ #64 of 458 * · 24% of its score · 2 tests

Beats DeepSeek V4 Flash Vision (Exp), GPT-5.2, GPT-5 here, though they rank higher overall. Trails MiMo-V2.5-Pro (1T-A42B) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
SWE-bench Verified57.9% ✎▁··▁·▃▂▁▄█▄▁ beats 3/29hard · avg 19%▇▇▇▇▇ 13%sharp · at its level
Arena WebDev (Code Arena)1460.7 ✎▁▂▄▃▅█▄▇▆▄▁▁ beats 61/119avg 981▇▇▇▇ 11%sharp
chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #58 of 393 * · 6.2% of its score · 1 test

Beats GPT-6 Luna, Grok 4.7, GPT-5.2 here, though they rank higher overall. Trails Gemma 4 31B, Gemini 2.5 Pro, Claude Sonnet 4.5 here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
LMArena (text)1443.4 ✎▁▁▁▂▃▃▃▆▆▇█▃ beats 306/379avg 1268▇▇ 6.2%blunt
math▁▂▃▅▆▅▄█▄▁▁▁ #66 of 398 · 34% of its score · 3 tests

Beats Claude Sonnet 4.6, Claude Opus 4.5, Qwen3.7 Plus here, though they rank higher overall. Trails INTELLECT-3, Qwen3-Coder-Next (80B-A3B), GPT-5 mini here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
FrontierMath tiers 1–3 v238.2% ✎▂··▁▁▃▄▄█▅▃▃ beats 28/82very hard · avg 1.2%▇▇▇▇▇▇▇▇▇▇▇▇ 29%sharp · at its level
OTIS mock AIME 2024–202593.3% ✎▃▃▄▃▃▃▇▇█▃▂▅ beats 155/193very hard · avg 8.5%▇ 2.9%sharp · noisy
MathConstraint full release, no tools (329 instances)47.4% ✎▄··▄▄·▄▄█▄·▄ beats 6/8very hard · avg 11%▇ 1.5%at its level
vision & documents▁▁▃▂▁▃▄▂▆▇█▄ #28 of 147 * · 10% of its score · 2 tests

Beats Claude Sonnet 5.5, MiMo-V2.6-Pro, GPT-5.6 Terra here, though they rank higher overall.

testresultvs the others in its rundifficultycountswhy
CharXiv (reasoning questions) lab81.5% ✎▄▂▄▆▄▄▆··▄█▂ beats 16/21medium · avg 39%▇▇▇ 8.2%typical
BEAR-Bench (bilingual RU/EN enterprise and academic document images, overall accuracy)65.0% ✎▃▃·▅·▃·█·█▅▅ beats 11/14hard · avg 24%▇ 2.2%at its level
other tests5 not in a task · 21% of its score
testresultvs the others in its rundifficultycountswhy
GPQA Diamond88.4% ✎▁▁▁▁▂▄▆▄▅▇█▅ beats 164/208hard · avg 40%▇▇▇ 7.6%sharp
Mystery Game Puzzles (Epoch)12.0% ✎█··▃▃▇▄█▂▃▁▁ beats 19/74very hard · avg 9.4%▇▇ 4.0%sharp · noisy
AgentWorldBench (open-ended, overall) lab50.8% ✎▄▄··▄▄·▄█▄██ beats 2/11hard · avg 25%▇ 3.5%blunt · at its level
Chess Puzzles (Epoch)17.0% ✎█▁▁▁▁▂▃▄▂▂▁▁ beats 72/135very hard · avg 5.9%▇ 3.4%sharp · noisy
SimpleQA Verified44.1% ✎▁▁▃▂▃▃▆▆█▃▂▅ beats 40/78very hard · avg 8.0%▇ 2.3%at its level

$ where to run it

Through an API

api · Together AI · $1.13/M · $0.5 in / $3 out per 1M tokens · 1M context · docs.together.ai ↗ · price sourceStandard base token rate from models.dev. Provider-specific tiers, long-context surcharges, caching and promotions may differ.

$ how the score was worked out · Epoch AI — internal evaluatio… 63%, LMArena leaderboard dataset 17%, Qwen (Alibaba) vendor-reporte… 8% and 4 more

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The 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's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
Epoch AI — internal evaluations7▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 63%CC BY 4.0
LMArena leaderboard dataset2▇▇▇▇▇ 17%CC BY 4.0
Qwen (Alibaba) vendor-reported benchmark scores for its own models (qwen.ai launch posts, Qwen2.5-VL and Qwen3-VL technical reports, Qwen3.8-Flash-Next card) · lab-reported1▇▇ 8%Factual vendor-reported score (not licensed content)
Agents' Last Exam — authors' own fixed-harness model comparison1▇ 4%CC BY 4.0
Qwen/Qwen-AgentWorld-35B-A3B model card · lab-reported1▇ 3%Apache-2.0
BEAR-Bench: A Bilingual Enterprise and Academic Reasoning Benchmark for Multimodal Models (arXiv:2608.17895v1)1▇ 2%CC BY 4.0
MathConstraint: Automated Generation of Verified Combinatorial Reasoning Instances for LLMs — authors' own model runs1▇ 1%CC BY 4.0
prior · pull toward the middle▇ 0%
weighted average= 70.0

$ improve this page

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Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json