Alibaba Cloud (Qwen)Qwen3.6-35B-A3BVSOpenAIGPT-5.4 nano
Our Take
We recommend Qwen3.6-35B-A3B over GPT-5.4 nano because it holds a clear reasoning lead. Choose Qwen3.6-35B-A3B for offline data control and complex codebases, multi-file repositories, and architectural planning, or GPT-5.4 nano only if you prefer using a cloud-hosted API setup for complex codebases, multi-file repositories, and architectural planning.
▶WHY?
Benchmark Calculations & Evidence:
Reasoning Accuracy: Both models were evaluated on the GPQA Diamond benchmark. Qwen3.6-35B-A3B scored 86%, while GPT-5.4 nano scored 82.8% (+3.2% gap).
Coding Performance: Both models were evaluated on the SWE-bench Pro benchmark. GPT-5.4 nano scored 52.4%, while Qwen3.6-35B-A3B scored 49.5% (+2.9% gap).
Hosting Model: Qwen3.6-35B-A3B runs locally for $0 API costs, while GPT-5.4 nano is hosted via a cloud-hosted API setup.
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Benchmarks & Scores
Coding (swe-bench-pro)
49.5%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)Winner (+3.2%)
86%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
Self HostLocal execution (zero API fees)Context Window
262.14k tokensBenchmarks & Scores
Coding (swe-bench-pro)Winner (+2.9%)
52.4%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)
82.8%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
$0.46Input: $0.20 | Output: $1.25Context WindowLarger
400k tokensFrequently Asked Questions about Qwen3.6-35B-A3B vs GPT-5.4 nano
GPT-5.4 nano is better for coding tasks on this benchmark. It scores 52.4% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning) compared to Qwen3.6-35B-A3B which scores 49.5%.
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