Alibaba Cloud (Qwen)Qwen3.6-35B-A3BVSOpenAIGPT-5 mini
Our Take
We recommend Qwen3.6-35B-A3B over GPT-5 mini 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 mini 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 mini scored 81.6% (+4.4% gap).
Coding Performance: Both models were evaluated on the SWE-bench Pro benchmark. GPT-5 mini scored 45.7%, while Qwen3.6-35B-A3B scored 49.5% (+-3.8% gap).
Hosting Model: Qwen3.6-35B-A3B runs locally for $0 API costs, while GPT-5 mini is hosted via a cloud-hosted API setup.
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Benchmarks & Scores
Coding (swe-bench-pro)Winner (+3.8%)
49.5%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)Winner (+4.4%)
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)
45.7%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)
81.6%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
$0.69Input: $0.25 | Output: $2.00Context WindowLarger
400k tokensFrequently Asked Questions about Qwen3.6-35B-A3B vs GPT-5 mini
Qwen3.6-35B-A3B is better for coding tasks on this benchmark. It scores 49.5% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning) compared to GPT-5 mini which scores 45.7%.
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