Qwen3.5-27B
According to BenchLM.ai, Qwen3.5-27B ranks #45 out of 115 models on the provisional leaderboard with an overall score of 63/100. It also ranks #16 out of 23 on the verified leaderboard. While not a frontier model, it offers specific advantages depending on the use case.
Qwen3.5-27B 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 15 of 193 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 Instruction Following (#15), while its weakest is Agentic (#42). This performance profile makes it a well-rounded choice across a range of tasks.
Ranking Distribution
Category rank across 7 benchmark categories — sorted by best rank
Category Performance
Scores across all benchmark categories (0-100 scale)
Category Breakdown
Agentic
#42Coding
#34Reasoning
Knowledge
#22Math
Multilingual
#26Multimodal
#39Inst. Following
#15Chatbot Arena Performance
Benchmark Details
Only benchmark rows with an attached exact-source record are shown here. Source-unverified manual rows and generated rows are hidden from model pages.
Compare This Model
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Frequently Asked Questions
How does Qwen3.5-27B perform overall in AI benchmarks?
Qwen3.5-27B currently ranks #45 out of 115 models on BenchLM's provisional leaderboard with an overall score of 63. It also ranks #16 out of 23 on the verified leaderboard. It is created by Alibaba and features a 262K context window.
Is Qwen3.5-27B good for knowledge and understanding?
Qwen3.5-27B ranks #22 out of 115 models in knowledge and understanding benchmarks with an average score of 78.8. There are stronger options in this category.
Is Qwen3.5-27B good for coding and programming?
Qwen3.5-27B ranks #34 out of 115 models in coding and programming benchmarks with an average score of 71.5. There are stronger options in this category.
Is Qwen3.5-27B good for reasoning and logic?
Qwen3.5-27B has visible benchmark coverage in reasoning and logic, but BenchLM does not currently assign it a global category rank there.
Is Qwen3.5-27B good for agentic tool use and computer tasks?
Qwen3.5-27B ranks #42 out of 115 models in agentic tool use and computer tasks benchmarks with an average score of 53.4. There are stronger options in this category.
Is Qwen3.5-27B good for multimodal and grounded tasks?
Qwen3.5-27B ranks #39 out of 115 models in multimodal and grounded tasks benchmarks with an average score of 61.6. There are stronger options in this category.
Is Qwen3.5-27B good for instruction following?
Qwen3.5-27B ranks #15 out of 115 models in instruction following benchmarks with an average score of 89. There are stronger options in this category.
Is Qwen3.5-27B good for multilingual tasks?
Qwen3.5-27B ranks #26 out of 115 models in multilingual tasks benchmarks with an average score of 74.1. There are stronger options in this category.
Is Qwen3.5-27B open source?
Yes, Qwen3.5-27B is an open weight model created by Alibaba, meaning it can be downloaded and run locally or fine-tuned for specific use cases.
Does Qwen3.5-27B have full benchmark coverage on BenchLM?
Not yet. Qwen3.5-27B currently has 15 published benchmark scores out of the 193 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 Qwen3.5-27B?
Qwen3.5-27B has a context window of 262K, which determines how much text it can process in a single interaction.
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