OpenAIGPT-5 miniVSZ.ai (Zhipu AI)GLM-4.7
Our Take
We recommend GLM-4.7 for complex multi-file code and clearly defined tasks, or the 1.5x cheaper GPT-5 mini 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 mini to save on API costs for simple scripts.
▶WHY?
Benchmark Calculations & Evidence:
Coding Evaluation: GPT-5 mini was evaluated on SWE-bench Pro (scoring 45.7%), 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 mini scored 81.6%.
Cost Efficiency: GPT-5 mini pricing ($0.25/M input, $2/M output) is 1.5x cheaper than GLM-4.7 ($0.6/M input, $2.2/M output).
Was this recommendation helpful?
Benchmarks & 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)1.5x cheaper
$0.69Input: $0.25 | Output: $2.00Context WindowLarger
400k tokensBenchmarks & Scores
Coding (swe-bench-verified)
73.8%multi-file code and clearly defined tasks
Reasoning (gpqa-diamond)Winner (+4.1%)
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 mini vs GLM-4.7
GPT-5 mini is cheaper than GLM-4.7. GPT-5 mini has a blended cost of $0.69/1M tokens, which is about 1.5x cheaper than GLM-4.7 at $1.00/1M tokens.
For coding tasks, GPT-5 mini scores 45.7% 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).
Related Matchups
Explore similar comparisons for GPT-5 mini and GLM-4.7.
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.