Alibaba Cloud (Qwen)Qwen3.7-MaxVSOpenAIGPT-5.4
Our Take
We recommend Qwen3.7-Max for a 1.5x API cost saving at identical performance levels. While both models deliver similar intelligence, Qwen3.7-Max is the optimal choice for high-volume pipelines. Choose Qwen3.7-Max for budget efficiency without sacrificing quality.
▶WHY?
Benchmark Calculations & Evidence:
Performance Match: Both models perform almost identically, with an average score gap of just 1.6% across reasoning and coding benchmarks.
Coding Benchmarks: Both models were evaluated on the SWE-bench Pro benchmark. Qwen3.7-Max scored 60.6%, while GPT-5.4 scored 57.7%.
Reasoning Benchmarks: Both models were evaluated on the GPQA Diamond benchmark. Qwen3.7-Max scored 92.4%, while GPT-5.4 scored 92.8%.
Cost Efficiency: Qwen3.7-Max pricing ($2.5/M input, $7.5/M output) is 1.5x cheaper than GPT-5.4 ($2.5/M input, $15/M output).
Was this recommendation helpful?
Benchmarks & Scores
Coding (swe-bench-pro)Winner (+2.9%)
60.6%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)
92.4%graduate-level science QA
Cost & Context
Cost (per 1M tokens)1.5x cheaper
$3.75Input: $2.50 | Output: $7.50Context Window
1.05M tokensBenchmarks & Scores
Coding (swe-bench-pro)
57.7%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)Winner (+0.4%)
92.8%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
$5.63Input: $2.50 | Output: $15.00Context WindowLarger
1.05M tokensFrequently Asked Questions about Qwen3.7-Max vs GPT-5.4
Qwen3.7-Max is cheaper than GPT-5.4. Qwen3.7-Max has a blended cost of $3.75/1M tokens, which is about 1.5x cheaper than GPT-5.4 at $5.63/1M tokens.
Qwen3.7-Max is better for coding tasks on this benchmark. It scores 60.6% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning) compared to GPT-5.4 which scores 57.7%.
Related Matchups
Explore similar comparisons for Qwen3.7-Max and GPT-5.4.
Do you want to find a model for your constraints?
Use our interactive model finder to filter LLMs by reasoning capability, coding performance, cost, and context length.