GLM-5.3 is 97.7% cheaper for input tokens ($0.35 vs. $15.00 per 1M tokens) and $1.00 vs. $75.00 for output tokens (42.9x cost advantage). In terms of operational performance, GLM-5.3 delivers lower response latency at 160ms TTFT (760ms faster than Claude 4 Opus) with 120 vs. 46 tok/s throughput. Claude 4 Opus leads coding evaluations at 77.2% SWE-bench Verified vs. 56.0% for GLM-5.3 and 87.1% 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
Claude 4 Opus
GLM-5.3
Net Delta / Advantage
Input Cost / 1M Tokens
$15.00
$0.35
GLM-5.3 is 42.9x cheaper
Output Cost / 1M Tokens
$75.00
$1.00
GLM-5.3 (98.7% lower)
Prompt Cache Read / 1M
$1.500
$0.090
GLM-5.3 is 16.7x cheaper
Prompt Cache Write / 1M
$18.75
$0.35
GLM-5.3 is 53.6x cheaper
Context Window (Max Output)
205k (64k out)
131k (16k out)
Claude 4 Opus (1.6x larger context)
Avg TTFT Response Latency
920ms
160ms
GLM-5.3 (760ms faster)
Generation Speed (Throughput)
46 tok/s
120 tok/s
GLM-5.3 (+74 tok/s faster)
SWE-bench Verified (Coding)
77.2%
56%
Claude 4 Opus (+21.2% lead)
GPQA Diamond (PhD STEM)
87.1%
67%
Claude 4 Opus (+20.1% lead)
MMLU-Pro (Multi-Discipline)
91.8%
80.5%
Claude 4 Opus (+11.3% lead)
MATH-500 (Quantitative Logic)
98.1%
88%
Claude 4 Opus (+10.1% lead)
Inference Value Score (IVR)
43.6 / 100
69.9 / 100
GLM-5.3 (+26.3 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 $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?
GLM-5.3 delivers faster first-token latency at 160ms TTFT (120 tok/s throughput) compared to 920ms TTFT (46 tok/s) for Claude 4 Opus.
How do Claude 4 Opus and GLM-5.3 compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, Claude 4 Opus scores 77.2% versus 56% for GLM-5.3. On GPQA Diamond graduate-level STEM reasoning, Claude 4 Opus scores 87.1% versus 67% for GLM-5.3 (and 98.1% vs 88% 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?
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).
