Which model is better: Claude Opus 5.5 or GLM-5.3?
GLM-5.3 is 91.2% cheaper for input tokens ($0.35 vs. $4.00 per 1M tokens) and $1.00 vs. $20.00 for output tokens (11.4x cost advantage). In terms of operational performance, GLM-5.3 delivers lower response latency at 160ms TTFT (520ms faster than Claude Opus 5.5) with 120 vs. 95 tok/s throughput. Claude Opus 5.5 leads coding evaluations at 89.9% SWE-bench Verified vs. 56.0% for GLM-5.3 and 93.2% 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
Claude Opus 5.5
GLM-5.3
Net Delta / Advantage
Input Cost / 1M Tokens
$4.00
$0.35
GLM-5.3 is 11.4x cheaper
Output Cost / 1M Tokens
$20.00
$1.00
GLM-5.3 (95.0% lower)
Prompt Cache Read / 1M
$0.200
$0.090
GLM-5.3 is 2.2x cheaper
Prompt Cache Write / 1M
$5.00
$0.35
GLM-5.3 is 14.3x cheaper
Context Window (Max Output)
1,024k (128k out)
131k (16k out)
Claude Opus 5.5 (7.8x larger context)
Avg TTFT Response Latency
680ms
160ms
GLM-5.3 (520ms faster)
Generation Speed (Throughput)
95 tok/s
120 tok/s
GLM-5.3 (+25 tok/s faster)
SWE-bench Verified (Coding)
89.9%
56%
Claude Opus 5.5 (+33.9% lead)
GPQA Diamond (PhD STEM)
93.2%
67%
Claude Opus 5.5 (+26.2% lead)
MMLU-Pro (Multi-Discipline)
92.1%
80.5%
Claude Opus 5.5 (+11.6% lead)
MATH-500 (Quantitative Logic)
98%
88%
Claude Opus 5.5 (+10.0% lead)
Inference Value Score (IVR)
57.8 / 100
69.9 / 100
GLM-5.3 (+12.1 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Choose Claude Opus 5.5 (Anthropic) for top-ranked frontier intelligence leader for autonomous multi-step agentic coding, repository-scale migrations, and complex scientific reasoning at a 40% lower workload cost than Opus 5 — offering a 1,024k context window, 680ms TTFT, and $4.00/1M input pricing ($0.200/1M cached reads).
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 $4.00/1M input ($20.00/1M output) for Claude Opus 5.5.
Which model has lower TTFT latency and higher tok/s throughput: Claude Opus 5.5 or GLM-5.3?
GLM-5.3 delivers faster first-token latency at 160ms TTFT (120 tok/s throughput) compared to 680ms TTFT (95 tok/s) for Claude Opus 5.5.
How do Claude Opus 5.5 and GLM-5.3 compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, Claude Opus 5.5 scores 89.9% versus 56% for GLM-5.3. On GPQA Diamond graduate-level STEM reasoning, Claude Opus 5.5 scores 93.2% versus 67% for GLM-5.3 (and 98% vs 88% on MATH-500).
When should you choose Claude Opus 5.5?
Choose Claude Opus 5.5 (Anthropic) for top-ranked frontier intelligence leader for autonomous multi-step agentic coding, repository-scale migrations, and complex scientific reasoning at a 40% lower workload cost than Opus 5 — offering a 1,024k context window, 680ms TTFT, and $4.00/1M input pricing ($0.200/1M 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).
