Which model is better: Claude 4 Opus or GLM-5.3-Flash?
GLM-5.3-Flash is 99.2% cheaper for input tokens ($0.12 vs. $15.00 per 1M tokens) and $0.40 vs. $75.00 for output tokens (125.0x cost advantage). In terms of operational performance, GLM-5.3-Flash delivers lower response latency at 90ms TTFT (830ms faster than Claude 4 Opus) with 175 vs. 46 tok/s throughput. Claude 4 Opus leads coding evaluations at 77.2% SWE-bench Verified vs. 52.8% for GLM-5.3-Flash and 87.1% 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
Claude 4 Opus
GLM-5.3-Flash
Net Delta / Advantage
Input Cost / 1M Tokens
$15.00
$0.12
GLM-5.3-Flash is 125.0x cheaper
Output Cost / 1M Tokens
$75.00
$0.40
GLM-5.3-Flash (99.5% lower)
Prompt Cache Read / 1M
$1.500
$0.030
GLM-5.3-Flash is 50.0x cheaper
Prompt Cache Write / 1M
$18.75
$0.12
GLM-5.3-Flash is 156.3x cheaper
Context Window (Max Output)
205k (64k out)
262k (16k out)
GLM-5.3-Flash (1.3x larger context)
Avg TTFT Response Latency
920ms
90ms
GLM-5.3-Flash (830ms faster)
Generation Speed (Throughput)
46 tok/s
175 tok/s
GLM-5.3-Flash (+129 tok/s faster)
SWE-bench Verified (Coding)
77.2%
52.8%
Claude 4 Opus (+24.4% lead)
GPQA Diamond (PhD STEM)
87.1%
63.5%
Claude 4 Opus (+23.6% lead)
MMLU-Pro (Multi-Discipline)
91.8%
78.4%
Claude 4 Opus (+13.4% lead)
MATH-500 (Quantitative Logic)
98.1%
84%
Claude 4 Opus (+14.1% lead)
Inference Value Score (IVR)
43.6 / 100
76.7 / 100
GLM-5.3-Flash (+33.1 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-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 $15.00/1M input ($75.00/1M output) for Claude 4 Opus.
Which model has lower TTFT latency and higher tok/s throughput: Claude 4 Opus or GLM-5.3-Flash?
GLM-5.3-Flash delivers faster first-token latency at 90ms TTFT (175 tok/s throughput) compared to 920ms TTFT (46 tok/s) for Claude 4 Opus.
How do Claude 4 Opus and GLM-5.3-Flash compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, Claude 4 Opus scores 77.2% versus 52.8% for GLM-5.3-Flash. On GPQA Diamond graduate-level STEM reasoning, Claude 4 Opus scores 87.1% versus 63.5% for GLM-5.3-Flash (and 98.1% vs 84% on MATH-500).
When should you choose Claude 4 Opus?
Choose Claude 4 Opus (Anthropic) for mission-critical R&D, long-horizon autonomous coding tasks, complex theorem proving, and executive strategic synthesis — offering a 204k context window, 920ms TTFT, and $15.00/1M input pricing ($1.500/1M 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).
