GLM-5.3 is 36.4% cheaper for input tokens ($0.35 vs. $0.55 per 1M tokens) and $1.00 vs. $2.19 for output tokens (1.6x cost advantage). In terms of operational performance, GLM-5.3 delivers lower response latency at 160ms TTFT (1290ms faster than DeepSeek R1) with 120 vs. 58 tok/s throughput. GLM-5.3 leads coding evaluations at 56.0% SWE-bench Verified vs. 49.2% for DeepSeek R1 and 67.0% vs. 71.5% 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
DeepSeek R1
GLM-5.3
Net Delta / Advantage
Input Cost / 1M Tokens
$0.55
$0.35
GLM-5.3 is 1.6x cheaper
Output Cost / 1M Tokens
$2.19
$1.00
GLM-5.3 (54.3% lower)
Prompt Cache Read / 1M
$0.140
$0.090
GLM-5.3 is 1.6x cheaper
Prompt Cache Write / 1M
$0.55
$0.35
GLM-5.3 is 1.6x cheaper
Context Window (Max Output)
131k (33k out)
131k (16k out)
Equal Context Window
Avg TTFT Response Latency
1450ms
160ms
GLM-5.3 (1290ms faster)
Generation Speed (Throughput)
58 tok/s
120 tok/s
GLM-5.3 (+62 tok/s faster)
SWE-bench Verified (Coding)
49.2%
56%
GLM-5.3 (+6.8% lead)
GPQA Diamond (PhD STEM)
71.5%
67%
DeepSeek R1 (+4.5% lead)
MMLU-Pro (Multi-Discipline)
84%
80.5%
DeepSeek R1 (+3.5% lead)
MATH-500 (Quantitative Logic)
97.3%
88%
DeepSeek R1 (+9.3% lead)
Inference Value Score (IVR)
64.8 / 100
69.9 / 100
GLM-5.3 (+5.1 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 (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 $0.55/1M input ($2.19/1M output) for DeepSeek R1.
Which model has lower TTFT latency and higher tok/s throughput: DeepSeek R1 or GLM-5.3?
GLM-5.3 delivers faster first-token latency at 160ms TTFT (120 tok/s throughput) compared to 1450ms TTFT (58 tok/s) for DeepSeek R1.
How do DeepSeek R1 and GLM-5.3 compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, DeepSeek R1 scores 49.2% versus 56% for GLM-5.3. On GPQA Diamond graduate-level STEM reasoning, DeepSeek R1 scores 71.5% versus 67% for GLM-5.3 (and 97.3% vs 88% on MATH-500).
When should you choose DeepSeek R1?
Choose DeepSeek R1 (DeepSeek) for open-weight chain-of-thought reasoning, synthetic data distillation, and complex mathematical verification — offering a 131k context window, 1450ms TTFT, and $0.55/1M input pricing ($0.140/1M 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).
