OpenAIGPT-5.4 nanoVSZ.ai (Zhipu AI)GLM-4.5
Our Take
We recommend GPT-5.4 nano for superior overall value and reasoning capabilities, as it is cheaper or equal in cost while delivering peak intelligence. While GPT-5.4 nano excels at complex complex codebases, multi-file repositories, and architectural planning, GLM-4.5 is suited for scripting multi-file code and clearly defined tasks. Choose GPT-5.4 nano for architectural codebase planning, or GLM-4.5 if you specifically require its simpler functions profile.
▶WHY?
Benchmark Calculations & Evidence:
Coding Evaluation: GPT-5.4 nano was evaluated on SWE-bench Pro (scoring 52.4%), while GLM-4.5 was evaluated on SWE-bench Verified (scoring 64.2%).
Reasoning Accuracy: Both models were evaluated on the GPQA Diamond benchmark. GPT-5.4 nano scored 82.8%, while GLM-4.5 scored 79.9%.
Cost Efficiency: GLM-4.5 pricing ($0.6/M input, $2.2/M output) is 0.5x cheaper than GPT-5.4 nano ($0.2/M input, $1.25/M output).
Was this recommendation helpful?
Benchmarks & Scores
Coding (swe-bench-pro)
52.4%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)Winner (+2.9%)
82.8%graduate-level science QA
Cost & Context
Cost (per 1M tokens)2.2x cheaper
$0.46Input: $0.20 | Output: $1.25Context WindowLarger
400k tokensFrequently Asked Questions about GPT-5.4 nano vs GLM-4.5
GPT-5.4 nano is cheaper than GLM-4.5. GPT-5.4 nano has a blended cost of $0.46/1M tokens, which is about 2.2x cheaper than GLM-4.5 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-4.5 scores 64.2% on swe-bench-verified (multi-file code and clearly defined tasks).
Related Matchups
Explore similar comparisons for GPT-5.4 nano and GLM-4.5.
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.