Which model is better: Claude 3.7 Sonnet or GLM-5.3?
GLM-5.3 is 88.3% cheaper for input tokens ($0.35 vs. $3.00 per 1M tokens) and $1.00 vs. $15.00 for output tokens (8.6x cost advantage). In terms of operational performance, GLM-5.3 delivers lower response latency at 160ms TTFT (380ms faster than Claude 3.7 Sonnet) with 120 vs. 72 tok/s throughput. Claude 3.7 Sonnet leads coding evaluations at 70.3% SWE-bench Verified vs. 56.0% for GLM-5.3 and 78.5% 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 3.7 Sonnet
GLM-5.3
Net Delta / Advantage
Input Cost / 1M Tokens
$3.00
$0.35
GLM-5.3 is 8.6x cheaper
Output Cost / 1M Tokens
$15.00
$1.00
GLM-5.3 (93.3% lower)
Prompt Cache Read / 1M
$0.300
$0.090
GLM-5.3 is 3.3x cheaper
Prompt Cache Write / 1M
$3.75
$0.35
GLM-5.3 is 10.7x cheaper
Context Window (Max Output)
205k (64k out)
131k (16k out)
Claude 3.7 Sonnet (1.6x larger context)
Avg TTFT Response Latency
540ms
160ms
GLM-5.3 (380ms faster)
Generation Speed (Throughput)
72 tok/s
120 tok/s
GLM-5.3 (+48 tok/s faster)
SWE-bench Verified (Coding)
70.3%
56%
Claude 3.7 Sonnet (+14.3% lead)
GPQA Diamond (PhD STEM)
78.5%
67%
Claude 3.7 Sonnet (+11.5% lead)
MMLU-Pro (Multi-Discipline)
88%
80.5%
Claude 3.7 Sonnet (+7.5% lead)
MATH-500 (Quantitative Logic)
96.2%
88%
Claude 3.7 Sonnet (+8.2% lead)
Inference Value Score (IVR)
54.3 / 100
69.9 / 100
GLM-5.3 (+15.6 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 $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?
GLM-5.3 delivers faster first-token latency at 160ms TTFT (120 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 compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, Claude 3.7 Sonnet scores 70.3% versus 56% for GLM-5.3. On GPQA Diamond graduate-level STEM reasoning, Claude 3.7 Sonnet scores 78.5% versus 67% for GLM-5.3 (and 96.2% vs 88% 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?
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).
