Benchmark analysis of Qwen3.5-35B-A3B by Alibaba across 13 sourced tests on BenchLM.
According to BenchLM.ai, Qwen3.5-35B-A3B ranks #32 out of 97 models with an overall score of 67/100. While not a frontier model, it offers specific advantages depending on the use case.
Qwen3.5-35B-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 13 of 62 tracked benchmarks. BenchLM only exposes verified benchmark rows publicly, so missing categories stay blank until a sourced evaluation is available.
Its strongest category is Knowledge (#8), while its weakest is Agentic (#49). This performance profile makes it particularly effective for knowledge-intensive tasks like research, analysis, and factual Q&A.
Provider
AlibabaSource Type
Open WeightReasoning
ReasoningContext Window
262K
Model Status
Current
Release Date
Mar 4, 2026Overall Score
Pricing
$0.00 / $0.00
Input / output per 1M
Runtime
N/A
Latency unavailable
External frontier-model reference data from Artificial Analysis, updated 2026-03-31. BenchLM uses these signals as a bounded calibration input for coding, agentic, and final display ordering.
Intelligence Index
37.12
Coding Index
30.25
Agentic Index
44.11
Qwen3.5-35B-A3B ranks #32 out of 97 models with an overall score of 67. It is created by Alibaba and features a 262K context window.
Qwen3.5-35B-A3B ranks #8 out of 97 models in knowledge and understanding benchmarks with an average score of 79.3. It is among the top performers in this category.
Qwen3.5-35B-A3B ranks #17 out of 97 models in coding and programming benchmarks with an average score of 68.3. There are stronger options in this category.
Qwen3.5-35B-A3B has visible benchmark coverage in reasoning and logic, but BenchLM does not currently assign it a global category rank there.
Qwen3.5-35B-A3B ranks #49 out of 97 models in agentic tool use and computer tasks benchmarks with an average score of 53.8. There are stronger options in this category.
Qwen3.5-35B-A3B ranks #32 out of 97 models in multimodal and grounded tasks benchmarks with an average score of 75.1. There are stronger options in this category.
Qwen3.5-35B-A3B ranks #14 out of 97 models in instruction following benchmarks with an average score of 91.9. There are stronger options in this category.
Qwen3.5-35B-A3B ranks #34 out of 97 models in multilingual tasks benchmarks with an average score of 81. There are stronger options in this category.
Yes, Qwen3.5-35B-A3B is an open weight model created by Alibaba, meaning it can be downloaded and run locally or fine-tuned for specific use cases.
Not yet. Qwen3.5-35B-A3B currently has 13 verified benchmark scores out of the 62 benchmarks BenchLM tracks. BenchLM only exposes verified public benchmark rows, so missing categories stay blank until a sourced evaluation is available.
Qwen3.5-35B-A3B has a context window of 262K, which determines how much text it can process in a single interaction.
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