OpenAIGPT-5.4 nanoVSZ.ai (Zhipu AI)GLM-4.6
Our Take
While these models specialize in different coding tasks: GPT-5.4 nano is suited for complex codebases, multi-file repositories, and architectural planning, while GLM-4.6 excels at multi-file code and clearly defined tasks, they share very similar reasoning capabilities. GPT-5.4 nano is the smarter buy here as it offers the same level of performance while being 2.2x cheaper than GLM-4.6.
▶WHY?
Benchmark Calculations & Evidence:
Coding Evaluation: GPT-5.4 nano was evaluated on SWE-bench Pro (scoring 52.4%), while GLM-4.6 was evaluated on SWE-bench Verified (scoring 68%).
Reasoning Accuracy: Both models were evaluated on the GPQA Diamond benchmark. GPT-5.4 nano scored 82.8%, while GLM-4.6 scored 82.9%.
Cost Ratio: GLM-4.6 blended CPM is 2.2x higher than GPT-5.4 nano.
Was this recommendation helpful?
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)
68%multi-file code and clearly defined tasks
Reasoning (gpqa-diamond)Winner (+0.1%)
82.9%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-4.6
GPT-5.4 nano is cheaper than GLM-4.6. GPT-5.4 nano has a blended cost of $0.46/1M tokens, which is about 2.2x cheaper than GLM-4.6 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-4.6 scores 68% on swe-bench-verified (multi-file code and clearly defined tasks).
Related Matchups
Explore similar comparisons for GPT-5.4 nano and GLM-4.6.
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.