xAIGrok 4.20VSZ.ai (Zhipu AI)GLM-4.5
Our Take
We recommend Grok 4.20 for complex complex codebases, multi-file repositories, and architectural planning, or the 3.0x cheaper GLM-4.5 if your budget requires optimizing costs for very high-volume pipelines. While Grok 4.20 offers a clear reasoning advantage, it carries a moderate price premium. Choose Grok 4.20 for architectural codebase planning, or GLM-4.5 to save on API costs for simple scripts.
▶WHY?
Benchmark Calculations & Evidence:
Coding Evaluation: Grok 4.20 was evaluated on SWE-bench Pro (scoring 51.8%), while GLM-4.5 was evaluated on SWE-bench Verified (scoring 64.2%).
Reasoning Accuracy: Both models were evaluated on the GPQA Diamond benchmark. Grok 4.20 scored 90%, while GLM-4.5 scored 79.9%.
Cost Efficiency: GLM-4.5 pricing ($0.6/M input, $2.2/M output) is 3.0x cheaper than Grok 4.20 ($2/M input, $6/M output).
Was this recommendation helpful?
Benchmarks & Scores
Coding (swe-bench-pro)
51.8%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)Winner (+10.1%)
90%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
$3.00Input: $2.00 | Output: $6.00Context WindowLarger
1.05M tokensBenchmarks & Scores
Coding (swe-bench-verified)
64.2%multi-file code and clearly defined tasks
Reasoning (gpqa-diamond)
79.9%graduate-level science QA
Cost & Context
Cost (per 1M tokens)3.0x cheaper
$1.00Input: $0.60 | Output: $2.20Context Window
131.07k tokensFrequently Asked Questions about Grok 4.20 vs GLM-4.5
GLM-4.5 is cheaper than Grok 4.20. GLM-4.5 has a blended cost of $1.00/1M tokens, which is about 3.0x cheaper than Grok 4.20 at $3.00/1M tokens.
For coding tasks, Grok 4.20 scores 51.8% 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 Grok 4.20 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.