Which model is better: Claude 3.7 Sonnet or GLM-5.3-Flash?
GLM-5.3-Flash is 96.0% cheaper for input tokens ($0.12 vs. $3.00 per 1M tokens) and $0.40 vs. $15.00 for output tokens (25.0x cost advantage). In terms of operational performance, GLM-5.3-Flash delivers lower response latency at 90ms TTFT (450ms faster than Claude 3.7 Sonnet) with 175 vs. 72 tok/s throughput. Claude 3.7 Sonnet leads coding evaluations at 70.3% SWE-bench Verified vs. 52.8% for GLM-5.3-Flash and 78.5% 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 3.7 Sonnet
GLM-5.3-Flash
Net Delta / Advantage
Input Cost / 1M Tokens
$3.00
$0.12
GLM-5.3-Flash is 25.0x cheaper
Output Cost / 1M Tokens
$15.00
$0.40
GLM-5.3-Flash (97.3% lower)
Prompt Cache Read / 1M
$0.300
$0.030
GLM-5.3-Flash is 10.0x cheaper
Prompt Cache Write / 1M
$3.75
$0.12
GLM-5.3-Flash is 31.3x cheaper
Context Window (Max Output)
205k (64k out)
262k (16k out)
GLM-5.3-Flash (1.3x larger context)
Avg TTFT Response Latency
540ms
90ms
GLM-5.3-Flash (450ms faster)
Generation Speed (Throughput)
72 tok/s
175 tok/s
GLM-5.3-Flash (+103 tok/s faster)
SWE-bench Verified (Coding)
70.3%
52.8%
Claude 3.7 Sonnet (+17.5% lead)
GPQA Diamond (PhD STEM)
78.5%
63.5%
Claude 3.7 Sonnet (+15.0% lead)
MMLU-Pro (Multi-Discipline)
88%
78.4%
Claude 3.7 Sonnet (+9.6% lead)
MATH-500 (Quantitative Logic)
96.2%
84%
Claude 3.7 Sonnet (+12.2% lead)
Inference Value Score (IVR)
54.3 / 100
76.7 / 100
GLM-5.3-Flash (+22.4 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).
Which is cheaper: Claude 3.7 Sonnet or GLM-5.3-Flash?
GLM-5.3-Flash is cheaper at $0.12/1M input tokens ($0.40/1M output) compared to $3.00/1M input ($15.00/1M output) for Claude 3.7 Sonnet.
Which model has lower TTFT latency and higher tok/s throughput: Claude 3.7 Sonnet or GLM-5.3-Flash?
GLM-5.3-Flash delivers faster first-token latency at 90ms TTFT (175 tok/s throughput) compared to 540ms TTFT (72 tok/s) for Claude 3.7 Sonnet.
How do Claude 3.7 Sonnet and GLM-5.3-Flash compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, Claude 3.7 Sonnet scores 70.3% versus 52.8% for GLM-5.3-Flash. On GPQA Diamond graduate-level STEM reasoning, Claude 3.7 Sonnet scores 78.5% versus 63.5% for GLM-5.3-Flash (and 96.2% vs 84% on MATH-500).
When should you choose Claude 3.7 Sonnet?
Choose Claude 3.7 Sonnet (Anthropic) for full-stack repository development, complex code refactoring, and agentic tool use with predictable prompt cache economics — offering a 204k context window, 540ms TTFT, and $3.00/1M input pricing ($0.300/1M 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).
