Which model is better: DeepSeek V4 Pro or GLM-5.3-Flash?
GLM-5.3-Flash is 90.9% cheaper for input tokens ($0.12 vs. $1.32 per 1M tokens) and $0.40 vs. $3.96 for output tokens (11.0x cost advantage). In terms of operational performance, GLM-5.3-Flash delivers lower response latency at 90ms TTFT (430ms faster than DeepSeek V4 Pro) with 175 vs. 78 tok/s throughput. DeepSeek V4 Pro leads coding evaluations at 80.6% SWE-bench Verified vs. 52.8% for GLM-5.3-Flash and 90.1% 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
DeepSeek V4 Pro
GLM-5.3-Flash
Net Delta / Advantage
Input Cost / 1M Tokens
$1.32
$0.12
GLM-5.3-Flash is 11.0x cheaper
Output Cost / 1M Tokens
$3.96
$0.40
GLM-5.3-Flash (89.9% lower)
Prompt Cache Read / 1M
$0.145
$0.030
GLM-5.3-Flash is 4.8x cheaper
Prompt Cache Write / 1M
$1.32
$0.12
GLM-5.3-Flash is 11.0x cheaper
Context Window (Max Output)
1,024k (128k out)
262k (16k out)
DeepSeek V4 Pro (3.9x larger context)
Avg TTFT Response Latency
520ms
90ms
GLM-5.3-Flash (430ms faster)
Generation Speed (Throughput)
78 tok/s
175 tok/s
GLM-5.3-Flash (+97 tok/s faster)
SWE-bench Verified (Coding)
80.6%
52.8%
DeepSeek V4 Pro (+27.8% lead)
GPQA Diamond (PhD STEM)
90.1%
63.5%
DeepSeek V4 Pro (+26.6% lead)
MMLU-Pro (Multi-Discipline)
88.4%
78.4%
DeepSeek V4 Pro (+10.0% lead)
MATH-500 (Quantitative Logic)
97.3%
84%
DeepSeek V4 Pro (+13.3% lead)
Inference Value Score (IVR)
67 / 100
76.7 / 100
GLM-5.3-Flash (+9.7 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: DeepSeek V4 Pro or GLM-5.3-Flash?
GLM-5.3-Flash is cheaper at $0.12/1M input tokens ($0.40/1M output) compared to $1.32/1M input ($3.96/1M output) for DeepSeek V4 Pro.
Which model has lower TTFT latency and higher tok/s throughput: DeepSeek V4 Pro or GLM-5.3-Flash?
GLM-5.3-Flash delivers faster first-token latency at 90ms TTFT (175 tok/s throughput) compared to 520ms TTFT (78 tok/s) for DeepSeek V4 Pro.
How do DeepSeek V4 Pro and GLM-5.3-Flash compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, DeepSeek V4 Pro scores 80.6% versus 52.8% for GLM-5.3-Flash. On GPQA Diamond graduate-level STEM reasoning, DeepSeek V4 Pro scores 90.1% versus 63.5% for GLM-5.3-Flash (and 97.3% vs 84% on MATH-500).
When should you choose DeepSeek V4 Pro?
Choose DeepSeek V4 Pro (DeepSeek) for open-weight 1.6T MoE powerhouse designed for cost-effective full-repository code generation, mathematical reasoning, and persistent chain-of-thought multi-step agent workflows — offering a 1,024k context window, 520ms TTFT, and $1.32/1M input pricing ($0.145/1M 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).
