Model profile
Qwen3.6-35B-A3B
Evidence coverage
57 of 323 tracked benchmarks are published. 41 are verified and 16 provisional. 7 of 8 categories are measured.
- Published / tracked
- 57 / 323
- Verified
- 41
- Provisional
- 16
- Categories with evidence
- 7 / 8
Evidence by category
- Agentic15 benchmarksMixed evidence
- Coding8 benchmarksMixed evidence
- Reasoning2 benchmarksReported
- Knowledge11 benchmarksMixed evidence
- Math5 benchmarksVerified
- Multilingual0 benchmarksNot measured
- Multimodal15 benchmarksMixed evidence
- Inst. Following1 benchmarkReported
Qwen3.6-35B-A3B ranks #104 out of 200 models on the public leaderboard with an overall score of 51.47/100. It does not yet have enough sourced coverage for BenchLM's verified leaderboard. While not a frontier model, it offers specific advantages depending on the use case.
Qwen3.6-35B-A3B is a open weight model with a 262K token context window. It uses explicit chain-of-thought reasoning, which typically improves performance on math and complex reasoning tasks at the cost of higher latency and token usage.
This profile currently has 57 of 323 tracked benchmarks. BenchLM only exposes non-generated benchmark rows publicly, so missing categories stay blank until a sourced evaluation is available.
Its strongest category is Multimodal & Grounded (#21), while its weakest is Agentic (#75). This performance profile makes it particularly strong for screenshots, documents, charts, and grounded multimodal workflows.
Peer position
Exact provisional scores and ranks for the closest listed peers. A score can appear before a model clears the evidence threshold for a rank, so equal scores can have different rank states.
Range 51.11–51.71
- Llama 3.1 405BMetaCompare#10251.71Llama 3.1 405B is #102 with a score of 51.71.
- DeepSeek-R1DeepSeekCompare#10351.67DeepSeek-R1 is #103 with a score of 51.67.
- Qwen3.6-35B-A3BCurrent modelAlibaba#10451.47Qwen3.6-35B-A3B is #104 with a score of 51.47.
- Mercury 2InceptionCompare#10551.28Mercury 2 is #105 with a score of 51.28.
- GLM-4.7-FlashZ.AICompare#10651.25GLM-4.7-Flash is #106 with a score of 51.25.
- Grok 4.1 FastxAICompare#10751.25Grok 4.1 Fast is #107 with a score of 51.25.
- GPT-4.1OpenAICompare#10851.11GPT-4.1 is #108 with a score of 51.11.
Category percentile
More
Relative position among models eligible for each sourced category. A higher percentile means a stronger position within that category's ranked cohort; 100 is highest.
- Multimodal29%Eligible cohort rank #21 of 29Category score 52.3
- Knowledge4%Eligible cohort rank #50 of 52Category score 47.0
- Coding50%Eligible cohort rank #62 of 122Category score 50.1
- Agentic37%Eligible cohort rank #75 of 119Category score 45.2
Category evidence
Scores and ranks appear only where this model has published benchmark evidence. Categories without displayable source records remain not measured.
| Category | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticRank #75 of 119Percentile 37thWeight 22%15 benchmarksMixed sources | 45.2 | #75 of 119 | 37th | 22% | 15 benchmarks | Mixed sources |
| CodingRank #62 of 122Percentile 50thWeight 20%8 benchmarksMixed sources | 50.1 | #62 of 122 | 50th | 20% | 8 benchmarks | Mixed sources |
| ReasoningWeight 17%2 benchmarksReported | Score pending | Not ranked | Not available | 17% | 2 benchmarks | Reported |
| KnowledgeRank #50 of 52Percentile 4thWeight 12%11 benchmarksMixed sources | 47.0 | #50 of 52 | 4th | 12% | 11 benchmarks | Mixed sources |
| MathRank Not rankedWeight 5%5 benchmarksVerified | 71.7 | Not ranked | Not available | 5% | 5 benchmarks | Verified |
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | Not ranked | Not available | 7% | 0 benchmarks | Not measured |
| MultimodalRank #21 of 29Percentile 29thWeight 12%15 benchmarksMixed sources | 52.3 | #21 of 29 | 29th | 12% | 15 benchmarks | Mixed sources |
| Inst. FollowingWeight 5%1 benchmarkReported | Score pending | Not ranked | Not available | 5% | 1 benchmark | Reported |
Benchmark Details
Rows below have a displayable published verification record. Each source link and provenance note remains in the page HTML while its category is closed. Source-unverified manual rows and generated rows stay hidden.
Agentic15 benchmarks
τ³-Bench Tool-Agent-User Evaluation
Artificial Analysis Agentic Index
τ²-Bench Tool-Agent-User Evaluation
GDPval-AA normalized
Gert Labs Composite Game Benchmark
Coding8 benchmarks
LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
Software Engineering Benchmark Verified
Artificial Analysis Coding Index
Artificial Analysis SciCode
Reasoning2 benchmarks
Artificial Analysis Long Context Reasoning
Critical Physics Tasks
Knowledge11 benchmarks
Humanity's Last Exam
Massive Multitask Language Understanding Professional
SuperGPQA: Scaling LLM Evaluation Across 285 Graduate Disciplines
Graduate-Level Google-Proof Q&A
Artificial Analysis GPQA Diamond
Artificial Analysis Humanity's Last Exam
Artificial Analysis Omniscience Index
Artificial Analysis Omniscience Accuracy
Artificial Analysis Omniscience Hallucination Rate
Math5 benchmarks
Harvard-MIT Mathematics Tournament February 2026
AIME 2026
Harvard-MIT Mathematics Tournament February 2025
Harvard-MIT Mathematics Tournament November 2025
Multimodal15 benchmarks
Massive Multi-discipline Multimodal Understanding Pro
CharXiv Reasoning
Massive Multi-discipline Multimodal Understanding
AI2D test split
RefCOCO average
Video-MME with subtitle
Video-MME without subtitle
MLVU mean average
Artificial Analysis MMMU-Pro
Inst. Following1 benchmark
Artificial Analysis IFBench
Frequently Asked Questions
How does Qwen3.6-35B-A3B perform overall in AI benchmarks?
Qwen3.6-35B-A3B currently ranks #104 out of 200 models on BenchLM's provisional leaderboard with an overall score of 51.47. It is created by Alibaba. Its published context window is 262K.
Is Qwen3.6-35B-A3B good for knowledge and understanding?
Qwen3.6-35B-A3B ranks #50 out of 52 models in knowledge and understanding benchmarks with an average score of 47. There are stronger options in this category.
Is Qwen3.6-35B-A3B good for coding and programming?
Qwen3.6-35B-A3B ranks #62 out of 122 models in coding and programming benchmarks with an average score of 50.1. There are stronger options in this category.
Is Qwen3.6-35B-A3B good for mathematics?
Qwen3.6-35B-A3B has visible benchmark coverage in mathematics, but BenchLM does not currently assign it a global category rank there.
Is Qwen3.6-35B-A3B good for reasoning and logic?
Qwen3.6-35B-A3B has visible benchmark coverage in reasoning and logic, but BenchLM does not currently assign it a global category rank there.
Is Qwen3.6-35B-A3B good for agentic tool use and computer tasks?
Qwen3.6-35B-A3B ranks #75 out of 119 models in agentic tool use and computer tasks benchmarks with an average score of 45.2. There are stronger options in this category.
Is Qwen3.6-35B-A3B good for multimodal and grounded tasks?
Qwen3.6-35B-A3B ranks #21 out of 29 models in multimodal and grounded tasks benchmarks with an average score of 52.3. There are stronger options in this category.
Is Qwen3.6-35B-A3B good for instruction following?
Qwen3.6-35B-A3B has visible benchmark coverage in instruction following, but BenchLM does not currently assign it a global category rank there.
Is Qwen3.6-35B-A3B open source?
Yes, Qwen3.6-35B-A3B is an open weight model created by Alibaba, meaning it can be downloaded and run locally or fine-tuned for specific use cases.
Does Qwen3.6-35B-A3B have full benchmark coverage on BenchLM?
Not yet. Qwen3.6-35B-A3B currently has 57 published benchmark scores out of the 323 benchmarks BenchLM tracks. BenchLM only exposes non-generated public benchmark rows, so missing categories stay blank until a sourced evaluation is available.
What is the context window size of Qwen3.6-35B-A3B?
Qwen3.6-35B-A3B has a published context window of 262K, which determines how much text it can process in a single interaction.
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