Model profile
Qwen3.6-27B
Evidence coverage
54 of 323 tracked benchmarks are published. 38 are verified and 16 provisional. 7 of 8 categories are measured.
- Published / tracked
- 54 / 323
- Verified
- 38
- Provisional
- 16
- Categories with evidence
- 7 / 8
Evidence by category
- Agentic10 benchmarksMixed evidence
- Coding8 benchmarksMixed evidence
- Reasoning2 benchmarksReported
- Knowledge12 benchmarksMixed evidence
- Math5 benchmarksVerified
- Multilingual0 benchmarksNot measured
- Multimodal16 benchmarksMixed evidence
- Inst. Following1 benchmarkReported
Qwen3.6-27B ranks #93 out of 200 models on the public leaderboard with an overall score of 53.82/100. 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.
Qwen3.6-27B 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 54 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 (#20), while its weakest is Agentic (#111). 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 53.43–54.06
- MiMo-V2-FlashXiaomiCompare#9154.06MiMo-V2-Flash is #91 with a score of 54.06.
- DeepSeek V4 Flash (High)DeepSeekCompare#9253.95DeepSeek V4 Flash (High) is #92 with a score of 53.95.
- Qwen3.6-27BCurrent modelAlibaba#9353.82Qwen3.6-27B is #93 with a score of 53.82.
- GPT-5.1OpenAICompare#9453.65GPT-5.1 is #94 with a score of 53.65.
- DeepSeek V3.1DeepSeekCompare#9553.64DeepSeek V3.1 is #95 with a score of 53.64.
- Claude Sonnet 4.5AnthropicCompare#9653.61Claude Sonnet 4.5 is #96 with a score of 53.61.
- DeepSeek V3.1 (Reasoning)DeepSeekCompare#9753.43DeepSeek V3.1 (Reasoning) is #97 with a score of 53.43.
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.
- Multimodal32%Eligible cohort rank #20 of 29Category score 54.2
- Knowledge6%Eligible cohort rank #49 of 52Category score 50.8
- Coding28%Eligible cohort rank #88 of 122Category score 46.6
- Agentic7%Eligible cohort rank #111 of 119Category score 32.5
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 #111 of 119Percentile 7thWeight 22%10 benchmarksMixed sources | 32.5 | #111 of 119 | 7th | 22% | 10 benchmarks | Mixed sources |
| CodingRank #88 of 122Percentile 28thWeight 20%8 benchmarksMixed sources | 46.6 | #88 of 122 | 28th | 20% | 8 benchmarks | Mixed sources |
| ReasoningWeight 17%2 benchmarksReported | Score pending | Not ranked | Not available | 17% | 2 benchmarks | Reported |
| KnowledgeRank #49 of 52Percentile 6thWeight 12%12 benchmarksMixed sources | 50.8 | #49 of 52 | 6th | 12% | 12 benchmarks | Mixed sources |
| MathRank Not rankedWeight 5%5 benchmarksVerified | 73.0 | 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 #20 of 29Percentile 32ndWeight 12%16 benchmarksMixed sources | 54.2 | #20 of 29 | 32nd | 12% | 16 benchmarks | Mixed sources |
| Inst. FollowingWeight 5%1 benchmarkReported | Score pending | Not ranked | Not available | 5% | 1 benchmark | Reported |
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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.
Agentic10 benchmarks
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
Knowledge12 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
Multimodal16 benchmarks
Massive Multi-discipline Multimodal Understanding Pro
CharXiv Reasoning
Massive Multi-discipline Multimodal Understanding
RefCOCO average
Video-MME with subtitle
MLVU mean average
Artificial Analysis MMMU-Pro
Inst. Following1 benchmark
Artificial Analysis IFBench
Frequently Asked Questions
How does Qwen3.6-27B perform overall in AI benchmarks?
Qwen3.6-27B currently ranks #93 out of 200 models on BenchLM's provisional leaderboard with an overall score of 53.82. It is created by Alibaba. Its published context window is 262K.
Is Qwen3.6-27B good for knowledge and understanding?
Qwen3.6-27B ranks #49 out of 52 models in knowledge and understanding benchmarks with an average score of 50.8. There are stronger options in this category.
Is Qwen3.6-27B good for coding and programming?
Qwen3.6-27B ranks #88 out of 122 models in coding and programming benchmarks with an average score of 46.6. There are stronger options in this category.
Is Qwen3.6-27B good for mathematics?
Qwen3.6-27B has visible benchmark coverage in mathematics, but BenchLM does not currently assign it a global category rank there.
Is Qwen3.6-27B good for reasoning and logic?
Qwen3.6-27B has visible benchmark coverage in reasoning and logic, but BenchLM does not currently assign it a global category rank there.
Is Qwen3.6-27B good for agentic tool use and computer tasks?
Qwen3.6-27B ranks #111 out of 119 models in agentic tool use and computer tasks benchmarks with an average score of 32.5. There are stronger options in this category.
Is Qwen3.6-27B good for multimodal and grounded tasks?
Qwen3.6-27B ranks #20 out of 29 models in multimodal and grounded tasks benchmarks with an average score of 54.2. There are stronger options in this category.
Is Qwen3.6-27B good for instruction following?
Qwen3.6-27B has visible benchmark coverage in instruction following, but BenchLM does not currently assign it a global category rank there.
Is Qwen3.6-27B open source?
Yes, Qwen3.6-27B is an open weight model created by Alibaba, meaning it can be downloaded and run locally or fine-tuned for specific use cases.
Does Qwen3.6-27B have full benchmark coverage on BenchLM?
Not yet. Qwen3.6-27B currently has 54 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 Qwen3.6-27B?
Qwen3.6-27B has a published context window of 262K, which determines how much text it can process in a single interaction.
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