Which model is better: DeepSeek R1 or GLM-5.3-Flash?
GLM-5.3-Flash is 78.2% cheaper for input tokens ($0.12 vs. $0.55 per 1M tokens) and $0.40 vs. $2.19 for output tokens (4.6x cost advantage). In terms of operational performance, GLM-5.3-Flash delivers lower response latency at 90ms TTFT (1360ms faster than DeepSeek R1) with 175 vs. 58 tok/s throughput. GLM-5.3-Flash leads coding evaluations at 52.8% SWE-bench Verified vs. 49.2% for DeepSeek R1 and 63.5% 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-Flash
Net Delta / Advantage
Input Cost / 1M Tokens
$0.55
$0.12
GLM-5.3-Flash is 4.6x cheaper
Output Cost / 1M Tokens
$2.19
$0.40
GLM-5.3-Flash (81.7% lower)
Prompt Cache Read / 1M
$0.140
$0.030
GLM-5.3-Flash is 4.7x cheaper
Prompt Cache Write / 1M
$0.55
$0.12
GLM-5.3-Flash is 4.6x cheaper
Context Window (Max Output)
131k (33k out)
262k (16k out)
GLM-5.3-Flash (2.0x larger context)
Avg TTFT Response Latency
1450ms
90ms
GLM-5.3-Flash (1360ms faster)
Generation Speed (Throughput)
58 tok/s
175 tok/s
GLM-5.3-Flash (+117 tok/s faster)
SWE-bench Verified (Coding)
49.2%
52.8%
GLM-5.3-Flash (+3.6% lead)
GPQA Diamond (PhD STEM)
71.5%
63.5%
DeepSeek R1 (+8.0% lead)
MMLU-Pro (Multi-Discipline)
84%
78.4%
DeepSeek R1 (+5.6% lead)
MATH-500 (Quantitative Logic)
97.3%
84%
DeepSeek R1 (+13.3% lead)
Inference Value Score (IVR)
64.8 / 100
76.7 / 100
GLM-5.3-Flash (+11.9 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).
GLM-5.3-Flash is cheaper at $0.12/1M input tokens ($0.40/1M 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-Flash?
GLM-5.3-Flash delivers faster first-token latency at 90ms TTFT (175 tok/s throughput) compared to 1450ms TTFT (58 tok/s) for DeepSeek R1.
How do DeepSeek R1 and GLM-5.3-Flash compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, DeepSeek R1 scores 49.2% versus 52.8% for GLM-5.3-Flash. On GPQA Diamond graduate-level STEM reasoning, DeepSeek R1 scores 71.5% versus 63.5% for GLM-5.3-Flash (and 97.3% vs 84% 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-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).
