GLM-5.3-Flash is 65.7% cheaper for input tokens ($0.12 vs. $0.35 per 1M tokens) and $0.40 vs. $1.00 for output tokens (2.9x cost advantage). In terms of operational performance, GLM-5.3-Flash delivers lower response latency at 90ms TTFT (70ms faster than GLM-5.3) with 175 vs. 120 tok/s throughput. GLM-5.3 leads coding evaluations at 56.0% SWE-bench Verified vs. 52.8% for GLM-5.3-Flash and 67.0% vs. 63.5% 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
GLM-5.3-Flash
Net Delta / Advantage
Input Cost / 1M Tokens
$0.35
$0.12
GLM-5.3-Flash is 2.9x cheaper
Output Cost / 1M Tokens
$1.00
$0.40
GLM-5.3-Flash (60.0% lower)
Prompt Cache Read / 1M
$0.090
$0.030
GLM-5.3-Flash is 3.0x cheaper
Prompt Cache Write / 1M
$0.35
$0.12
GLM-5.3-Flash is 2.9x cheaper
Context Window (Max Output)
131k (16k out)
262k (16k out)
GLM-5.3-Flash (2.0x larger context)
Avg TTFT Response Latency
160ms
90ms
GLM-5.3-Flash (70ms faster)
Generation Speed (Throughput)
120 tok/s
175 tok/s
GLM-5.3-Flash (+55 tok/s faster)
SWE-bench Verified (Coding)
56%
52.8%
GLM-5.3 (+3.2% lead)
GPQA Diamond (PhD STEM)
67%
63.5%
GLM-5.3 (+3.5% lead)
MMLU-Pro (Multi-Discipline)
80.5%
78.4%
GLM-5.3 (+2.1% lead)
MATH-500 (Quantitative Logic)
88%
84%
GLM-5.3 (+4.0% lead)
Inference Value Score (IVR)
69.9 / 100
76.7 / 100
GLM-5.3-Flash (+6.8 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 GLM-5.3-Flash (Zhipu AI) for sub-100ms ultra-fast inference model designed for real-time customer support, document classification, and agentic workflows — offering a 262k context window, 90ms TTFT, and $0.12/1M input pricing ($0.030/1M cached reads).
GLM-5.3-Flash is cheaper at $0.12/1M input tokens ($0.40/1M output) compared to $0.35/1M input ($1.00/1M output) for GLM-5.3.
Which model has lower TTFT latency and higher tok/s throughput: GLM-5.3 or GLM-5.3-Flash?
GLM-5.3-Flash delivers faster first-token latency at 90ms TTFT (175 tok/s throughput) compared to 160ms TTFT (120 tok/s) for GLM-5.3.
How do GLM-5.3 and GLM-5.3-Flash compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, GLM-5.3 scores 56% versus 52.8% for GLM-5.3-Flash. On GPQA Diamond graduate-level STEM reasoning, GLM-5.3 scores 67% versus 63.5% for GLM-5.3-Flash (and 88% vs 84% 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 GLM-5.3-Flash?
Choose GLM-5.3-Flash (Zhipu AI) for sub-100ms ultra-fast inference model designed for real-time customer support, document classification, and agentic workflows — offering a 262k context window, 90ms TTFT, and $0.12/1M input pricing ($0.030/1M cached reads).
