OpenAIGPT-5.4 nanoVSZ.ai (Zhipu AI)GLM-4.7
Our Take
We recommend GLM-4.7 for complex multi-file code and clearly defined tasks, or the 2.2x cheaper GPT-5.4 nano if your budget requires optimizing costs for very high-volume pipelines. While GLM-4.7 offers a clear reasoning advantage, it carries a moderate price premium. Choose GLM-4.7 for multi-file code and clearly defined tasks, or GPT-5.4 nano to save on API costs for simple scripts.
▶WHY?
Benchmark Calculations & Evidence:
Coding Evaluation: GPT-5.4 nano was evaluated on SWE-bench Pro (scoring 52.4%), while GLM-4.7 was evaluated on SWE-bench Verified (scoring 73.8%).
Reasoning Accuracy: Both models were evaluated on the GPQA Diamond benchmark. GLM-4.7 scored 85.7%, while GPT-5.4 nano scored 82.8%.
Cost Efficiency: GPT-5.4 nano pricing ($0.2/M input, $1.25/M output) is 2.2x cheaper than GLM-4.7 ($0.6/M input, $2.2/M output).
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Benchmarks & Scores
Coding (swe-bench-pro)
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)2.2x cheaper
$0.46Input: $0.20 | Output: $1.25Context WindowLarger
400k tokensBenchmarks & Scores
Coding (swe-bench-verified)
73.8%multi-file code and clearly defined tasks
Reasoning (gpqa-diamond)Winner (+2.9%)
85.7%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
$1.00Input: $0.60 | Output: $2.20Context Window
202.75k tokensFrequently Asked Questions about GPT-5.4 nano vs GLM-4.7
GPT-5.4 nano is cheaper than GLM-4.7. GPT-5.4 nano has a blended cost of $0.46/1M tokens, which is about 2.2x cheaper than GLM-4.7 at $1.00/1M tokens.
For coding tasks, GPT-5.4 nano scores 52.4% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning), while GLM-4.7 scores 73.8% on swe-bench-verified (multi-file code and clearly defined tasks).
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