GLM-5.3 is 86.0% cheaper for input tokens ($0.35 vs. $2.50 per 1M tokens) and $1.00 vs. $12.50 for output tokens (7.1x cost advantage). In terms of operational performance, GLM-5.3 delivers lower response latency at 160ms TTFT (320ms faster than Grok 4) with 120 vs. 95 tok/s throughput. Grok 4 leads coding evaluations at 73.2% SWE-bench Verified vs. 56.0% for GLM-5.3 and 86.2% vs. 67.0% on GPQA Diamond.
Verified daily via automated API latency tests and official documentation.
Zero-Shot RAG Verified
Verified Performance & Unit Economics Delta (12 Dimensions)
Daily synchronized metrics across token pricing, prompt cache economics, latency, throughput, and 5 capability benchmarks.
Metric / Dimension
GLM-5.3
Grok 4
Net Delta / Advantage
Input Cost / 1M Tokens
$0.35
$2.50
GLM-5.3 is 7.1x cheaper
Output Cost / 1M Tokens
$1.00
$12.50
GLM-5.3 (92.0% lower)
Prompt Cache Read / 1M
$0.090
$0.500
GLM-5.3 is 5.6x cheaper
Prompt Cache Write / 1M
$0.35
$2.50
GLM-5.3 is 7.1x cheaper
Context Window (Max Output)
131k (16k out)
262k (66k out)
Grok 4 (2.0x larger context)
Avg TTFT Response Latency
160ms
480ms
GLM-5.3 (320ms faster)
Generation Speed (Throughput)
120 tok/s
95 tok/s
GLM-5.3 (+25 tok/s faster)
SWE-bench Verified (Coding)
56%
73.2%
Grok 4 (+17.2% lead)
GPQA Diamond (PhD STEM)
67%
86.2%
Grok 4 (+19.2% lead)
MMLU-Pro (Multi-Discipline)
80.5%
89.9%
Grok 4 (+9.4% lead)
MATH-500 (Quantitative Logic)
88%
97.6%
Grok 4 (+9.6% lead)
Inference Value Score (IVR)
69.9 / 100
57.6 / 100
GLM-5.3 (+12.3 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Choose GLM-5.3 (Zhipu AI) for bilingual flagship foundation model optimized for cross-lingual reasoning, long-form structured analysis, and enterprise tool execution — offering a 131k context window, 160ms TTFT, and $0.35/1M input pricing ($0.090/1M cached reads).
GLM-5.3 is cheaper at $0.35/1M input tokens ($1.00/1M output) compared to $2.50/1M input ($12.50/1M output) for Grok 4.
Which model has lower TTFT latency and higher tok/s throughput: GLM-5.3 or Grok 4?
GLM-5.3 delivers faster first-token latency at 160ms TTFT (120 tok/s throughput) compared to 480ms TTFT (95 tok/s) for Grok 4.
How do GLM-5.3 and Grok 4 compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, GLM-5.3 scores 56% versus 73.2% for Grok 4. On GPQA Diamond graduate-level STEM reasoning, GLM-5.3 scores 67% versus 86.2% for Grok 4 (and 88% vs 97.6% on MATH-500).
When should you choose GLM-5.3?
Choose GLM-5.3 (Zhipu AI) for bilingual flagship foundation model optimized for cross-lingual reasoning, long-form structured analysis, and enterprise tool execution — offering a 131k context window, 160ms TTFT, and $0.35/1M input pricing ($0.090/1M cached reads).
When should you choose Grok 4?
Choose Grok 4 (xAI) for real-time financial/market intelligence, PhD-level STEM problem solving, and autonomous code generation — offering a 262k context window, 480ms TTFT, and $2.50/1M input pricing ($0.500/1M cached reads).
