GLM-5.3 is 88.3% cheaper for input tokens ($0.35 vs. $3.00 per 1M tokens) and $1.00 vs. $15.00 for output tokens (8.6x cost advantage). In terms of operational performance, GLM-5.3 delivers lower response latency at 160ms TTFT (460ms faster than Grok 3) with 120 vs. 88 tok/s throughput. Grok 3 leads coding evaluations at 65.8% SWE-bench Verified vs. 56.0% for GLM-5.3 and 84.6% 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 3
Net Delta / Advantage
Input Cost / 1M Tokens
$0.35
$3.00
GLM-5.3 is 8.6x cheaper
Output Cost / 1M Tokens
$1.00
$15.00
GLM-5.3 (93.3% lower)
Prompt Cache Read / 1M
$0.090
$0.750
GLM-5.3 is 8.3x cheaper
Prompt Cache Write / 1M
$0.35
$3.00
GLM-5.3 is 8.6x cheaper
Context Window (Max Output)
131k (16k out)
134k (16k out)
Grok 3 (1.0x larger context)
Avg TTFT Response Latency
160ms
620ms
GLM-5.3 (460ms faster)
Generation Speed (Throughput)
120 tok/s
88 tok/s
GLM-5.3 (+32 tok/s faster)
SWE-bench Verified (Coding)
56%
65.8%
Grok 3 (+9.8% lead)
GPQA Diamond (PhD STEM)
67%
84.6%
Grok 3 (+17.6% lead)
MMLU-Pro (Multi-Discipline)
80.5%
80%
GLM-5.3 (+0.5% lead)
MATH-500 (Quantitative Logic)
88%
89.8%
Grok 3 (+1.8% lead)
Inference Value Score (IVR)
69.9 / 100
52.7 / 100
GLM-5.3 (+17.2 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).
Choose Grok 3 (xAI) for massive compute foundation model specialized in PhD-level science reasoning, real-time data analysis, and enterprise logic — offering a 134k context window, 620ms TTFT, and $3.00/1M input pricing ($0.750/1M cached reads).
GLM-5.3 is cheaper at $0.35/1M input tokens ($1.00/1M output) compared to $3.00/1M input ($15.00/1M output) for Grok 3.
Which model has lower TTFT latency and higher tok/s throughput: GLM-5.3 or Grok 3?
GLM-5.3 delivers faster first-token latency at 160ms TTFT (120 tok/s throughput) compared to 620ms TTFT (88 tok/s) for Grok 3.
How do GLM-5.3 and Grok 3 compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, GLM-5.3 scores 56% versus 65.8% for Grok 3. On GPQA Diamond graduate-level STEM reasoning, GLM-5.3 scores 67% versus 84.6% for Grok 3 (and 88% vs 89.8% 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 3?
Choose Grok 3 (xAI) for massive compute foundation model specialized in PhD-level science reasoning, real-time data analysis, and enterprise logic — offering a 134k context window, 620ms TTFT, and $3.00/1M input pricing ($0.750/1M cached reads).
