Qwen3.5 397B (Reasoning)
According to BenchLM.ai, Qwen3.5 397B (Reasoning) ranks #23 out of 119 models on the provisional leaderboard with an overall score of 78/100. It does not yet have enough sourced coverage for BenchLM's verified leaderboard. This places it in the mid-tier of AI models, with strengths in specific benchmark categories.
Qwen3.5 397B (Reasoning) is a open weight model with a 128K 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.
Qwen3.5 397B (Reasoning) sits inside the Qwen3.5 397B family alongside Qwen3.5 397B. This profile currently has 18 of 225 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 Mathematics (#11), while its weakest is Multimodal & Grounded (#55). This performance profile makes it particularly strong for mathematical reasoning, scientific computing, and quantitative analysis.
Ranking Distribution
Category rank across 8 benchmark categories — sorted by best rank
Category Performance
Scores across all benchmark categories (0-100 scale)
Category Breakdown
Agentic
#22Coding
Reasoning
#15Knowledge
#22Math
#11Multilingual
#14Multimodal
#55Inst. Following
#33Chatbot 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.
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See how Qwen3.5 397B (Reasoning) stacks up against similar models
Frequently Asked Questions
How does Qwen3.5 397B (Reasoning) perform overall in AI benchmarks?
Qwen3.5 397B (Reasoning) currently ranks #23 out of 119 models on BenchLM's provisional leaderboard with an overall score of 78 (estimated). It is created by Alibaba and features a 128K context window.
Is Qwen3.5 397B (Reasoning) good for knowledge and understanding?
Qwen3.5 397B (Reasoning) ranks #22 out of 119 models in knowledge and understanding benchmarks with an average score of 78.3. There are stronger options in this category.
Is Qwen3.5 397B (Reasoning) good for coding and programming?
Qwen3.5 397B (Reasoning) has visible benchmark coverage in coding and programming, but BenchLM does not currently assign it a global category rank there.
Is Qwen3.5 397B (Reasoning) good for reasoning and logic?
Qwen3.5 397B (Reasoning) ranks #15 out of 119 models in reasoning and logic benchmarks with an average score of 81.4. There are stronger options in this category.
Is Qwen3.5 397B (Reasoning) good for agentic tool use and computer tasks?
Qwen3.5 397B (Reasoning) ranks #22 out of 119 models in agentic tool use and computer tasks benchmarks with an average score of 71. There are stronger options in this category.
Is Qwen3.5 397B (Reasoning) good for multimodal and grounded tasks?
Qwen3.5 397B (Reasoning) ranks #55 out of 119 models in multimodal and grounded tasks benchmarks with an average score of 57.9. There are stronger options in this category.
Is Qwen3.5 397B (Reasoning) good for instruction following?
Qwen3.5 397B (Reasoning) ranks #33 out of 119 models in instruction following benchmarks with an average score of 80.6. There are stronger options in this category.
Is Qwen3.5 397B (Reasoning) open source?
Yes, Qwen3.5 397B (Reasoning) is an open weight model created by Alibaba, meaning it can be downloaded and run locally or fine-tuned for specific use cases.
Which sibling models are related to Qwen3.5 397B (Reasoning)?
Qwen3.5 397B (Reasoning) belongs to the Qwen3.5 397B family. Related variants on BenchLM include Qwen3.5 397B.
Does Qwen3.5 397B (Reasoning) have full benchmark coverage on BenchLM?
Not yet. Qwen3.5 397B (Reasoning) currently has 18 published benchmark scores out of the 225 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 397B (Reasoning)?
Qwen3.5 397B (Reasoning) has a context window of 128K, which determines how much text it can process in a single interaction.
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