Which model is better: DeepSeek V4 Pro or GLM-5.3?
GLM-5.3 is 73.5% cheaper for input tokens ($0.35 vs. $1.32 per 1M tokens) and $1.00 vs. $3.96 for output tokens (3.8x cost advantage). In terms of operational performance, GLM-5.3 delivers lower response latency at 160ms TTFT (360ms faster than DeepSeek V4 Pro) with 120 vs. 78 tok/s throughput. DeepSeek V4 Pro leads coding evaluations at 80.6% SWE-bench Verified vs. 56.0% for GLM-5.3 and 90.1% 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
DeepSeek V4 Pro
GLM-5.3
Net Delta / Advantage
Input Cost / 1M Tokens
$1.32
$0.35
GLM-5.3 is 3.8x cheaper
Output Cost / 1M Tokens
$3.96
$1.00
GLM-5.3 (74.7% lower)
Prompt Cache Read / 1M
$0.145
$0.090
GLM-5.3 is 1.6x cheaper
Prompt Cache Write / 1M
$1.32
$0.35
GLM-5.3 is 3.8x cheaper
Context Window (Max Output)
1,024k (128k out)
131k (16k out)
DeepSeek V4 Pro (7.8x larger context)
Avg TTFT Response Latency
520ms
160ms
GLM-5.3 (360ms faster)
Generation Speed (Throughput)
78 tok/s
120 tok/s
GLM-5.3 (+42 tok/s faster)
SWE-bench Verified (Coding)
80.6%
56%
DeepSeek V4 Pro (+24.6% lead)
GPQA Diamond (PhD STEM)
90.1%
67%
DeepSeek V4 Pro (+23.1% lead)
MMLU-Pro (Multi-Discipline)
88.4%
80.5%
DeepSeek V4 Pro (+7.9% lead)
MATH-500 (Quantitative Logic)
97.3%
88%
DeepSeek V4 Pro (+9.3% lead)
Inference Value Score (IVR)
67 / 100
69.9 / 100
GLM-5.3 (+2.9 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 (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 $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?
GLM-5.3 delivers faster first-token latency at 160ms TTFT (120 tok/s throughput) compared to 520ms TTFT (78 tok/s) for DeepSeek V4 Pro.
How do DeepSeek V4 Pro and GLM-5.3 compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, DeepSeek V4 Pro scores 80.6% versus 56% for GLM-5.3. On GPQA Diamond graduate-level STEM reasoning, DeepSeek V4 Pro scores 90.1% versus 67% for GLM-5.3 (and 97.3% vs 88% 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?
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).
